The ultimate guide to passing your next data science interview

Written By Julien

Updated in December 2023.

Data science is an interdisciplinary field that combines statistics, mathematics, scientific methods, AI, specialized programming, analytics, and storytelling to extract value from data.

A Data Scientist applies statistical methods and uses a wide range of tools and techniques to analyze and prepare data. They also explain the meaning behind the results to various stakeholders.

Data can help companies achieve their goals and solve problems. Data science serves as a bridge between data and the boardroom, connecting the business and technical worlds. A skilled data scientist can effectively communicate with individuals at different levels of expertise.

This guide is intended for data science novices. It offers two tracks to explore: a technology track and a business track. You can choose to focus on one area of improvement or easily switch between the two. It provides actionable insights to help you tackle any data science case.

Tech track 💻

Before going into data science module details, we’ve listed two all-in-one data scientist tracks (one free, one paid). Once you've covered these topics, you can directly train on Kaggle.

Free option

We recommend two very reputable courses on Coursera from the University of Michigan (Applied Data Science with Python | Coursera) and Stanford (Machine Learning by Stanford University | Coursera)

Paid option ± €45/month

Data Scientist in Python Career Path – Dataquest
This track covers most of the data science entry-level concepts (basic python, data manipulation, visualization, modeling, SQL queries, git, command line…)

If any of the above topics are unclear to you, or if you simply want to improve a specific skill, here are the different resources for each module that we find interesting.

1. Exploring data science

No matter your technical background, data science is open to everyone if you have some interest in coding and data manipulation. We list here resources to get started on the most-used programming language, Python, and have a better understanding of this multidisciplinary approach.

Books

  • Introduction to the coding world: Automate the Boring Stuff with Python. A great book to start coding with Python. It starts with the foundational concepts of programming. It is very hands-on and practical if you do the exercises while reading it.

  • Basic python commands: Data Science from Scratch First Principles with Python by Joel Grus. This book is focused on introducing basic Python, and practical coding concepts of what you may do day-to-day as a Data Scientist.

  • Introduction to machine learning: The Hundred-Page Machine Learning Book by Andriy Burkov. Although the premise of condensing ML knowledge might seem dubious, the author does an excellent job of giving an overview of what ML is about. Some math and coding examples are present, and it contains quite a lot of details for such a short book.

YouTube: Python tutorials

A channel that covers a range of very basic (python installation, data types etc.) to more complex Python topics (e.g. building a web app).

Corey Schafer - YouTube

Autodidact (Try-Error-Success):

Compiles resources to make an "open-source curriculum for learning Data Science". There are lots of resources, all open source (but not necessarily free). Not only do they give recommendations on learning resources for ML, Math, Data viz, Python, but they also give suggestions on the order in which they should be tackled.

The Open-Source Data Science Masters by datasciencemasters

2. Improve your data science skills

If you are someone that has a background in mathematics, statistics, data analysis, these more advanced resources will help you improve your technical skills further.

YouTube

Understand the essence hidden behind the complicated formulas used in data science and advanced statistics classes.

Books

  • The Data Science Interview Book
    As the name implies, you’ll find a very useful summary of plenty of tech aspects of data science. It is not 100% exhaustive for an Agilytic interview, since the Business angle is missing. Regardless, a very structured and useful summary.

3. Challenge your data science skills

So, you absorbed as much information, concepts, and techniques as it feels humanly possible. It’s time to put your skills to the test! Here are some places to help you get a clearer idea of what you’ve mastered, and what could be improved. Don’t forget to upload on GitHub your work. This will bring all your data science skills to light in the entire digital world.

4. If a more structured learning path is what you're seeking

If you have the time and seek a comprehensive training, consider enrolling in a Data Science master's program. A master's degree affords you more time for in-depth practice, enabling a deeper understanding of statistical tools and machine learning techniques. Additionally, complementing your academic journey with a data science internship provides an excellent opportunity to elevate your skills in a real-world setting.

Below is a non-exhaustive list of high-quality Data Science master's programs in Belgium.

Specialized Master in Big Data & Data Science (MS-BGDA) - ULB - 1 year

This master program requires candidates to have completed a prior master's degree. It is designed for individuals with a foundational understanding of statistics and coding who aim to advance their expertise and specialize in data science.

