This is not about a 14-hour work day, or working for 70-hours a week. Reid Hoffman, the co-founder and executive chairman of LinkedIn, has made a startling prediction that the traditional 9 to 5 job will vanish by 2034. But unlike other bold claims that people keep making, this is not just another dystopian, or even utopian, proclamation. 

“You may not only work at different companies, you might work in different industries,” said Hoffman, adding that people may stop working like employees and begin working in a gig economy.

The ‘Gig Economy Revolution’, Hoffman believes, will be more significant than anticipated. According to his prediction, within the next decade, 50% of the population will become freelancers and earn more while working for “3 or 4 gigs”, than those working in traditional employment, 

Though this might create a lot of opportunities, it could create a lot of instability as well, which Hoffman said people would not necessarily like. “One of the challenges would be how do we minimise the uncertainty and increase the stability, while continuing to have the opportunities, productivity, and flexibility,” said Hoffman.

Notably, Hoffman’s previous predictions have been accurate. In 1997, he foresaw the emergence of social media and its transformative impact on the world, anticipated the sharing economy (as an early investor in Airbnb), and predicted the AI revolution before the advent of ChatGPT.

The Future is About Results, Not Hours

Not just this, Hoffman has also predicted the imminent decline of traditional office headquarters, noting that this shift is occurring more rapidly than anticipated. He expects office space costs to drop by 40% by 2034 as more companies move to a fully online presence without physical offices. 

According to him, the future of work will be both hyper-local and global at the same time. However, he emphasised that the disappearance of 9 to 5 jobs is not a threat but an opportunity to redefine success. He added that the future will favour those who are adaptable.

This is similar to what AIM said earlier about there being jobs for ten times the developers. Contrary to popular belief, the tech job market is booming with a high demand for specific skills. All developers have to do is move beyond being “generic software engineers.”

This is applicable for every field. With the help of AI, people will be able to do multiple jobs at once and even become overemployed. This isn’t uncommon as it is, as several employees already moonlight in addition to their regular day jobs. But in the future, it will constitute a major part of the economy.

Since everyone has the free time to engage in a certain amount of creative jobs at their workplaces, such as creation of art, music, and other such pursuits using AI tools, the question about how much time it takes to do some work would be eliminated. All people would focus on is the end result rather than the time it takes. 

More Businesses?

Moreover, another prediction is that by 2034, one in three professionals will operate multiple micro-businesses. The passion economy will give rise to unexpected millionaires. This could also possibly give rise to the first billion dollar business built by one person with the help of AI.

“Do you think it’s finally possible for a one-person billion dollar startup?” asked Ben Horowitz to Marc Andreessen. “You could put a whole bunch of things in this bucket…it’s the inherent scalability of software and the internet but you could also put AI,” Andreessen responded, adding that you could also do it using a lot of work getting outsourced.

Andreessen said that it is possible that super geniuses in the coming 20 years would be able to crack this using just AI and Copilots. Horowitz added, “the internet is actually probably in some ways a bigger breakthrough than AI,” while also saying that we might see a few companies like these crop up soon.

This conversation is similar to what Sam Altman said in an interview. “We’re going to see 10-person companies with billion-dollar valuations pretty soon…in my little group chat with my tech CEO friends, there’s this betting pool for the first year there is a one-person billion-dollar company, which would’ve been unimaginable without AI. And now [it] will happen.”

So it is likely people will stop working at companies and, instead, start creating their own businesses soon. Not working 9 to 5 jobs, but a lot more jobs beyond the regular 9 to 5.

The post 9-to-5 Jobs Will be Extinct by 2034 appeared first on AIM.

Meta CEO Mark Zuckerberg seems to firmly believe that “in the future, there will be far more creative jobs than there are today”. 

In a recent interview with Kallaway, Zuckerberg shared that as technology evolves, so do the AI tools we use, and staying updated with these is crucial for everyone in the creative space. He reiterated that in the future most forms of everyday work would require more imagination.

In an episode of the Lex Fridman Podcast, the Meta founder and CEO said that a lot more people will work on creative stuff in the future – it would then be considered similar to traditional labour or service.

However, Mira Murati, the chief technology officer of OpenAI, believes otherwise. 

During an interview at the AI Everywhere event at Dartmouth College, she said, “Some creative jobs may go away, but maybe they shouldn’t have been there in the first place.”

How to be Creative with AI?

Previously, AIM reported on a fascinating story about a 19-year-old artist selling AI-generated art in Bengaluru. The artist highlighted that his images were original, generated from scratch, and not copied from other creators or existing works.

Well, this isn’t the first example of artists embracing AI to be more creative. In dance, for instance, AI-generated visuals and music interact with live performers, as seen in Cloud Gate Dance Theatre’s production ‘Waves’.

Here, AI not only enhances the visual and auditory landscape but also integrates with dancers’ movements, using data collected from their physiological signals. 

Zuckerberg deeply believes that the future will consist of many AIs created by different people, each offering unique experiences. This belief in diversity and variety is also why he supports open-source development so strongly.

Meta’s AI Studio

Meta recently announced the testing of user-created AI chatbots on Instagram with a new tool called AI Studio. These AI characters, developed by various content creators, will soon appear in the US, labelled as AI to ensure user awareness. 

“We’re launching the first test phase with around 50 creators. We’ll gradually expand this to a small percentage of users, refining the experience along the way. 

“By the end of July or August, we anticipate a full rollout. It will be fascinating to see how people respond to interacting with these AI created by their favourite creators,” Zuckerberg said. 

He further mentioned that Meta’s goal is to build more tools that empower more people, including those who don’t see themselves as creators today. 

It’s like the Picasso quote: “All children are artists. The challenge is how to remain one when you grow up.”

AI for All

Meanwhile, authors Barry Lynn, Max von Thun, and Karina Montoya call out the dangers of monopolist AI development. They argue that tech giants like Google, Amazon, Microsoft, Meta, and Apple control the “upstream” infrastructure, which includes essential resources and technologies.

Reflecting this sentiment, Zuckerberg, too, states that AI technology should not be monopolised by a single company; instead, it should be accessible to everyone. 

“This involves providing tools for creators and users to develop their own AIs, similar to user-generated content, and open-sourcing the technology to allow innovation and experimentation,” he said. 

He emphasised the need to empower a diverse range of creators and small businesses to develop their own AIs, which will create a richer, more dynamic technological landscape.

The post AI will Create More Creative Jobs Than There are Today  appeared first on AIM.

It appears that concerns about being ghosted by your interviewer are a thing of the past, all thanks to “Alex.” 

But who exactly is Alex?

US-based startup Apriora AI has leveraged AI to streamline the hiring process. Their flagship product, Alex, represents a paradigm shift in recruitment methodology by seamlessly integrating advanced technology into the interview process.

Unlike traditional interviewers, Alex operates as a two-way AI interface, capable of conducting live video interviews with job candidates. This cutting-edge technology provides applicants with immediate feedback and a more transparent hiring experience.

One of the notable features of Alex is its unparalleled capacity to manage interviews. Unlike humans, Alex does not require breaks or downtime, enabling it to conduct interviews continuously without interruption. 

This perpetual availability ensures that companies can maintain a steady flow of interviews regardless of external factors such as weather conditions or staffing constraints. 

Moreover, Alex’s ability to review resumes and conduct interviews non-stop throughout the day significantly increases the likelihood of job seekers securing interviews. By facilitating a higher volume of interviews, Apriora AI aims to enhance opportunities for both employers and applicants in the competitive job market. 

Further, various platforms are now turning to AI models to streamline the hiring process. The platforms present recruiters and HR professionals with an array of AI tools to navigate this season of talent acquisition.

Some of the existing Indian generative AI platforms for recruiters and HR professionals include MachineHack for Enterprises, Oracle Recruiting, and Zoho Recruit.

The post Candidates Will Soon Be Interviewed by AI appeared first on AIM.

As we know, SQL (Structured Query Language) is a vital tool in a data scientist’s toolbox. Of course, mastering SQL is essential for interviews, but a good understanding of SQL by solving complex queries will help you a great deal in different phases of your career.

Here are five complex solutions for all the data enthusiasts out there! 

Note: For the following queries, extensive data tables will be available that need to be fed into an SQL editor. With their respective inputs, the output is generated.   

1. Query to fetch ‘EmpId’ and ‘FullName’ of the employees working under the Manager with id – ’03’.

Answer: For this, the EmployeeDetails table can be used to fetch the employee details with a where clause for the Manager.

(Input)

SELECT EmpId, FullName

FROM EmployeeDetails

WHERE ManagerId = 03;

Here’s an example.

2. Query to fetch the first record from a table

Answer: There are two ways to ‘fetch the first record’, 

(Input)

select * from Student where RowID = select min(RowID) from Student;

The second method is by printing just one (first) row of the table:

select * from Student where Rownum = 1;

Here’s an example. 

3. Show only common records between two tables.

Answer: Feed the following input in the editor:

(Input)

Select * from Student;

Intersect

Select * from StudentA;

Here’s an example. 

4. Query to find the record in Table A, not Table B, without using the NOT IN operator.

Consider two tables:

Answer: We can use the MINUS operator for Oracle and EXCEPT for SQL Server.

The query will be as follows:

(Input)

SELECT * FROM Table_A

MINUS

SELECT * FROM Table_B

Here’s an example. 

5. Differentiate between UNION and UNION ALL

Answer: The major difference between UNION and UNION ALL is:

UNION eliminates duplicate records. On the other handUNION, ALL does not.

