The Complete Collection of Data Science Cheat Sheets – Part 1
A collection of cheat sheets that will help you prepare for a technical interview, assessment tests, class presentation, and help you revise core data science concepts.
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Editor's note: For the full scope of cheat sheets included in this 2 part series, please see The Complete Collection of Data Science Cheat Sheets - Part 2.
Cheat sheets can help us revise the concepts of statistics, programming language syntax, data analytics tools, and machine learning frameworks. It can also help you ace technical interviews and assessment tests. Jupyter Notebook is the essential cheat sheet that everyone should learn. It contains shortcuts, tricks, and functions for running a Python notebook.
I use cheat sheets to prepare for technical interviews, as tech recruiters want to assess the subject matter expertise. Searching for cheat sheets that work for you can take hours as most of them are not easy to comprehend. The blogs are divided into two parts that include easy-to-follow and summarized sheet cheats to revise all the concepts of data sciences.
The two part series is further divided into subcategories; SQL, Web Scraping, Statistics, Data Analytics, Business Intelligence, Big Data, Data Structures & Algorithms, Machine Learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks, and VIP cheat sheets.
The first blog consist of six subcategories:
- Web Scraping
- Statistics, Probability, & Math
- Data Analytics
- Business Intelligence
- Big Data
Majority of technical interviews and assessment tests include some type of SQL questions so, it is better to prepare for the interview using the collection of SQL cheat sheets. These cheat sheets will also help you get better at creating and managing databases. It will also help you understand complex SQL queries.
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Web Scraping is an essential part of data science, as it is used for gathering data, market research, and maintaining data pipelines. Beautiful Soup is a popular library for parsing HTML/Java scripts and converting them into human-readable dataframe. The section consist of tools that are used to parse scripts in Python and R.
Statistics, Probability, & Math
Artificial intelligence, data analytics, and modern research depend on statistics. It is the backbone of our modern society, so if you want to review old concepts or learn new complex ideas then check out a collection of statistical cheat sheets.
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- William Chen's Probability Cheatsheet 2.0
- Stanford: Algebra and Calculus
- Statistics, Probability & Math
- MIT: Statistics
- Stanford: Statistics
- Calculus for Machine Learning
- Linear algebra for deep learning
- SciPy: Linear Algebra in Python
Data analytics is used for making business decisions, marketing campaigns, scientific research, and designing unique data products. Entire IT industry depends on it. This category is further divided into three subcategories; Python, R, Julia. All of these languages are popular among data scientists and data analysts.
The list contains the most used Python packages from data ingestion, manipulation, and visualization. Numpy and Pandas are the most popular tools among the data community for performing scientific calculation and data augmentation.
- Python For Data Science Cheat Sheet For Beginners
- Pandas for Data Science
- Pandas: Data Wrangling
- Python Seaborn
- Data Visualization: Bokeh
- Importing Data
R is quite famous among statisticians and data analytics professionals. It is recommended to learn syntax and functions of famous Packages such as Tidyverse. The Tidyverse contains a complete data science solution from importing data to creating visually simulating data reports.
- Python with R and reticulate
- Tidyverse For Beginners
- Data visualization with ggplot2
- Data transformation with dplyr
- Data tidying with tidyr
- Data import with readr, readxl, and googlesheets4
- Apply functions with purrr
- Factors with forcats
- Dates and times with lubridate
- Dynamic documents with rmarkdown
- Advanced R
- The data.table R Package
- xts Cheat Sheet: Time Series in R
Julia is an emerging language and in my opinion, this is the future of data science. The list contains a quick introduction of Julia syntax, data wrangling, and data visualization.
- Fast Track to Julia
- Data Wrangling with DataFrames.jl
- MATLAB Vs. Python Vs. Julia
- Make.jl Examples
No code applications for Business Intelligence are becoming industry standards. These applications can help you create data analytical reports, dashboards, and immersive visualization. These tools are helping businesses make data-driven decisions. The most popular tools are MS Excel, Power BI, and Tableau.
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By 2025, it is estimated that 463 exabytes of data will be created each day globally - (weforum.org). With that, major data companies are looking for data engineers and data scientists to work on big data solutions. This collection of cheat sheets can give you an introduction to the essential big data tools.
In this blog, we have covered all the cheat sheets that will help you prepare for data analytics, business intelligence, and data science interviews. The blog includes the collection of SQL, Web Scraping, Statistics, Data Analytics, Business Intelligence, and Big data cheat sheets. The cheat sheet has helped me in preparing for job interviews and I hope it can help you too. It is wise to bookmark this page, so whenever you have a technical interview, you can start preparing immediately instead of searching cheat sheets online.
In the second part, we will cover more advanced categories like, Data Structures & Algorithms, Machine learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks.
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.