Within the master's curriculum, you have the option to select either a thesis or an internship. This choice provides a valuable opportunity to apply the theoretical knowledge gained in the courses to real-life challenges.

It's important to note that the master's program grants access to data science courses across faculties, including Engineering, Sciences, and Solvay (Economics).

Master of Statistics and Data Science - KUL - 2 years

Whether you're completing your bachelor's degree or seeking to enhance your knowledge post-master's, this program offers a unique two-year opportunity to delve into the realm of data science and elevate your coding skills. Please note that our master's program does not include a structured internship component. If you're seeking this type of experience, you'll need to pursue it independently alongside your academic commitments.

Business track 💼

1. Understand a problem / Define the objective

Your main goal as a data scientist is to derive actionable insights from data. To avoid chasing unanswerable questions that won’t lead anywhere, it is important to know where you are going, and how you are going to get there. Knowing how to understand a client’s problem and define their business objective will be a significant asset. We suggest the book Cracked it! and case interview frameworks:

Cracked it!: How to solve big problems and sell solutions like top strategy consultants.

This book trains you in how to tackle any challenging problem efficiently and sell its solution. Based on business case examples, it shows how to state, structure, and resolve problems and to be solution/results oriented.

For business cases situations, we recommend reading some very well-known business frameworks (used by top-tier consulting firms) only to better structure your thoughts (don’t learn them by heart):

2. Master your interview

Your interview is your time to really shine and ‘sell yourself,’ while giving the first glimpse into your communication skills. We’ve gathered in-depth interview tips that might help. It’s a cliché but it’s true, make sure to practice, practice, practice.

Be prepared to answer data science questions. Notably, how to approach a problem, your structure, and how you manage to draw conclusions. This website offers some examples: OVER 100 Data Scientist Interview Questions and Answers! | by Terence Shin | Towards Data Science

3. Learn to share your insights with others:  

What is even more important than the new solution, tool, or model you’ve created is how you share it with others. Data science is just as much about working towards executing a new project as it is communicating your findings with clients or colleagues. Nail your presentation and storytelling skills and you’ll be in good shape.

We highly recommend reading the book Storytelling with data - mandatory reading at Agilytic. We see significant improvements in the quality of the visualizations and presentations of our colleagues after they have read it.

Storytelling with Data: A Data Visualization Guide for Business Professionals.

Nice to Have – Boost your CV ⚡

1. Work collaboratively with your data team

From time to time, our team challenges might include plastic dinosaurs.

You may have this idea of a data scientist as someone who is working solo for long periods of time. We’re here to bust this myth! Over the years, we’ve seen again and again that the best and most helpful ideas come out of team collaboration and discussion. To facilitate reviewing and building on one another’s work, we use the ever-popular GitHub.

2. Learn new BI tools

Business intelligence (BI) insights can be useful to learn from existing data to inform decisions and operate the company’s current state. You may hear it in comparison with business analytics, which refers to using company data to anticipate trends and outcomes. Microsoft Power BI and Tableau are the most popular tools to help with data visualization, accessing, and analyzing data sets, mainly for reporting purposes.

3. Know your computer

Computer science allows us to produce quality analyses and make great discoveries each day. Understanding how a computer works, especially when an incomprehensible error appears on the screen, can save you a lot of time and effort in the configuration of some tools or even in debugging.

  • Videos MIT tutorials: An MIT Open Course divided into 10 lectures/online chapters, and you might want to select those who interest you most. A great resource to learn about topics useful for developers, but usually not tackled in formal education, like shell tools, editors (vim...), command-line, Git, and debugging

4. Be familiar with cloud technology

In the field of data science, familiarity with cloud technology is a must. It provides scalability, accessibility, and efficiency, enabling data scientists to analyze data, deploy models, and collaborate seamlessly. Mastering the cloud is crucial for staying competitive and delivering impactful insights in today's data-driven world.

Interested in a data science career at Agilytic?

Did you know that Agilytic has current Data Scientist, Engineers and Managers openings?

We are always looking for new colleagues to help take our data science and engineering practice to the next level and contribute to the entrepreneurial project.

You can find out more about the job and the application process here.

We value a work environment that allows you to do great work, improve your skillset in data science and, most importantly, be happy while doing it.

Sounds interesting?

Apply today!

Julien https://www.linkedin.com/in/julientheys/
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