For example, consider two tables:

UNION of A and B = 20, 30, 25

UNION ALL of A and B = 20, 25, 25, 30

The performance of UNION ALL is considered better than UNION since UNION ALL does not require additional work of removing duplicates.

Note: The above queries enhance your knowledge as an aspiring data science professional (from data architects to software engineers) and add to your existing skills. As a result, SQL is one of the most in-demand skills among all jobs in the domain, appearing in 42.7% of all data science job postings. 

The post 5 Complex SQL Queries to Nail Your Job Interview appeared first on AIM.

In recent years, India has become synonymous with tech innovation. To make the most of this burgeoning potential, global firms are ramping up their recruitment plans, aiming to hire 200,000 employees from the country by the end of 2022-23. If you are one of the lot and have an interview scheduled, peek into these questions below

C/C++

Define local block.

A local block is any part of a C program enclosed between the left brace ({) and the right brace (}). For example, a (switch) statement can also contain braces – that specific portion would be considered a local block.

Should variables be stored in local blocks?

Using local blocks for storing variables is unusual and should be avoided, with only rare exceptions. One of these exceptions is for debugging purposes when you want to declare a local example of a global variable to test within a function. On the other hand, you should also use a local block to make your program more readable in the current context. 

Sometimes having the variable declared closer to where it is used makes your program more readable. However, well-written programs usually do not have to resort to declaring variables in this manner, and you should refrain from using local blocks.

What are ivalue and rvalue?

ivalue: An expression that may appear as either the left-hand or right-hand side of an assignment.

rvalue: An expression that can be assigned to an ivalue. The rvalue appears on the right side of an assignment statement.

Data Structure 

What pointer type should be used while using C language to implement the heterogeneous linked list?

The heterogeneous linked list is made of several data types in its nodes, and a link pointer is needed to connect them. Using ordinary pointers, in this case, is not possible. Hence, void pointers capable of storing a pointer to any type as a generic pointer type are opted for.

What data structures are used to perform recursion? 

Stack. Due to its LIFO (Last In First Out) property, it remembers the ‘caller’ so it knows whom to return when the function has to return. Recursion uses a system stack to store the function calls’ return addresses.

Every recursive function has its equivalent non-recursive function. Even when such procedures are written, an explicit stack is to be used.

Areas where data structures are applied extensively.

• Compiler Design
• Operating System
• Database Management System
• Statistical Analysis Package
• Numerical Analysis
• Graphics
• Artificial Intelligence
• Simulation

Java 

Define transient variables. 

A transient variable is a special variable created by using the transient keyword. It may have a non-serialized value at the time of serialisation. It is initialised by default during deserialization. 

Why do threads block on I/O?

Threads block (or enter the waiting state) on I/O so other threads can execute while the operation is performed.

What is synchronisation, and why is it important?

Synchronisation is controlling the access of multiple threads to shared resources. Without synchronisation, one thread can modify a shared object while another uses or updates that object’s value. This often leads to significant errors.

SQL Server

Define Relational Database Management Systems.

Relational Database Management Systems (RDBMS) are database systems that maintain data in tables. Relationships may be created and maintained across as well as among the tables and data. 

In an RDBMS, relationships between data items are expressed through tables. Associations among these tables are expressed via data values instead of pointers allowing a high degree of data independence. In addition, a relational database can reunify the items from different files, providing dynamic data usage tools.

Define normalisation. 

Normalisation is a design and organisation process applied to data structures to minimise redundancy based on rules that help build relational databases. 

It usually divides a database into two or more tables and defines their relationships. The objective is to separate data so that a field’s additions, deletions, and modifications can be done in just one table and propagated through the rest of the defined relationships database.

What is Denormalization?

The process of attempting to optimise the database performance by adding redundant data is denormalization. At times it is necessary because current DBMSs poorly integrate relational models. 

A true RDBMS would allow for a complete normalised database at the logical level for high performance. Denormalization is a method to move from higher to lower normal forms of database modelling to speed up database access.

The post 12 Must-Know Questions for Your Next Tech Job Interview appeared first on AIM.

“Coming out from college, I didn’t know programming, I learned a little bit of SQL at my first job, but nothing much.”

For this week’s machine learning practitioner’s Analytics India Magazine(AIM) got in touch with Rahul Agarwal, an ML engineer at Facebook. Rahul has a bachelors in mechanical engineering from IIT Delhi and has previously held machine learning roles at Walmart Labs and Citibank.  In this interview, he gave a glimpse of the ground reality of making it to top ML roles.

AIM: How did your journey in machine learning begin?  

Rahul: I remember that distinctly. I was sort of lucky to stumble upon the Machine Learning course from Andrew Ng. Andrew Ng was talking about the regression problem and he was using the housing data to explain it. The moment he told about the prediction function. Just pass the variables and get the price. That moment was sublime. Is it magic? Coming from a background where I am good with maths, I couldn’t have thought of such a formula approximation myself and that hooked me. How maths can be used to solve these practical problems.

AIM:What were the initial challenges and how did you address them?

Rahul: Coming out from college, I didn’t know programming, I learned a little bit of SQL at my first job, but nothing much. I didn’t have a mentor or someone who could have guided me while starting with Data Science. The thing that helped me the most was making a plan once I realised that I like this particular stream. I made a plan of all the things I would have to learn: Went through a lot of course structures that were taught in various universities and tried to find something similar to those since I couldn’t leave my job. At my job, I tried to get headfirst into the data science projects whenever I could get my hands on them. It didn’t really matter if I would be able to do them or not. That was always something that I would consider later while understanding more about the problem myself or while googling about the problem. Even if I would not be able to solve something I still would learn a lot about Data Science while trying to solve it. And I have learned that it would rarely happen that you wouldn’t be able to suggest any solution to a particular problem if you do your research well.

AIM: What books and other resources have you used in your journey?

Rahul: Before I answer this question there is a disclaimer that there is no particular book or course which will teach you everything. You would have to take a lot of them and find out yourself what suits your learning needs and what suits how you learn. I loved the Machine learning Book from Tom Mitchell. I also liked the Probability and Statistics by Joe Blitzstein. If you are looking at the list of courses I took in my Data Science Journey I have neatly summarised them here.

AIM: How was your experience interviewing for an ML role at Facebook?

Rahul: It was August last year and I was in the process of giving interviews. By that point, I was already interviewing for Google India and Amazon India for Machine Learning and Data Science roles respectively. And then my senior advised me to apply for a role in Facebook London.

Later, as I studied for the FB coding interview, I realised that I took it a little light and that I was not prepared for the coding interviews at all. The first interview was telephonic. This was a very basic data structure interview and sort of a basic sanity check. This was followed by two onsite coding rounds and system design round. For more details check How I cracked my MLE interview at Facebook.

Recommendations by Rahul Agarwal

• Cracking the Coding Interview by Gayle Laakmann McDowell
• Algorithm Specialization on Coursera by UCSanDiego
• System Design Primer by Donne Martin
• Techdummies Youtube videos
• Facebook Field Guide to Machine Learning video series

Also Read: Rahul Agarwal’s MLWhiz blog.

AIM:What does a typical day look like for you as an ML engineer?

Rahul: You start with reading your mails and replying to the important ones. Then you start with your list of tasks for the day which would include some Data Exploration tasks or working on some feature engineering, or building a model or checking some hypothesis. In the meantime, I would have a lot of meetings with people regarding data issues or how would we evaluate our models. Sometimes the meetings are with business stakeholders to understand their pain points and what we could do to solve those issues.

AIM: How do you approach any machine learning problem?

Rahul: I try to tackle any machine learning problem by first looking at the data itself as I guess any Machine Learning engineer would do. Only once you look at the data you would be able to frame a rough structure of how you would like to solve a particular problem in your head. My basic principle has always been to come up with a very simple baseline solution in the beginning and continually improve on that. The motivation behind that is that you can actually try to optimise and tune your machine learning model forever. The first thing you should always strive for is finding the minimal value you can get by solving the problem as a Machine learning problem. Another point I would like to emphasise is to fail fast. Go into production as soon as possible. Only after testing your model in real life circumstances would you really know the effectiveness of the model, what it fails on and the things you want to focus on.

AIM: What does your machine learning toolkit look like?

Rahul Agarwal’s workstation

Rahul: It pretty much depends on the person. I like to use Python as my language of choice but I know many good people who like R and some who are doing Data Science using Javascript. So I would say it depends on the project. My advice would be to not bind yourself to any tool/frameworks as frameworks and tools will come and go. Try to understand the mathematical concepts and the logic behind a data science algorithm and you should be fine. In the end, learning a Tool and Framework would also become a daily part of your existence when I see how fast the ecosystem changes.

AIM: There is a lot of hype around machine learning. So, when the dust settles down,which domain might stand the test of time?

Rahul: Why do you think the dust will settle down? I don’t see Deep Learning going anywhere with its adoption being at near country levels. The recommendation systems are never going away from our lives whether it be product recommenders or ads. I see ML being more and more engineering heavy with every passing day. Yes, we still test hypotheses and we still love our research problems but a far higher weight is given to putting things in production and derive value fast. ML is going to play a key part in Maps which will still continue to improve. The Robot industry is set to have a boom in the next few years with reinforcement learning. The AR/VR industry is something that could really be the next big thing. I guess we have just started.

AIM: What do outsiders get wrong about this field?

