Top 7 Essential Cheat Sheets To Ace Your Data Science Interview
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Landing a data science job is no easy feat. With companies receiving hundreds of applications for each opening, you need to stand out from the competition to get an interview. And once you land the interview, you need to demonstrate both technical competence and communication skills to prove you’re the right person for the role.

That’s why having the right preparation and materials can give you a critical edge. In his new blog we will cover the most important cheat sheets that every data science candidate should review before an upcoming interview. The cheat sheets cover a wide range of key data science topics, from statistics and Python to SQL and machine learning algorithms.



Structured Query Language (SQL) is used for managing and accessing the database. It is the most important skill that data scientists need. Apart from accessing the data, data professionals use it for running data analysis queries on a large amount of the data. 

No matter which technical data interview you are preparing for, the Getting Started with SQL cheat sheet will be a handy guide for you. It will help you revise common syntax and teach you how to use them. Moreover, it will also assist you with coding interviews.



Many data scientists do not use probability or statistical tests in their daily work. It can be difficult to stay updated with all the important terminologies. However, it is important to note that you may be asked about concepts such as A/B testing, confidence intervals, hypothesis testing, correlation analysis, and more.

If you are afraid of feeling embarrassed during an interview, you can refresh your memory by referring to the Probability and Statistics cheat sheet. Provided by Stanford University, this cheat sheet includes all the essential terminology that may be used during the interview.



Pandas is a Python library that is primarily used for data cleaning, wrangling, analysis, processing, and saving. During an interview, you may be asked about various components of this library and how to analyze data using pandas. You may also be asked to perform data analysis and write a report based on your findings.

The Pandas Data Wrangling cheat sheet provides byte-sized information on various pandas functions with visual representation, helping you in technical and coding interviews.



Data visualization is an important skill for data scientists. While data scientists may be good at analyzing data, choosing the right type of plot to effectively communicate insights is a bit tricky. During interviews, failing to select the optimal chart to showcase analysis can create a poor impression on interviewers. 

To avoid this pitfall, data scientists must have a look at the Data Visualization cheat sheet in order to instinctively select the ideal plot to convey the message they aim to deliver to stakeholders. This will help you with coding interviews and take-home assignments. 



Scikit-learn is a widely used Python library that offers a broad array of tools and functionalities for implementing different machine learning algorithms. As a data scientist, you may be required to solve basic regression problems using various Scikit-learn functions for data augmentation, processing, model training, and optimization.

Building and evaluating machine learning models is a crucial part of a data scientist’s job. It is natural to learn various functions of Scikit-learn by reviewing the Scikit-learn for Machine Learning cheat sheet. 



Git is an essential skill for data scientists to master, especially those working on collaborative teams. On any data science project with multiple contributors, Git enables version control and code merging so team members can concurrently work on code without runtime conflicts.   

You must demonstrate your Git skills before being invited to work on the project. So, it is essential to review the Git for Data Science cheat sheet to learn the most commonly used syntax and functions.



The Data Science Super cheat sheet is a bit different. You will review it to learn all of the important theoretical concepts. 

You will learn about: 

  1. Distributions
  2. Various machine learning concept
  3. Model evaluation
  4. Linear Regression
  5. Logistic Regression
  6. Decision Tree
  7. Support Vector Machine
  8. Clustering
  9. Dimensionality Reduction
  10. Natural Language processing
  11. Neural Networks
  12. Convolutional Neural Network
  13. Recurrent Neural network
  14. Boosting
  15. Reinforcement Learning
  16. Anomaly Detection
  17. Time Series
  18. Statistics
  19. A/B Testing

With one hour left before your interview, this cheat sheet is all you need to review. It will help you go over the most commonly asked interview questions.

I hope you enjoy the list of the seven essential cheat sheets. Let me know if you’d like to see more similar content.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

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