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Getting a job in data science is not an easy task. Since companies receive hundreds of applications for each vacancy, you need to stand out from the competition to get an interview. And once you get the interview, you need to demonstrate technical proficiency and communication skills to show that you’re the right person for the job.
That’s why having the right preparation and materials can give you a critical advantage. In their 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 to manage and access the database. It is the most important skill that data scientists need. In addition to accessing data, data professionals use it to run data analysis queries on large amounts of data.
No matter what technical data interview you are preparing for, the Introduction to SQL cheat sheet will be a useful guide for you. It will help you review common syntax and teach you how to use it. Additionally, it will also help you code interviews.
Many data scientists do not use statistical or probability testing in their daily work. It can be difficult to stay up to date with all the important terminologies. However, it’s important to note that you may be asked about concepts like A/B testing, confidence intervals, hypothesis testing, correlation analysis, and more.
If you are afraid of being embarrassed during an interview, you can refresh your memory by referring to the Odds and statistics cheat sheet. Provided by Stanford University, this cheat sheet includes all the essential terminology that can be used during the interview.
Pandas is a Python library mainly used for data cleaning, manipulation, analysis, processing and storage. 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.
He Pandas data dispute The cheat sheet provides byte-sized information on various Pandas features 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 graph to effectively communicate insights is a bit tricky. During interviews, not selecting the optimal graph to display the analysis can create a bad impression on interviewers.
To avoid this pitfall, data scientists should take a look at Data visualization cheat sheet to instinctively select the ideal plot to convey the message you intend to convey to interested parties. This will help you code interviews and take-home tasks.
Scikit-learn is a widely used Python library that offers a wide range 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.
Creating and evaluating machine learning models is a crucial part of a data scientist’s job. It is natural to learn various features of Scikit-learn by reviewing the Scikit-learn cheat sheet for machine learning.
Git is an essential skill for data scientists to master, especially those working in collaborative teams. In any data science project with multiple contributors, Git enables version control and code merging so that team members can simultaneously work on code without runtime conflicts.
You must demonstrate your Git skills before you are invited to work on the project. Therefore, it is essential to review the Git for Data Science cheat sheet to know the most used syntax and functions.
He Super data science The cheat sheet is a little different. You will review it to learn all the important theoretical concepts.
You will learn about:
- Distributions
- Various machine learning concepts.
- Model evaluation
- Linear regression
- Logistic regression
- Decision tree
- Support Vector Machines
- Group
- Dimensionality reduction
- Natural language processing
- Neural networks
- Convolutional neural network
- Recurrent neural network
- Boosting
- Reinforcement learning
- Anomaly detection
- Time series
- Statistics
- A/B testing
With one hour left until the interview, this cheat sheet is all you need to review. It will help you review the most frequently asked interview questions.
I hope you enjoy the list of seven essential cheat sheets. Let me know if you would like to see more similar content.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master’s degree in technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