Rahul: That it is easy to get into and reading a book or doing a course/bootcamp could teach you about AI/ML. Right now, you need a lot of math and programming background just to even start. And a lot of luck to get a job and show what you are capable of. That is not to say it is impossible, but be prepared to spend a lot of time learning before getting in and learning never stops in this field.

 AIM: From a global AI perspective, where do you think India stands?

Rahul: From my perspective, India would no doubt be a major beneficiary of this AI/ML wave. The first reason being that India has always been at the forefront of software. And AI/ML is no different. India needs to research more on AI/ML. We can see it happening with various Big companies opening up their AI labs in India but it needs to happen at the college level too where students get proper research infrastructure and good mentorship.

The post From Zero Coding Skills To Top ML Job At FAANG: Interview With Rahul Agarwal appeared first on AIM.

A Cloud Architect plays a pivotal role in developing the computing strategy in an organisation. The primary responsibility of a cloud architect is to provide expertise on cloud infrastructure to the development teams. Armed with a good understanding of cloud concepts, networking, cloud security and such, a Cloud Architect runs the cloud environments and offers expert guidance to the development teams in an organisation.

Here are the top eight interview questions for aspiring cloud architects.

1|Name some cloud providers. Compare any two popular Cloud Service Providers

Solution: Some of the best and fastest growing cloud service providers for B2B data analytics, AI and more-

• Amazon Web Services
• Google Cloud Platform
• Microsoft Azure
• IBM Cloud
• Snowflake
• Databricks
• DataRobot
• Sisense

Amazon Web Service (AWS) is the oldest player in the cloud computing services market. AWS brought in nearly $10 billion in quarterly sales, hitting a $40 billion annual run rate, four times the projection for Google. 

Know more here.

2| How can you speed up large data transfers on cloud?

Solution: One of the best methods for transferring big data files to the cloud is to use hybrid transfer protocol, also known as Accelerated File Transfer Protocol or AFTP. This protocol is a TCP/UDP hybrid with the capability to boost file transfer speeds up to 100%. Also, sometimes poor network conditions can limit you from exploring the potential of big data cloud computing. One of the best ways to handle this problem is by avoiding an internet file transfer altogether and shipping the portable storage devices containing data to the cloud service providers.

Know more here.

3| Discuss the strategy for application migration to the cloud

Solution: The complexity of migrating existing applications differs as it depends on the architecture and existing licensing arrangements. Some of the important cloud migration strategies include-

• Outlining the business objectives through the cloud.
• Getting the right professionals
• Conducting a comprehensive business as well as technical analysis of the current environment, apps, and infrastructure. 
• Deciding on cloud vendors
• Creating a cloud roadmap 
• Getting your application cloud-ready using migration models, such as Lift and Shift (Rehost) and Rearchitect (Refactor). 
• Creating a data migration plan
• Testing and switching to production 

Know more here.

4| What is the use/ importance of API Gateway?

Solution: An API gateway is an API management tool which lies between clients and a collection of backend services. API gateway acts as a reverse proxy in order to accept all the application programming interface calls, aggregate the different services needed to fulfill them, and return the appropriate result.

API gateways are the way to decouple the client interface from backend implementation. When a client makes a request, the API gateway breaks it into various requests and routes them to the right places, produces a response, as well as keeps track of everything.

Know more here.

5| Why do you use subnets?

Solution: A subnet, or subnetwork, is a segmented piece of a larger network. More specifically, subnets are a logical partition of an IP network into multiple, smaller network segments. Organisations use them to sub-divide larger networks into smaller, more efficient subnetworks. One key goal of a subnet is to split a large network into a grouping of smaller, interconnected networks to help minimise traffic. This way, traffic doesn’t have to flow through unnecessary routes, increasing network speeds.

Know more here.

6| Mention some best practices for Cloud Security.

Solution: From storing data to accessing productivity tools, cloud services are used for multiple purposes in corporate environments. Here are some of the best practices-

• Focus on understanding your current state and assessing risk
• Strategically apply protection to your cloud services as per the level of risk
• Adjust cloud access policies as new services emerge
• Remove malware from a cloud service.

Know more here.

7| What is a cloud migration strategy? How do you orchestrate one?

Solution: A cloud migration strategy is the plan an organisation makes to move its data and applications from an on-premises architecture to the cloud. There are various approaches, such as Rehosting (“lift and shift”), replatforming, repurchasing, refactoring, etc. Some of the basic cloud migration steps are as follows-

• Planning your migration
• Choosing your cloud environment
• Migrating your apps and data
• Validating post-move success

Know more here.

8| How do you connect on-premise applications to cloud infrastructure?

Solution: An effective way to utilise the cloud is to connect on premise systems to a cloud system by creating a hybrid cloud environment. One of the options for connecting on-premise environment and your cloud environment is a VPN. For instance, in case of AWS VPC VPN, it creates an encrypted private channel for transferring data between your on premise network and your Amazon VPC network. AWS Direct Connect is another method to connect an on premise system to the cloud. It operates by linking the internal network to an AWS Direct Connect location.

Know more here

Wrapping Up

If you are prepping for your cloud architect interview, the above-mentioned questions will surely help you to ace the test. Also, it is important to mention that based on your experience in the cloud computing environment, you may be asked a few questions such as-

• If something were to go wrong in your organisation’s cloud, how do you make it right?
• Explain how you helped your organisation benefit from cloud-based solutions.
• Describe your experience in cloud migration in which you played a hands-on role.

The post Top Interview Questions To Land A Cloud Architect Job appeared first on AIM.

The uncertainties ushered in by the Covid-19 pandemic may have slowed down hiring across industries, but some companies are still conducting job interviews – albeit remotely. With lockdowns forcing people to adapt to work from home, companies are also tailoring their business processes and requirements to align with the new work setting.

However, assessing candidates during in-person interviews can be challenging enough; and virtual interviews makes the overall process of accurately screening someone for a specific data science role even more difficult. It can even be daunting for some, who heavily rely on facial cues and gestures to judge a candidate’s competency to perform well in stressful situations.

But conducting remote interviews need not be so hard. In fact, with experience, companies will recognise that using virtual tools to assess a candidate can be more effective and rewarding when compared to conventional methods.

This is because it allows companies to follow a more structured process to get them the information they need. Especially true for data science positions, most companies lay a lot of emphasis on the candidate’s technical expertise and the breadth of their domain knowledge, all of which can be accurately assessed remotely.

Even if most companies are not hiring right now, businesses will do well to understand how to get the most out of remote interviews, since that may become the norm in the foreseeable future. To surmount some of the challenges in conducting a good virtual interview, here are some tips and best practices to make your hiring process better.

Create A Structured Process For Remote Interviews

Companies need to first draw up a framework that clearly outlines a process based on their objectives. In simple terms, it refers to the steps to run the interview smoothly with the candidate, as well as peers who will be joining the interview. Firstly, begin by notifying all stakeholders and find a way to keep everyone in the loop about any possible developments. There are many tools that can keep track of your workflows efficiently, and this can help monitor any changes to the scheduled interview.

These tools will also help systematically document key process changes in one place to minimise confusion and maintain a clear stream of communication. It can also log key behavioural and technical interview questions to use during the interview. Having such a structure in place will also help ensure consistency across interviews for the same position with different candidates. 

This may actually improve the overall interview process for critical data science positions since most interviewers do not refer to notes when conducting interviews. However, virtual interviews allow them the flexibility to conduct a fair interview, since all candidates will be evaluated based on a proper structure. You can also log in your observations and share it with peers immediately, expediting the overall interview process. 

Furthermore, some companies have noted that a common complaint among data science candidates is that the job role and what is ultimately demanded of them was not clearly outlined in the interview. Having a more structured process in place will take care of such ambiguities. 

ALSO READ: Can Kaggle Achievements Alone Be A Good Metric To Hire Data Scientists?

Give All Tech Tools A Test Run 

Delays caused by technical snags can derail remote interviews, and it is applicable for both candidates as well as interviewers. Before you have even scheduled an interview, prep all the tools that you will be using and give them all a quick test run.

If you run into any technical issues during or right before the interview, it does not look professional and will reflect poorly on companies whose core offerings are tech-centred. Moreover, it is likely to agitate you and reduce the valuable time you have earmarked for important discussions with a possibly skilled candidate.

Furthermore, ensure that you have a good tech stack in front of you to get the most out of this interview. If possible, use a unified communications platform that is widely used and allows you to leverage numerous tools to communicate, including screen sharing, instant messaging, etc., in addition to video conferencing. Especially useful when testing the technical knowledge of candidates, try the screen sharing feature in advance and make sure you can navigate it comfortably if you get stuck.

Before any of this, however, make sure your signal strength is adequate, and your internet connection is strong. Set a check-list of things before an interview: Are all your devices plugged in? Are the camera and microphone working properly? Remote interviews can be stressful for both candidates and the interviewer, and a poor connection will only exacerbate these feelings.

Allow Yourself & Candidates To Adapt To The Medium 

While some people, especially freelancers, might have acclimated themselves to remote interviews, most people are still getting comfortable with the format. This means that they will take some time to adapt to the medium, and interviewers should allow candidates that flexibility.

Use certain facial cues to establish trust at the beginning itself so that candidates can open up more quickly. A good way to do this would be to have a friendly demeanour and make some small talk before diving into the nitty gritty of the interview.

Furthermore, give them the time to develop their responses. Candidates in remote interviews may be in a rush to conclude quickly or give cryptic answers since they know they are only able to use their words to hold your attention. Interrupt by asking more related questions, and seem interested in what they are saying, even if it is off-topic. However, be mindful of the time and steer the conversation to relevant topics if necessary.

