If you need information about any topic — tech-related or otherwise — what do you do? Like most people, you head to Google and you find thousands of materials, articles, books and videos about your topic. Although this easy access to information allows people around the world to learn new skills, start new careers and explore topics from the comfort of their homes, the glut of information can be overwhelming. Cheat sheets to the rescue!
Cheat sheets are an amazing resource for shortcut information about various data science topics. They’re great for experienced data scientists looking to brush-up their skills but especially useful for those entering the field or diving into a new topic.
When I was in high school, undergrad and even my postgrad studies, I used to make cheat sheets the old fashioned way — with a pen and a paper — for any topic I wanted to understand better. That took a long time, but in the end, it was worth it because I had all the basic information at my fingertips.
Luckily for me (and you!), the good people of the internet with a much better sense of design created these cheat sheets for different data science topics. These are some of my favorites and they’ve helped me a lot in my career.
Data Science Cheat Sheet Topics
- Probabilty
- Statistics
- SQL
- Pandas
- Data Visualization
- Matplotlib
- Machine Learning
- Natural Language Processing
- Jupyter Notebooks
1. Probability
The core of data science is math and, in particular, probability theory. When analyzing data you’ve collected, the data often follows one of the most commonly used probability distributions. Knowing how these distributions look, their characteristics, properties and what they mean is essential for every data scientist.
You need to know what random variables are, calculate the main properties of any distribution, and how to tell the difference between them. This comprehensive 10-page cheat sheet contains a semester’s worth of materials and covers all the basics of probability theory.
2. Statistics
As a field, data science collects and analyzes data to predict future data and events using statistics. As a result, data scientists can help businesses find trends, patterns, decide what strategies are working/not working and what their users want. But what if statistics isn’t in your wheelhouse? Stanford University created this webpage-like cheat sheet that clearly and concisely covers the basics of statistics.
3. SQL
You can’t spell data science without “data.” As data scientists, our job is to try and figure out the story our data is trying to tell and then use that story to make predictions about future events. The data we need to collect and analyze is almost always stored in some sort of a database.
This means you’ll often need to interact with (and extract information from) a database to collect your data. The language to interact with databases is SQL. This SQL cheat sheet covers the language basics clearly and will help you understand how databases store and handle data.
4. Pandas
When most people decide to get started with data science, they do so using Python. The main library used to analyze, explore, manipulate and clean data is the mother of all libraries: PandasLiterally, there's no data science code written in Python that doesn’t have “import pandas as pd” at the top.
Pandas works based on a data type called a data frame. You’ll often find yourself repeating the same steps for every new data science project you start. This amazing Pandas cheat sheet from DataCamp will help you understand the fundamentals of Pandas and how to use the library efficiently.
5. Data Visualization
Perfecting your data visualization is an essential skill for all data scientists. You might think data visualization is all about presenting your findings and results but that’s only part of it. Data visualization is also a tool to explore your data to find patterns or trends within it.
The most important question you may ask yourself before you visualize any data set is: what chart do I use? Or when do I use [insert chart name here]? This cheat sheet helps you understand the differences between charts, when to use them and how to create them efficiently.
6. Matplotlib
Speaking of visualization, if you ever learned how to design and create your own visualization in Python, then you, for sure, came across Matplotlib. Matplotlib is to data visualization as Pandas is to data analysis. It’s a robust library that allows you to create various types of visualization with ease.
DataCamp has created an awesome, straightforward cheat sheet for the different functions within Matplotlib and how to use them correctly.
7. Machine Learning
Machine learning is one of the main branches of data science; it then branches off into other subfields like deep learning and natural language processing. It seems complex, but machine learning comes down to a few basic concepts. If you know them, you can easily handle any machine learning application.
Stanford University has created a comprehensive machine learning cheat sheet that contains sub-cheat sheets for supervised learning, unsupervised learning, model metrics and deep learning.
8. Natural Language Processing
Natural language processing (NLP) is arguably the most popular branch of data science. It deals with enabling computer programs to understand and comprehend natural languages. NLP is the technology that enables many of today’s advanced technologies like automatic translators and virtual assistants.
For NLP, I have two cheat sheets for you: The first one covers NLP's basic terminology and techniques. The second focuses on how to apply NLP techniques using Python and NLTK.
9. Jupyter Notebooks
If you ever looked for specific data science tutorials, you mostly found that the programmer implemented their code using Jupyter Notebooks. Jupyter Notebooks are great for building various computer science applications and sharing your code with others. It can contain code, text, visualization all in the same place.
This Jupyter Notebook cheat sheet will help you get your development environment ready to start building projects in no time.
The Takeaway
As a professional field, data science has exploded in popularity and resources in recent years. Getting into data science can be a bit overwhelming, but if you know what you need to learn, these cheat sheets can help you truly understand the foundational principles of your project. Throughout my education, these cheat sheets helped me a lot (and still do if I want a refresher).
Sometimes having too much information can be a bad thing. Drowning in search engine results is confusing and frustrating, especially for those who are just getting started in data science. Hopefully these cheat sheets can save you from the glut of information and help you on your way to becoming a proficient data scientist.
