The Preparation Guide for Data Science and Analytics Interviews - Issue 216

Your go-to list of resources for preparing for data and analytics job interviews.

Welcome to the Data Analysis Journal, a weekly newsletter about data science and analytics.

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As I catch up on your questions after returning from my travels, one common theme stands out: how to prepare for interviews for data and analytical roles.

So today, I’ve decided to put together a consolidated guide on everything I've already published related to interviewing—how to prepare, concepts to brush up on, vocabulary to remember, case studies to review, and so on. Later, I’ll create similar guides for A/B testing, ML, and maybe product analytics.

If you’re actively interviewing or just want a reminder of when the simple becomes complex, keep this guide close!

Any interview process is stressful and, unfortunately, often unfair. 

Candidates are not getting paid for drafting strategies, breaking down roadmaps, creating decks, designing flows, or solving data challenges for potential employers. There is no law making recruiters and hiring managers accountable for their attitude, lack of effort, or inappropriate exercises/questions.

🚫 Say no to take-home assignments.

Take-home exercises or assignments should not be accepted in 2024. 

I do not offer take-home or online assignments because I consider them disrespectful of candidates’ time. There are far more effective ways to evaluate a candidate’s knowledge, experience, or fit. If the hiring manager fails to do this, it shouldn’t come at the cost of the candidate’s time. Say no to any assignments and instead offer a portfolio, an additional interview screen to demonstrate your skills, or request compensation.

👤 Your Portfolio is your best resume.

This might be controversial for some, but I personally no longer read CVs and resumes. For me, a candidate's LinkedIn profile and portfolio (whether on GitHub, Kaggle, a blog, or public dashboards) provide a much clearer picture of their skills, interests, and experience.

Once resume building, proofreading, and ATS-friendliness (which is a complete scam) became a science on their own, it made it harder for hiring managers to recognize the right-fit person between all the polished and exaggerated wording (yes, your Power-BI dashboard indeed increased company net new revenue by 200%). It also takes quite some time to fact-check the experience and education from a resume, whereas LinkedIn removes that burden from you.

💔 Don’t take it personally

I've had to say “no” to strong candidates many times because they were missing one important aspect of the role, such as experience setting up analytics for mobile apps, experience with paid recurring subscriptions, or knowledge of ad attribution. It’s impossible to be an expert at everything, so focus on becoming the best in the specialty you love and differentiate yourself.

Don’t overstress, and don’t stop trying, even if it doesn’t work out at first. Also, don’t stress over acceptance rates or interview difficulty levels.

Preparing for the interview

From Interview Query

If you go through the publications below, I guarantee you will be better prepared and equipped with the foundation for most data roles, although I mainly focus on data scientist and analytical roles in my newsletter.

First-round: 

• How to pass a first-round interview for data science roles

• How To Measure Data Quality

Take-home assignment / Portfolio

• How To Pass A Technical Assignment For a Senior Data Scientist Position

• Data Portfolio Done In Notebook

Honing skills:

For any data or analytical positions, you will be interviewed in at least 2 or 3 categories listed below. I’ve put A/B testing in a separate section because, frankly, I don’t know of any company that isn’t running experiments today. 

Analytical Thinking

• Introduction To Problem-Solving And Critical Thinking

• A Deep Dive Into User Engagement Through Tricky Averages

• How To Do a Root Cause Analysis

• Introduction To Proxy Metrics

• Fixed vs Rolling Time Windows?

SQL:

• A Selection Of SQL Tutorials

• How To Pass A SQL Interview For A Data Scientist Position

• Top 10 Advanced SQL Functions For Data Analysis

• How To Get Average Logins Per User Per Day

• How To Get Randomly Distributed Users in SQL

• Parsing Semi-Structured Data In SQL

• When Simple Becomes Tricky: SQL CASE Statement

• SQL For Weekday Product Usage Analysis

• The Frequency Of User Activity: SQL and Analysis

• Getting Date Functions in SQL

Python: 

• A Selection Of Python Tutorials for Analysts

• Python For Data Science: The Difference Between Merge, Join, And Concat

• Data Analysis in Python: A Guide to Working With Dates in Pandas

• Getting Started With Python Plots

• SQL And Python Mistakes To Avoid

A/B tests: 

• A/B Test Checklist

• Playbook For Launching, Monitoring, and Analyzing A/B Tests

• Why You Shouldn’t Stop A/B Tests Early

• Why You Shouldn't Stop A/B Tests Late

• How To Run An A/B Testing On Low Traffic

• An Analysis Of Bias Or Why A/B Testing Fails

• Methods To Accelerate A/B Testing

Statistics

• What Statistics Are Used In Data Analysis?

• Applying Statistics In Product Analytics

• How To Prove Causation

• Ways to handle the missing data in your modeling or analysis

• When To Use Mean Or Median

• Outliers: To Drop or Not to Drop?

• How To Find Optimal Proxy Metrics

• Decoding Regression Scores

• Statistics 101: When Simple Becomes Tricky

To close the loop, my guide for hiring managers with my interview questions - How To Hire Exceptional Analysts and Data Scientists.

Most importantly, stay confident and positive. Keep your spirit up, and good luck! Analytics is exciting! 

Thank you for reading, until next Wednesday!