Building a connection in remote interviews can be challenging but not impossible. Use your words wisely, smile often and even exaggerate your facial expressions if need be – remember that you are doing this to find a good hire for your company.

ALSO READ: Top Free Courses To Help Data Scientists Prepare For Job Interviews

Choose Your Team Wisely

Like in in-person interviews, remote interviews are best conducted with multiple people involved. This is because even if you have a proper structure in place for each interview for the same position, a bulk of the interview should be anchored around the responses generated by the candidate. 

This means that if you missed an opportunity to ask an important follow-up question, someone else in your team can take it up. This also helps mimic a conventional interview, since more people can make the overall interview more conversational and organic. It will also give you the opportunity to collaborate with some of your peers to select the best candidate for your company.

What is more, having multiple perspectives of a candidate is better than one. It can be more difficult to manage since chances of interruptions are more, but the value it can add to the overall hiring process should negate these minor inconveniences. Assigning specific roles to each person in the team will help alleviate some of these challenges.

Outlook

Learning to conduct remote interviews offers you the opportunity to re-evaluate your current interview processes. Is it designed to give you all the information you need about the candidate? What features of remote interviews can you leverage that you would not have got from a conventional in-person interview?

Although it will take some getting used to, use this as a chance to transition to a digital economy more fully. With remote working possibly becoming a norm in the time to come, learning to conduct remote interviews effectively will be an important segment of your overall company experience.

The post Best Practices For Companies Conducting Remote Interviews appeared first on AIM.

Due to the imposed lockdown in most of the countries, the hiring process, which requires job seekers and recruiters being in the same room, has become difficult. To address problems in the hiring process, the popular professional social networking/career development platform, LinkedIn, leveraged AI and introduced two new tools in their arsenal. The solutions will simplify the process of hiring for companies as well as help candidates improve their interview skills.

Linkedin Video Intro – For Creating A Better First Impression

The first tool, LinkedIn introduced, is focused towards easing your virtual job interview. LinkedIn’s own survey tells that 65% of people believe that the impression one makes online is as important as they make in person. But showcasing soft skills online is a challenging task, and it is best shown when one is in the same room with recruiters.

LinkedIn is fast-tracking a solution to this problem with the launch of its new video introduction tool. This tool enables recruiters to ask candidates to send a video response to questions, which will help them to assess their soft skills like communication, presentation, and more.

The video intro app, from the recruiters’ side, works like this: Once applications are received for a job posting, the recruiters can choose the most qualified candidates to answer up to two questions from a list of option LinkedIn provides. This list consists of questions: What is your greatest strength? Tell me about a time you showed leadership? What would your co-workers say about you? Candidates are required to answer these questions via video recording or a written response. The option of submitting a written reply is more important here as some people feel uncomfortable with recording a video response. But, recording a video will let the interviewer know you better and help the candidate stand out from the crowd.

One should keep in mind that this tool helps you create a better first impression on recruiters. This is for introductory purpose, and the actual interview comes afterwards.

We have accelerated the launch of two new features to help you put your best foot forward. We are testing a new video introduction feature and adding an AI-powered instant feedback tool that analyses speech content and patterns to help you test and refine your interview skills.

Blake Barnes, LinkedIn blog post.

Linkedin AI Feedback Tool

The second tool LinkedIn introduced is an AI-powered feedback tool for people who want to practice answering questions that asked in job interviews. A statistics, LinkedIn pointed out, was that more than 50% of people say that they lack confidence and according to a report, 33% of interviewers say that they know whether they are going to hire a candidate within the first 90 seconds of the interview. That’s way less than two minutes, stats like these can make any candidate nervous about their chances. LinkedIn has rolled out its AI-powered instant feedback to help candidates be more prepared while facing a virtual or in-person interview.

Here’s how it works: once LinkedIn members record and upload their answers, they get a detailed assessment of those answers within seconds. This assessment includes metrics like filler words used, frequency of those words, words per minute, and speed over time. One also gets feedback on profanity, rhythm and tempo, phrases that might be considered inappropriate in an interview.

For a better understanding, this feedback tools detect the pace of speech and recommends changes that might help candidates better understand facts and figures. It also recommends alternatives to disfluencies like ‘um’, ‘like’, ‘actually’, ‘basically’ or references like ‘guys’ or even ‘the best man for the job’.

This tool can be accessed right after one has applied for jobs on LinkedIn. It provides an interactive way to practice answering to the most commonly asked interview questions in private, and it gives one a chance to improve their interview skills without external help. If you have a premium account, LinkedIn offers sample answers to the commonly asked interview questions by hiring experts.

The post How to use LinkedIn New AI Tools To Make Virtual Job Interviews Easy appeared first on AIM.

Calls for interviews, albeit exciting, can be quite daunting too. Just as data science is an ever-evolving discipline, so are the dynamic interview practices to land a job in this field. It is often a travesty that perfectly accomplished data scientists fail to do well in some of these interviews, either because they are unable to communicate effectively, or they do not prepare adequately.

Recruiting technology has evolved in recent years. It has become more sophisticated, and if professionals want to stand out in job interviews, they will have to employ new strategies to get noticed.

A host of new online courses can help plug this gap. Whether you are just entering the workforce or looking to switch jobs, these expert-led courses will guide you through the key steps required to ace your next interview. Offering a media-rich learning experience, some of these courses will equip you with specific interviewing techniques to improve your chances of landing a job:

Udemy’s Job Interview Skills Training

About the course: Put together by TeachUcomp, this comprehensive course enables learners to master their interviewing skills. With 33 on-demand video lessons, it will teach candidates to understand what they can do before an interview is scheduled, how they can answer ‘15 most important interview questions’, and even handle the follow-up process after an interview.

According to the course creators, they have incorporated nearly two decades of experience, in-classroom training and teaching techniques to prepare this material. What is more, after completing the class, learners can practice their skills by toggling into the application.

Associated Institute: TeachUcomp
Duration: 1.5 hours 
Click here to enrol

ALSO READ: Top Data Scientists Talk About Lessons Learned While Searching For A Job

Coursera’s Advanced Interviewing Techniques

About the course: Modelled by the University of Maryland, this course aims to give learners detailed strategies to handle tough competency-based interviews. It also emphasises on behavioural techniques, enabling them to identify and understand what a potential employer might be looking for.

Part of the Interviewing and Resume Writing in English specialisation, it covers skills required to respond to tricky questions, demonstrate strengths to interviewers, ask appropriate questions that convey key competencies, and the skills to negotiate good compensation packages. What is more, it also prepares learners for telephonic interviews.

Associated Institute: University of Maryland
Duration: 21 hours
Click here to enrol

Edx’s English@Work: Basic Job Interview Skills

About the course: With up to 3 hours of weekly course work, learners can learn basic job interview skills under a month. Using videos led by industry professionals, it will teach learners the ‘six-step formula’ to give a successful interview, how to successfully stand out, avoid common mistakes, and answer to impress while highlighting their soft skills.

According to the course creators, it will help learners build a solid foundation to emerge successful in any job interview. Although the course is free, learners can get a verified certificate if they are willing to make a small payment.

Associated Institute: The Hong Kong Polytechnic University
Duration: 3 weeks
Click here to enrol

ALSO READ: Top Questions To Detect Unskilled Data Scientists In Job Interviews

FutureLearn’s How To Succeed At Interviews

About the course: Put together by The University of Sheffield, the course is targeted at professionals in the early stages of their career. It would also be helpful to people who have not given an interview in a while, and are thus, out of practice.

According to the course creators, it will help learners get a better understanding of how to anticipate key questions, create a good first impression, dress appropriately for an interview, modulate their voice depending on the question, direct questions at interviewers, and how to successfully handle different types of interviews. What is more, the course also covers telephonic interviews and those conducted over video calls, and how to give good presentations.

Associated Institute: The University of Sheffield
Duration: 3 weeks
Click here to enrol

Coursera’s Successful Interviewing

About the course: Part of a five-course specialisation on landing, preparing, and giving successful job interviews, it covers advanced interview formats, including situational and remote interviews. It also focuses on teaching learners how to conduct thorough research on the company to prepare well for an interview.

Additionally, it covers critical traditional questions (Tell Me About Yourself), how to handle the first 5 minutes when the tone for the rest of the interview has already been set, speech techniques to sound persuasive, how to present accomplishments effectively, and more.

Associated Institute: University of Maryland
Duration: 24 hours
Click here to enrol

ALSO READ: Best Practices For Data Science Job Search

How2Become’s Online Interview Training 

About the course: Although the course is not essentially free, the platform is providing 30-day free access to all its users. Put together by award-winning career coach and best-selling author Richard McMunn, it comprises training guides and videos.

With this course, learners can train themselves to create a good impression, understand commonly held misconceptions when it comes to job interviews, avoid common mistakes, and navigate the dos and don’ts to emerge successful in interviews.

Click here to enrol

The post Top 6 Free Courses To Help Data Scientists Prepare For Job Interviews appeared first on AIM.

With data science subsumed into critical systems across a wide range of industries, it demands that greater care is taken when recruiting for these positions. Moreover, in some cases, an erroneous evaluation can not only affect a company’s profit margins, but also potentially put lives at risk. For instance, with data science integrated into the AI engines of self-driving companies or medical products and services, there is far more at stake.

Looking for ideal candidates who are stress-resistant and adept at vital technologies is challenging enough, but the volley of ‘fake’ data scientists masquerading as skilled professionals makes it even harder. 

