technical questions
September 29, 2020

Resources to help you prepare for technical questions & tests

By Paul Laughlin

To conclude our series for those changing jobs or out of work at present, let’s focus on preparing for technical questions & tests.

I started this series by sharing how impressed I’d been with the new approaches adopted by many who were between jobs. Then Tony Boobier shared with us ‘10 rules for reemployment‘ based on a book by Olivia Jules. My review of his book on ‘AI & the future of Banking‘ might also help those seeking work in that sector.

Building on that, Kevin Watson shared a template to help you create a blueprint for your ideal job. I then shared a 9-step model to help you prepare for evidencing softer skills in your interview. I’ve also shared some experience for those considering starting up their own business instead.

So, now, to conclude this series, I will briefly share 4 recommended resources to help those seeking data & analytics roles. To help you prepare for the technical aspects. Whether your assessment process includes technical questions, tests or an exercise plus presentation – you should find something to help.

Preparing for Data Science/Engineering interviews

Many candidates these days will be seeking data science, data engineering or data ops roles. I’ve covered elsewhere why I recommend that the importance of analysts is not overlooked. However, given the current job market & the roles advertised, I can understand why many focus here.

So, to start the resources I’ll share, here is a good overview post which helpfully covers 4 general topics. At the end of his post, Harshit Tyagi also suggests 4 focus areas for further study for 4 different roles. Use this as a jumping-off point to test where you may need to learn more it should serve you well.

As this post is focussed on your technical prep, I won’t critique the lack of focus on the much needed softer skills in those roles. However, I was disappointed not to see more emphasis on data visualisation skills. For those, worth checking my own series of recommended resources.

Preparation Guide for Data Science Interviews

These are unprecedented times where many of us are looking to switch or land a job. Interview preparation has come to the limelight. And interviews are a big deal for everyone. Uncertainty, randomness, and human errors make an interview damn scary. Adrenaline rushing through your veins, you are on the verge of messing it all up.

Going deeper with the prolific Analytics Vidhya blog

If you need to demonstrate the depth of your technical knowledge & expertise, you are going to need more than the above review. With a particular focus on data science roles, prolific & generous blogger Pranav Dar shares this comprehensive guide.

Modestly entitled “The Most Comprehensive Data Science & Machine Learning Interview Guide You’ll Ever Need”, it is impressive. Certainly providing both neophytes & experienced data scientists with resources to keep learning.

I’d also encourage those seeking other data roles or analyst roles to not be put off by the Data Science & Machine Learning. There are also several sections that would be relevant for them. These include questions on Statistics, Analytics case studies, logic puzzles & SAS/SQL related questions. Well worth a browse as treasure trove of useful links.

The Most Comprehensive Data Science & Machine Learning Interview Guide You’ll Ever Need

This is the most comprehensive interview guide you will find for data science and machine learning interviews. From questions on data science, Machine Learning and deep learning to how to prepare and behave in interviews, this guide has it all!

In all the focus on technology & coding, don’t neglect statistics

Building on my earlier comment about not neglecting the importance of data visualisation skills, I’ll also prioritise statistics. Even within this focus on evidencing technical skills, I have seen too many candidates fall short by demonstrating coding skills without equally showing statistical thinking.

So, I was pleased to see this short set of 5 Probability questions shared by Adam Sabra on Towards Data Science. I’d recommend having paper & pen to hand when reading this – so you can note down your own approach before reading each answer.

5 Probability Questions to Test Your Skills

With many of you applying to Data Science positions, it is expected to be asked various sorts of probability questions during the technical aspect of the interview process. Within this post, I aim to cover 5 different probability questions (increasing in difficulty) which I believe serve as a good blanket to the various types of questions you would expect in the interview process.

Towards Data Science is a great blog (on Medium) to explore during your preparation for an assessment process. As well as the above example there are many other posts sharing questions to test yourself. Another one that caught my eye (as it focusses on analytical thinking) is one where they share secrets from Google. Here are 6 questions that Google have asked in their interviews:

Google’s Data Science Interview Brain Teasers

As a part of Google’s Data Science Interview, they like to ask questions that they call “Problem-Solving” questions which are pretty similar to brain teasers. In this article, we’ll look at six questions that Google’s asked and provide the answers below!

How will you plan & track your preparation?

I hope those above resources help you to prepare for your interview, congratulations on getting one. As a reminder, please also check out my previous posts on evidencing softer skills & being clear on your priorities. You may also want to check out the suggestions on how to contribute positively during your ‘between jobs’ time.

Finally, with all the work to be done in both marketing yourself, finding a role & preparing for an assessment – I recommend planning. Fortune favours the prepared & planning can ensure you prioritise rather than randomly cram at the last minute.

So, take some time now to write a list of the tasks you’ve identified for this project. Identify dependencies & prioritise. Then decide on the most important 3 things to do each day. You might also like to visually track your progress somewhere you’ll see each day. Very best wishes for success!