With data scientists hailed as one of the ‘sexiest jobs of the 21st century’, there is an emerging trend of more and more people branding themselves as such, even if they remotely happen to work with data, or have a few related tech skills. 

While not intentional in most cases, this can largely be attributed to the novelty still attached to the field of data science, and the lack of a rigid job description that accompanies it. But how can recruiters filter through this to find the right candidates?

A good place to start would be to identify the skill set you should be seeking for that position. We have compiled a list of five broad questions that you can ask candidates when interviewing for data science roles. Although these should be modulated based on the opening, most should be able to test the technical competency of candidates: 

What technologies do you typically use for your data science projects? 

Educating yourself about the technologies they employ and are comfortable working with will offer you a window into whether or not they can be a good fit for your company. This is because if they are inflexible about the tools they use, it may be prudent to think about the associated costs of hiring them.

In some cases, companies may be willing to incur that cost if candidates are able to prove their mettle, and are able to effectively convince you that those choices make practical sense. It will also inform you about the breadth of their knowledge about technologies.

What machine learning model will you choose to perform predictions on a [given] use case? Explain the rationale behind your choice.

Taking the previous question up a notch, this will enable candidates to demonstrate their mathematical understanding of the algorithm they are employing. With the help of a practical use case that is similar to the company’s, this question can be posed to them as part of a larger coding exercise.

In addition to quizzing them on the performance of their ML algorithm, you can also dig a little deeper and test their knowledge on the pros and cons of that approach. Furthermore, follow it up with questions on what they would have done to improve the predictive performances of their approach if given more time.

Test their data preprocessing experience/skills.

It may be useful at some point during the interview process to have an understanding of their data preprocessing experience or skills. Give them a coding exercise using some internal data inputs, and ask them to clean it. 

Since a lot of time goes into preprocessing, it would be useful to know how candidates tackle these issues. Although experience will teach aspiring candidates how to perform better, an understanding of where your candidate finds themself on this scale would be helpful.

Citing real-world problems, explain how you can validate your findings based on the modeling technique you used.

If the answer to this is not clear from the previous questions, it may be important to address it now. This is because while some candidates may know all that there is to know about algorithms and statistics, very few data-focused people may be able to identify which techniques may be appropriate to solve specific problems in the  real-world.

Furthermore, it would only be fair to give candidates an opportunity to take this a step further and even propose a solution to that problem. The ensuing discussion will shed light on the issues of scale, and their knowledge about the sector the company operates in as well – a critical component for data science professionals to understand.

How do you acquire knowledge about new machine learning tools? Do you do that on a consistent basis? 

While this question could take the shape of a discussion, it will give you an idea of how invested they are in the field of data science. You can also gauge their awareness about the industry by the source of their day-to-day learning.

Asking them to list an existing tool that appears to be under-appreciated/over-hyped, and the areas within the discipline of data science that they would like to learn more about will also help you understand the candidates better. Another question like this would be – which data scientists do you admire most and why? 

Outlook

Data science is a blend of scientific tools and techniques that comprises machine learning algorithms, statistics, and programming. Although this necessitates that data scientists are proficient in related technologies, it also demands competence in other fields too.

This includes business acumen, communication skills, and the ability to understand where technology fits in this larger puzzle in order to deliver insights. While it is no easy task to assess – or prove – these qualities in an individual, the above questions could help guide interviews in the right direction.

The post Top Questions To Detect Unskilled Data Scientists In Job Interviews appeared first on AIM.

Even remote data science workers are not spared the dread of facing interviews for job opportunities. No matter how confident you may be of your technical expertise, the best of us walk in with butterflies in our stomach. 

However, most of this nervousness stems from the uncertainty around the entire interview process. Although all companies conduct their interview processes differently, most follow a similar template.

By condensing these common patterns into a few actionable points, we have created a guide for you to ace your next data science interview. However, be assured that it will be a long, drawn-out process. Interviewing for data science roles remote or otherwise can take you through multiple stages over the course of a few months.

Telephonic Interview

Getting a referral and having experienced professionals advocate for you might get you to this stage, but you need to prepare well to get past it.

A telephonic interview by either a technical recruiter or a data science manager is the first step for most companies hiring for data science positions. These calls will broadly assess if you have the right skills for the position you have applied for, and are generally short and to the point.

It will also delve into your past project experience to ascertain if you are a good fit for the company.

One way to handle your interview at this stage is to express your passion for the company you have applied for. Research well and tell the interviewer why it makes professional sense for you to take on this role and associate yourself with the company. Thoroughly review the job posting and compare it against your resume when talking to the interviewer.

Furthermore, do not hesitate to ask questions that demonstrate you researched the company well.

Written Assessment

After you have cleared the telephonic interview, companies often send a home assessment. This will vary depending on the position you have applied for, but typically includes a dataset for you to analyse or even a coding assessment. Assignments also greatly differ depending on the company and will reflect what you will be doing on a regular basis if hired.

The format also varies from company to company. While some may provide an online platform that is unmonitored, others may prefer that you log in with an interviewer watching you perform the assessment. Both will, however, be bound by a strict timeline.

While the outcome of this assessment totally depends on the depth of your technical knowledge, it will be advisable to use detailed visuals to make it interpretable to business stakeholders. This tells the interviewer that you have a good understanding of how your work can drive business value.

Interview With Data Science Manager

This person will be leading the data science team, and maybe somebody you would be directly reporting to. Other members of the data science team/data engineers may also join in for the interview.

While the first interview covered broad topics to assess whether or not you make a good fit for the company, this interview — done over Skype — will be quite technical. Expect them to comb through your resume to discuss the projects you have worked on in the past. 

Take time to explain the logic behind the methodologies you employed and the algorithms you used. Be prepared to meticulously go over everything you have mentioned in your resume — from the tools you used to the logic behind using those.

They may also ask you to expound on some of your answers from the written assessment, so be sure to save your answers and revise them before you appear for this interview. Furthermore, think of ways in which you could have improved on the assessment.

Although these tips should help you ace the interview, attempting to understand what the data science team is working on can help you greatly.

Presentation

Although these are quite rare, some companies may ask you to present the findings of your analysis — the one in the assessment, or from one of your past projects — using PowerPoint. You need not get flustered with colour coding or embellishing your presentation, although it should be visually appealing. The focus should be on your verbal communication with your audience (business stakeholders).

What is the best way to handle this presentation? Explain in simple terms why you used the methods to arrive at your analysis, followed by explaining the outcomes in a non-technical language, and finally addressing the key takeaways.

Final Interview

This may usually be conducted by the HR department or even the CTO. They may ask about how you would like to grow in the company, or what you would like to learn on the job.

This is an opportunity for you to reiterate your passion for the company and how you could bring value to the company. Since this is your final chance, do not hesitate to talk about yourself as long as it can tie back to your excitement for the job. Also, ask questions about the next steps and the future of the company.

The post How To Prepare For A Remote Data Science Interview appeared first on AIM.

You are here for several reasons. My best bets would be a) you have made up your mind about pursuing a career in data science, and b) you want to know why people who fell in the first category arrived at that decision.

There is no shortage of articles that delve into a step-by-step process of how you can become a data scientist. While some attempt to summarise must-have skills for data scientists, others try to describe how they can acquire those skills. Although these are important, I hope to answer the question that should predate this.

Why do you want to be a data scientist?

Because it holds the promise of a career with high pay, high demand, and high security? That is fair, but not necessarily what a recruiter wants to hear. While this is a given, your answer should help them gauge what is it about data science that draws you in and is likely to keep you engaged in the years to come.

It should shine a spotlight on what motivated you to apply for the position and even your inspiration for working for the company. It may also be good to emphasise your interest in data mining and your desire to apply analytical skills to solve real-world issues. In other words, your answer should demonstrate that you are pursuing this position because you are passionate about data (as well as the company you are applying for).

Job portal Indeed has provided a template for aspiring data scientists to use during job interviews:

“I have a passion for working for data-driven, innovative companies. Your firm uses advanced technology to address everyday problems for consumers and businesses alike, which I admire. I also enjoy solving issues using an analytical approach and am passionate about incorporating technology into my work. I believe that my skills and passion match the company’s drive and capabilities.”

While you can recreate this with some variations for your next interview, here are some other ways in which you can answer this dreaded — but straightforward — question:-

Flaunt Your Knowledge

The hype around data science has drowned out a nuanced understanding of the field. This is especially true for candidates who are hoping to enter the industry based on mismatched expectations.

Recruiters understand this very well. Your answer to this question, then, should cut through this bias and inform them that you know enough about the field to not harbour any unrealistic expectations of the job.

You can begin by asking them about the company’s data collection model. More often than not, companies hire data scientists even before they have started collecting the right data. In these cases, data scientists would invariably be involved in every step of the workflow — including data collection, storage, and visualisation. While these may be discussed as they sketch a broad picture of your role, pivoting the discussion to this point on your own is sure to help you get noticed.

Introspect On What Excites You About Data Science

Engaging in something or being involved in it for a long time can drive you to overlook details that are actually uncommon. Think deeply about what excites you the most about data science, and you already have your answer.

Whether it is the ubiquity of this technology, its fast-evolving nature, its applications in solving real-world problems, its scope to integrate previous knowledge with new ideas, its capacity to tame unstructured data, or build predictive capabilities — there is no right answer here.

After you have identified what excites you the most in data science, expound on it and illustrate it with clear examples.

Prove That You Know What The Future Holds

As interested as you may be in data science, all companies want to know if you can challenge yourself by constantly evolving and growing with the technology. While it is good to establish your knowledge about the field, you will also need to demonstrate that you can embrace change just as well and are capable of adapting quickly.

Automation of AI models is likely to usurp the most technically challenging — and perhaps appealing — part of the job. This means that a big chunk of your role would insight and oversight of data — something that you prove you are just as excited about. Understand from them how they are connecting the analytics to the business and how their software interacts with people.

The post Data Science Interview – Why Do You Want To Be A Data Scientist? appeared first on AIM.

Data science aspirants should be mindful of the fact that recruiters are always looking for strong communicators.

Whether you have just graduated from college, or are seeking a career change, data science has emerged as one of the most sought-after jobs today. According to our research, the analytics industry has grown to $3.03 billion in size and is expected to double by 2025.

This may be buoyed by the fact that the ability to collect more data has created a demand for people who can effectively organise and analyse vast troves of information to generate business insights. 

But even as companies scout for talent, data science positions vary greatly. While some lean heavily towards creating models to make predictions, others may involve writing pipelines to transform raw data into structured information. While a key part of your preparation will involve putting together questions you may be asked, it is just as critical to be prepared with a set of questions to ask your potential employer. This not only indicates your level of interest in the role but will also help you understand if the company is a good fit for you.

If you are still on the fence about how to start a career in data science, this will provide valuable insights. For aspirants preparing to crack a job interview in this field, here are eight questions you should consider asking your potential employer:-

What is my expected role and main responsibilities in the company?

Given how new the position is, the role of a data scientist is often difficult to define in clear terms. While it may be challenging for recruiters to draw a picture of the projects you are likely to be involved in, do not leave the interview with just a vague idea of what will be expected of you if you are hired.

Helpful tips: If you are working in product analytics, ask if you will be directly involved in interpreting data to make business decisions. If you are working as a data analyst or a data engineer, attempt to understand if your role is focused on specific analysis tasks, and the depth expected in building and maintaining data infrastructure, respectively. 

What is the metric on which my performance will be evaluated?

Given the hype, expectations of data scientists are sky-high, and this necessitates learning how to succeed at a new workplace. This question also shows that you are thinking about performing well, which invariably aligns with the company’s cultural values.

Additionally, when discussing this, be open to opportunities that would allow you to demonstrate your value in the company. Also, try to understand what they would consider a successful first three or six months.

Where does data science fit within the organisation? (Or) how will the projects I work on align with key business goals?

The best data science solutions emerge when a proper understanding of business needs and data is established. Hence, with this question, you demonstrate that you value business impact and know enough about the company to ask a business-related question. What is more, your curiosity about the priorities of the organisation will indicate that you intend to align your work with these goals and help drive the organisation forward. 

Although this question may be more appropriate for more senior data science candidates, it tells recruiters that you will be invested in the business and are thinking in the right direction. 

Who/which teams will I be working with? Also, who will I report to and what is his/her background?

Data scientists work in cross-departmental teams that require a lot of collaboration, and hence, it is good to exhibit interest in who you will be working with. It will also give you an insight into the company culture.

Asking this question will also give you an idea of the depth of experience your boss has and if they will understand the amount of time your projects might take. Why is this important? Some tasks — though seemingly trivial — may actually take a lot of time to accomplish. This would mean that managers who have little experience with data may underappreciate your work since they do not understand it. However, reporting to a CTO might not always be the better option either.

Technical experience — most of the time — does not equal data experience. But the truth is that many technically-oriented people believe they know more than they actually do. Thus, since time expectations are highly dependent on your reporting manager’s background, it is a good practice to know beforehand who you could be reporting to.

A communications strategy is another area where your boss’ background will have a lot of influence. Are they looking for minute details of how you worked through the data, or do they just want an outline of the impact?

How many data scientists are there in the company, and how do they collaborate with other departments?

This could be a follow-up to the previous question. During interviews, recruiters are always looking for strong communicators who have the ability to work well with other departments in the organisation.

This is because data scientists need to be able to tell the stories that data reveals to people of varying technical knowledge. This means that they should be adept at breaking down complex concepts to colleagues who are trying to implement their findings.

What opportunities does the organisation provide for professional development of its employees?

Showing an interest in additional training opportunities will demonstrate that you are a lifelong learner. With the field evolving quickly, companies are constantly looking for people who can take the initiative to upskill themselves.

What toolset do you use? Are you open to using new ones?

This gives you a window into the organisation’s commitment to technology. 

It also gives off a sense that you are smart and have enough experience behind you to recognise that you are part of a big process.

How did the company handle a project that did not go well?

This will help you understand how a company copes with failures — that is inevitable at some point in its lifetime — and how they learn from it.

A healthy response to failure, in this case, is to take a step back, introspect and then implement processes that could reduce the chances of that happening again.

The post Eight Questions Data Scientists Should Ask Recruiters During Job Interviews appeared first on AIM.

Appearing for an interview can be a daunting experience for many due to the uncertainty that surrounds an interview. It is always a concern for everyone to figure out the questions that might be asked, and some of them may not be even related to the job role at all. In a vast field like data science, the questions can be asked from any corner, which needs thorough preparations. These reasons are enough to put an interviewee in a dilemma, thereby decreasing the confidence of the applicant. 

In this article, we will guide you to crack an interview for the role of a data scientist with several tips that will create a real difference.

Adequate Research

To begin with, it is of utmost importance to understand the designation and job profile offered by an organisation. The job profile might specify skills, tools and techniques which should be clearly understood prior to applying. It is often witnessed that candidates apply for a particular position without understanding the job profile or having the relevant skills for the profile. If a job profile is not clearly mentioned, it is wiser to have a conversation with a human resource officer since data scientist positions vary from company to company. 

Furthermore, it is advisable to conduct thorough research about the organisation as well. The research will give the candidate a sound knowledge of the kind of work done by the organisation, on what scale it operates in the industry and the kinds of consumer it serves. In this way, the organisation’s demand from a candidate can also be evaluated prior to the interview.

Creating A Digital Presence

Creating a strong digital presence is the very first step; a candidate should take while applying for the job of a data scientist. A LinkedIn profile which looks professional to the core should be maintained that specify work history with details about projects worked on. Up next, a GitHub account is a must to give the recruiter a first-hand look at projects and codes which is more convincing than anything else. 

Another place to create a strong presence is Quora, where a candidate can answer several questions related to data science and help people in need. This way, a recruiter can witness the knowledge a candidate holds in regard to data science or how the candidate analyses the questions and answers them. 

Not to mention, StackOverflow is a good place to create an online presence since it is handcrafted for professional programmers to share questions and answers. Also, doing a wide range of projects on Kaggle will demonstrate your capabilities in data science techniques.

A Fine Resume and Positive Personality

The resume is the first piece of paper that helps to create an impression due to which the resume should be composed in a clear and crisp manner, with all the provided information accurate to the candidate’s knowledge. Moving on, preparing for the interview in the most crucial part as the preparation is the key to cracking the interview. 

A candidate should have a piece of thorough knowledge about the company and its operation as a hiring manager often tries to figure how interested the candidate is about the company. A smart hiring manager will put the candidate’s skill to test in order to understand how good the candidate is in terms of the skills mentioned in the resume. 

A candidate’s personality also plays a role in the selection and hence, it is good to keep a positive outlook and clear career goals. In this manner, the hiring manager will be able to understand whether the candidate is an ideal fit for the organisation or not, and what value the candidate brings to the organisation in the tale to reach his or her career goals. The candidate can showcase a number of projects such as Character Recognition and Forest Fire Prediction. To learn more about different kinds of data science projects and get a better idea, read our article ‘Top 10 Data Science Project Ideas for 2020.’

Good Knowledge of Data Science 

As the interview gets going, the interview panel might have a senior data scientist or software engineer along with the hiring manager to assess the candidate’s knowledge about data science. A candidate should prepare about a variety of topics such as machine learning, deep learning and conceptual questions about natural language processing. A lot of questions can be asked about programming along with problems solving questions related to the domain of the organisation. 

Most of the questions will be from statistics, so it would be an excellent initiative to brush up the basics of statistics. Moving on, the hiring manager might throw some behavioural questions to understand how the candidate reacts to different situations that often occur in the organisation. Last but not least, a number of culture fit questions might be on the way. The hiring manager might ask about the recent development in data science or about the latest tools in data science. The reading habit of a candidate can earn some brownie points since the manager would like to know their interest in the domain.

Appearance and Hygiene

The last point has nothing to do with skills or knowledge, but it is a critical point to be followed. Just like the resume, a candidate’s appearance is similar to a cherry on top. No organisation would like to welcome a candidate who is not well dressed or maintains good hygiene. For an interview, formals are the ultimate dress code one should carry. 

However, if a candidate is not sure, asking the recruiter about the dress code is a realistic option to follow. A candidate must be well-groomed and maintain proper hygiene, be it during the interview or while working with the organisation. 
Last but not least, one should be well prepared for the video interview too. Various companies are conducting interviews over video calls. Follow these tips to streamline your video interview process and increase the potential of landing a job.

The post Interview Strategies To Land A Data Science Job appeared first on AIM.

Today, for every job posting, organisations receive thousands of applications from aspirants, thus opting for face-to-face interviews have become tedious to manage for recruiters. Therefore, companies are leveraging platforms such as Skype, Hangout, and other video chat platforms to scrutinise potential candidates immediately.

Video interviews, if not for the final round, are being adopted by enterprises to evaluate applicants for initial rounds. This helps recruiters to trim down the applicants’ list without distributing companies’ resources for managing potential candidates in case of an in-person interview at the office. Besides, the adoption of such platforms is also beneficial for aspirants as they can save their precious time and be more productive.

In this article, Analytics India Magazine gives you five top tips to ace these interviews:

Setup your system

Often interviewers ask to perform some task for evaluating applicants’ technical proficiency. Thus, instead of searching for IDE for coding, you should be prepared with your prefered IDE. Mostly, data scientists utilise Jupyter Notebook. And getting up and running with Jupyter Notebook usually consumes time, as it involves several steps before it starts. One has to open Anaconda Terminal then move to their working directory, write command to activate the desired environment, and then, create a notebook to begin coding finally. To get around with such time taking processes, you should ensure that you already have a clean Jupyter Notebook with relevant packages in the environment.

Budding data scientists should also double-check that all tools are installed and updated to its latest version, as often they do not use all the applications frequently, ignoring the updates are quite common among them. This might lead to an awkward situation for aspirants if the interviewer asks them to do a task on the tool that they have not used for a while. Moreover, few applications do not grant access to its features if the user is running an older version.

Thus, updating the software while making the interview wait would spoil the flow of the evaluating process. No doubt, updates can be released at any time of the day and in rare cases, you may have to update during the interview process, but then you will have a reasonable explanation about it.

Approach and data intuition

Almost all the budding data scientists will boast of their programming skills — so aspirants need to communicate what value will they bring to the table apart from just the technical skills. To ensure this, recruiters ask several questions that have no right or wrong answers for checking your approach to a particular situation.

Case in point, how many cabs are running in your city? While you will have to make a wild guess, but you will not be assessed on the numeric value, you said. Rather, the next question will be: How would you break it down into different variables that led you to estimate the number of cabs?

Categorise the cabs such that it should add up to your previous answer. This is where your approach is examined based on various ways can you ideate. Besides, you can be given datasets, where you need to demonstrate your expertise in pattern recognising and decision-making ability. 

Positioning of eye

Everyone knows that they should focus on the camera so that the recruiter gets your image as if you are looking at their eyes. However, it is easier said than done, so practising is the only solution to mitigate this challenge. Sooner rather than later, such problems will be mitigated as Microsoft in its latest event has introduced a technique which can make eyes look straight even if one is focusing at the screen. But, until it is democratised, one has to manage by finding a balance between the time at the screen and the camera. Continuously, sticking your eyes to the camera is also not recommended as aspirants should have a glimpse of the recruiter for assimilating whether they are happy with the answers.

Professional clothing

Although it is not a face-to-face interview, aspirants must wear formals from top to bottom, as you may have to move for pulling documents if the recruiter asks for. Therefore, you should never take a chance and opt for proper attire.

Other interview etiquettes

Being prepared in advance is crucial as a delay in the interview schedule can impact negatively. Therefore, punctuality is important for making the most out of the time allotted for the interview. Besides, following-up with a thank you note to reaffirm your excitement about the position will demonstrate your interest in the job, which can earn you brownie points.

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Landing a job in the data science domain is not an easy task — it’s not just the knowledge and skills that you need, but you also have to make sure that you give your best when you go for an interview. However, even the interview phases isn’t that easy, especially when the interview goes well, and the HR says s/he will get back to you.

In order to help candidates land a job in the data science sector, we have written a series of articles. However, this time we are focusing on another aspect which is the post-interview phase. In this article, we are going to give you some effective tips that you need to keep in mind when you are done with your data science interview and are waiting for the company to get back to you.

Get Contacts

It is one of the most important things to do when you are about to leave after an interview. However, you might be wondering as to whether it is appropriate to ask for it. Well, when it’s about that dream job, there is no harm in giving it a try. 

Having said that, assess your situation. Do you think you did well in the interview? If you think you did, then subtly ask them if they know when they would be making the hiring decisions, and if they can provide you with their business card or email address so you can check in with them. 

Word to the wise: If they don’t provide you with their contact, then there might be multiple reasons — starting from you not performing well in the interview to their privacy policy. Do not be pushy, it is probably best to pass and leave with a thank you. 

Know When To Send Your First Follow-Up Email

Once you come back from your interview you might feel restless for some time thinking about what the results and you would even feel like sending an email right away — don’t do that. It is not a good practice. If you want to follow up, follow the general rule — two or three days is a good amount of time to wait before sending your first follow-up email. 

Send Personalised Emails

The reason behind getting contacts of all the interviews is that you can send emails to each one of them. However, keep in mind when you are sending that follow-up emails — don’t send the same email to everyone.

When a company is hiring for the data science domain, then it is obvious that someone from the data science domain would be involved in the process. The domain is so vast that only HR cannot take the complete decision. Therefore, make sure you send the emails based on the conversation you had with each interviewer.

If you are sending an email to someone from the data science department, do not forget to ask if they need any more details to check on such as your previous projects or the independent data science projects you have done so far.

Know When To Call

This point has a strong connection with the first one. When you ask for contacts, they might even provide you with their phone numbers; however, you should call that person at random hours. Therefore, if as good practice ask them when the good time is to have a conversation on the phone if you want to check with them.

Be Prepared With Your Research Outputs

When you are done sending your follow-up emails and you have got the responses as well, do not just wait, rather spend your time doing industry research such as salary. The salary discussion is one of the most crucial aspects in the hiring phase — sometimes people end up losing their opportunities by asking a salary that is not relevant to their job role or their experience.

So, make sure you do your research well on how the industry is paying its data scientist and on what basis the paycheck is decided. When you have strong points, you can have a more relevant as well as a strong salary discussion.

Be Transparent

This is one of the most common things the people who are looking for a job done. If you have gone for multiple data science job interviews and manage to land a job, then make sure you let your other potential employers know by sending emails to the contacts you got during your interview. You may be thinking, why do you have to do so. But it is not just a good practice to keep the transparency of the whole scenario, but it also helps you get a better opportunity.

If you are wondering how? If you land a job before one of your potential employers could decide on you, they might reconsider and give you an opportunity, and this might also lead you to get that paycheck you asked for. 

The post 6 Ways To Follow Up After A Data Science Job Interview appeared first on AIM.

Cybersecurity is one of the fastest-growing fields in emerging tech. With the big shift from traditional technologies, organisations are now opening numerous job positions for cybersecurity professionals. According to a report, between January 2017 and March 2018, job postings by Indian employers for cybersecurity roles increased by 150%. The job growth in this sector is also slated to grow by 37% per year at least until 2022.

In this article, we list down 10 most frequently asked questions during a cybersecurity interview.

1| What Is The Difference Between Threat, Vulnerability, And Risk?

Threat, vulnerability and risk are factors related to cyberattacks. A threat is basically an agent which has the potential to cause harm in a target organisation. It includes malware and spyware, among other threat. A vulnerability can be said as a weakness in a security program within the organisation which may be caught by a hacker. Lastly, a risk is a potential for loss when a system has a vulnerability within the organisation.

2| Describe Traceroute

A traceroute is a command-line utility which basically measures the speed as well as route that data takes to a destination server. It works by transmitting the TTL (Time To Live) value through packets. It can help a user to diagnose where the breakdown of communications have occurred. 

Read more from here.

3| What Do You Mean By CIA? Why Is It Important?

CIA is a security model which helps in guiding the security policies within an organisation. CIA stands for Confidentiality, Integrity and Availability. Confidentiality controls access to information, integrity assures the accurateness of sensitive data while availability is the assurance of reliability as well as constant access to the sensitive data by the authorised professionals.

Read more from here.

4| Difference Between HTTPS, SSL & TLS

HTTPS (Hypertext Transfer Protocol Secure) is a protocol which is used to communicate or exchange information. SSL (Secure Sockets Layer) is a standard cryptographic protocol which enables secure communications over the internet. TLS (Transport Layer Security) can be said as the successor of SSL. It is similar to SSL and provides additional and better protection of data than SSL. 

Read more from here.

5| What Is A Three-Way Handshake?

A three-way handshake, also known as TCP handshake is a method used in the TCP-IP network to create a connection between a local host/client and server. This method requires both the client and server to exchange SYN and ACK packets before the actual communication of data begins.

Read more from here.

6| What Is End-To-End Email Encryption? How Does It Work?

End-to-end email encryption is a procedure of transmitting data where only the sender and receiver are able to read email messages. This method requires both the sender and the receiver to have a pair of cryptographic keys. The sender encrypts the message locally on his/her device using the recipient’s public key. The receiver decrypts it on his/her device using his/her private key.

Read more from here.

7| Difference Between Symmetric & Asymmetric Encryption 

Asymmetric encryption is also known as public-key cryptography which uses two keys to encrypt a plain text. Popular asymmetric key encryption algorithm includes ElGamal, RSA, DSA, etc. It is mostly used in day-to-day communication channels. Symmetric encryption involves only one secret key to cipher as well as decipher any information. The secret key can either be a number, a word or a string of random letters.

Read more from here.

8| How Do Encoding, Hashing & Encryption Differ?

Encoding, encryption, and hashing are kind of similar terms and can create confusion sometimes. In the Encoding method, the data is transformed into a form which is readable by most of the systems and can be used by any external process. Encryption can be said as an encoding technique where the data is encoded in such a way that only authorised users can access the data. Hashing ensures integrity by converting the data into a hash function, which can be any number generated from string or text.

Read more from here.

9| What Is Social Engineering Attack?

The act of manipulating a user of a computing system to reveal confidential information which can be used to gain unauthorised access to a computer system is known as social engineering attack. Some of the common techniques are familiarity exploit, phishing, intimidating circumstances, tailgating, etc.

Read more from here.

10| How Can You Defend Yourself From Cross-Site Scripting Attack?

Cross-Site Scripting attack or XSS can be said as one of the most common vulnerabilities which can be found in applications. There are three main types of cross-site scripting attacks: Stored (or persistent) XSS, reflected XSS and DOM-based XSS. It is very difficult to remove this type of vulnerabilities. Methods such as escaping, validating input and sanitizing can help in preventing such type of attacks.

Read more from here. 

The post 10 Frequently Asked Questions In A Cybersecurity Job Interview appeared first on AIM.

In order to keep up with the ever-changing world, organisations are leveraging artificial intelligence. There is no denying that AI is redefining industries by providing greater personalisation not only to companies but also to users and is disrupting how people used to work. But what if we tell you that your next interview is going to be with an AI-based bot? Now, AI has also stepped into the recruiting space and is taking interviews to hire talent.

Here Are Some Of The AI-Powered Interview Platforms

Gecko.ai

Gecko is another AI-based interview platform that works on AI, Sentiment analysis and facial recognition. The beauty of this video-based evaluation bot is that it can conduct both live and offline interviews.

The questions for interview are being set by recruiters that can later be played back for detailed analysis and review. That is not all, the platform’s AI-powered sentiment analysis engine scans each interview to provide deep insights on candidate attitude, positivity, and overall sentiment.

The bot is designed in such a way that it even follows up with prospective candidates for interviews via email. And it continues to do that until candidate responds.

Mya

Mya is a conversational AI assistant helps hiring teams of firms to form trust and confidence with candidates through open-ended, natural, and dynamic conversations. This AI-powered interview platform uses deep learning to deliver a human-like conversation to the candidate.

Using semantic parsing, named entity recognition, and multiple intent classifications, Mya captures meaningful information from the candidate. The bot is designed in such a way that it understands context, complex, multi-part statement, changed answers, or interjections and it can also change conversation direction. That is not all, Mya is in continuous learning the process as its machine learning algorithms improve with every conversation.

AutoView

A product of the AspiringMinds, AutoView is Artificial Intelligence powered interview bot. The platform uses Video analytics, Neural language processing, Machine learning, and Speech recognition to carry out an interview process.

Talking about interviewing, AutoView screens candidates based on 4 parameters: based on candidate’s facial expressions and gestures; sentiment analysis of voice and text; ability and knowledge required for the job role; and workplace competencies, cultural fitment and personality.

That is not all, the platform is available 24×7 and a student can take the interview as per his/her convenient time.

Paññã

Paññã is a data-driven AI video interview platform that provides artificially intelligent hiring, an ever-growing repository of dynamic questions, expert evaluation, recorded interviewing, video conferencing and voice and face recognition.

One of the best thing about this platform is that it blocks out proxy interviews and under-qualified applicants from large pools of candidates. That is not all, from live interview to dynamic custom interview, Paññã provides a highly professional interview environment.

Also, the platform prepares and conducts intelligent interviews that are unique and progressive in nature.

HireVue

HireVue is an AI-powered interview platform that not only helps recruiters but also candidates. The platform’s hiring intelligence has gained significant traction recently and today, it is transforming the way companies discover, hire, and develop talent.

HireVue uses a combination of proprietary voice recognition software and licensed facial recognition software. With the help of an algorithm, the platform determines which candidates resemble the ideal candidate. And it does it all by analysing traits such as body language, tone, and keywords used during the interview. Once the algorithm is done with its job, it lets the recruiter know which candidates are at the top of the heap.

The post Top 5 AI-Powered Job Interview Tools Used By Recruiters appeared first on AIM.

The increasing data has given a rise to the number of professionals who can draw valuable insights from it. Data engineer is one of the most popular positions in companies and is crucial to the analytics team. Data analysts and other roles are often confused with data engineer roles, but the latter is usually involved in building infrastructure or framework necessary for data generation. They work on the architecture aspect of data, like data collection, data storage, and data management, among others.

Having said this, every company may have its own definition of what a data engineer, the hiring process remains largely the same and so does the interview questions. If you are applying for a data engineer role, these are the most likely questions that you might be asked:

General Questions

What are the different types of design schemas in data modeling?

• There are two schemas in data modeling: Star schema and the other is Snowflake Schema.

How is the Hadoop database different from the traditional Relational Database Management System?

• The Hadoop database is a column-oriented database which has a flexible schema to add columns on the fly. It is equipped with sparse tables with tight integration of MR (market research) and horizontal scalability, very efficient for semi-structured and unstructured data.
• RDMS is designed for the row-oriented databases with a fixed schema. It is optimized for joins and not for sparse tables. Not having integration with MR makes another major difference from Hadoop. RDBMS is preferred for the structured data

Elaborate on Hadoop distributed file system

• Hadoop can work directly with any scalable distributed file system such as Local FS, HFTP FS, S3 FS, and others, but the most common file system used by Hadoop is the HDFS
• The Hadoop Distributed File System is built on the Google File System (GFS) and contribute a distributed file system that is designed to run on large clusters (thousands of computers) of small computer machines in a definitive and accurate manner.
• HDFS uses a master/slave architecture where master consists of a single NameNode that manages the file system metadata and one or more slave DataNodes that store the actual data.

How data analytics and big data can boost business revenue

• Using data in an efficient to ensure the business growth
• Maximizing the customer value
• Cutting down the cost production of the company
• Turning analytical to improve staffing levels forecasts

Technical Questions: Get Set Sode

Data science has an in-depth coding involved which requires the programming knowledge of various languages such as python, java. Statistical software as R programming. Database systems like Hadoop. Testing tools of ETL and task automation platforms like Powershell. Here are a few questions asked on these topics.

Python

Name a few well-known python packages    

• Pandas: It’s A package which provides adaptable data structures for working with relational or labeled data.
• NumPy: A package which grants you to work with numerical based data structures
• Matplotlib: Its A 2D rendering engine written for especially for Python.
• Tensorflow: its A package used for developing computational graphs.

What are Lambda functions?

Lambda functions are functions without a name. We can define a function and use it as a lambda function. It can be understood by the below example.

               g=lambda z :z*2

                a=g(5)

                Print (a)

                ##5*2=10(out put)

What is meant by *args and **kwargs?

When a function is ordered its known as *args. The unordered arguments used in a function are called as **kwarg. To understand better we will see an example.

   def total_cost(number=1, price_per_unit=1):
    return number * price_per_unit

    total_cost(number=10, price_per_unit=12)

   total_cost(price_per_unit=12, number=10)

The arguments number and price_per_unit are kwargs are optional arguments and can be reversed

when arguments cannot be inverted those are known as *args. We will see an example for these *args.

             def square_area(side):
               return side*side

               square_area(5)

                  ##25(output)

What is the difference between list and tuples? Give examples.

• Lists can be defined as mutable, that is, they can be edited. For example, list_1=[‘naren’,123,’india’]
• Tuples can be defined as immutable (tuples are lists which can’t be edited). Eg:list_1=(‘india’,100,’virat’)

R Programming

How can a .csv file be loaded in R?

How do you install a package in R?

Mention some widely used packages for data mining in R?

• data.table- this package contributes for throughout examination of large files.
• rpart and caret- these packages benefit in machine learning prototypes
• Arules- used for association rule learning.
• ggplot- maintains distinct data visualization plots.
• tm- help in performing text mining.
• Forecast- implement functions for time series analysis

Hadoop Database

What are the main methods of a Reducer?

• setup(): this method is used for configuring various parameters like input data size, distributed cache.

public void setup (context)

• reduce(): a heart of the reducer always called once per key with the associated reduced task

public void reduce(Key, Value, context)

• cleanup(): this method is called to clean temporary files, only once at the end of the task

public void cleanup (context)

Mention the various schedules in a Hadoop framework.

• COSHH (a classification and optimization based schedule for heterogeneous Hadoop systems) – is a scheduler which examines heterogeneity at both the application and cluster degree.
• FIFO Scheduler –in FIFO scheduling, a jobbing reporter picks jobs from a work queue, oldest job first.
• Fair Sharing scheduler-in a fair share scheduling the goal is to assign resources to jobs such that on mean time, each job obtains an equal share of the accessible resources.

Microsoft PowerShell

Explain what is the importance of brackets in PowerShell?

• Parenthesis Brackets (): Curved parenthesis style brackets are used for mandatory arguments.
• Braces Brackets {}: Curly brackets are used in blocked statements
• Square Brackets []: They define arbitrary items, and they are not used frequently.

Mention the three ways that PowerShell uses to ‘Select’

• The most familiar and widely used  way is the Wmiobject technique, in this technique we use ‘-query’ to introduce a classic ‘Select * from’ a phrase
• The second widely used method used for ‘Select’ in PowerShell is Select-String. Which completely checks for a word, phrase or any pattern match.
• The third way is Select-Object.

Conclusion

Getting a data engineer post is tough but not impossible. With numerous complications associated with collecting and managing data, this field is now hosting to a wide array of jobs and designations. Having the ability to integrate knowledge, skill and analytical approach is essential. It’s not just about data science; it’s about having the ability to transform that data into visualization. Your strategy will only be as good as the data, so take the time to graduate with skills required to be a data engineer whom employers will want to hire.

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