Data Science Models Cheat Sheet

This post updates a previous very popular post 50+ Data Science, Machine Learning Cheat Sheets by Bhavya Geethika. If we missed some popular cheat sheets, add them in the comments below.

Cheatsheets on Python, R and Numpy, Scipy, Pandas

Data science is a multi-disciplinary field. Thus, there are thousands of packages and hundreds of programming functions out there in the data science world! An aspiring data enthusiast need not know all. A cheat sheet or reference card is a compilation of mostly used commands to help you learn that language’s syntax at a faster rate. Here are the most important ones that have been brainstormed and captured in a few compact pages.

Mastering Data science involves understanding of statistics, mathematics, programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding & a human instinct—that drives decisions.

The Most Comprehensive Cheat Sheet. This one is from the pandas guys, so it makes sense that. Cheat Sheet for Machine Learning Models. M.S.E Data Science @Johns Hopkins University with a B.S. In Applied Mathematics. Previously @MongoDB, current Data Science Intern @EA.

Here are the cheat sheets by category:

Cheat sheets for Python:

Python is a popular choice for beginners, yet still powerful enough to back some of the world’s most popular products and applications. It's design makes the programming experience feel almost as natural as writing in English. Python basics or Python Debugger cheat sheets for beginners covers important syntax to get started. Community-provided libraries such as numpy, scipy, sci-kit and pandas are highly relied on and the NumPy/SciPy/Pandas Cheat Sheet provides a quick refresher to these.

Python Cheat Sheet by DaveChild via cheatography.com
Python Basics Reference sheet via cogsci.rpi.edu
OverAPI.com Python cheatsheet
Python 3 Cheat Sheet by Laurent Pointal

Data Science Models Cheat Sheet Free

Cheat sheets for R:

The R's ecosystem has been expanding so much that a lot of referencing is needed. The R Reference Card covers most of the R world in few pages. The Rstudio has also published a series of cheat sheets to make it easier for the R community. The data visualization with ggplot2 seems to be a favorite as it helps when you are working on creating graphs of your results.

At cran.r-project.org:

At Rstudio.com:

Data Science Models Cheat Sheets

R markdown cheatsheet, part 2

Others:

DataCamp’s Data Analysis the data.table way

Cheat sheets for MySQL & SQL:

For a data scientist basics of SQL are as important as any other language as well. Both PIG and Hive Query Language are closely associated with SQL- the original Structured Query Language. SQL cheatsheets provide a 5 minute quick guide to learning it and then you may explore Hive & MySQL!

SQL for dummies cheat sheet

Cheat sheets for Spark, Scala, Java:

Apache Spark is an engine for large-scale data processing. For certain applications, such as iterative machine learning, Spark can be up to 100x faster than Hadoop (using MapReduce). The essentials of Apache Spark cheatsheet explains its place in the big data ecosystem, walks through setup and creation of a basic Spark application, and explains commonly used actions and operations.

Dzone.com’s Apache Spark reference card
DZone.com’s Scala reference card
Openkd.info’s Scala on Spark cheat sheet
Java cheat sheet at MIT.edu
Cheat Sheets for Java at Princeton.edu

Cheat sheets for Hadoop & Hive:

Hadoop emerged as an untraditional tool to solve what was thought to be unsolvable by providing an open source software framework for the parallel processing of massive amounts of data. Explore the Hadoop cheatsheets to find out Useful commands when using Hadoop on the command line. A combination of SQL & Hive functions is another one to check out.

Cheat sheets for web application framework Django:

Django is a free and open source web application framework, written in Python. If you are new to Django, you can go over these cheatsheets and brainstorm quick concepts and dive in each one to a deeper level.

Django cheat sheet part 1, part 2, part 3, part 4

Cheat sheets for Machine learning:

We often find ourselves spending time thinking which algorithm is best? And then go back to our big books for reference! These cheat sheets gives an idea about both the nature of your data and the problem you're working to address, and then suggests an algorithm for you to try.

Machine Learning cheat sheet at scikit-learn.org
Scikit-Learn Cheat Sheet: Python Machine Learning from yhat (added by GP)
Patterns for Predictive Learning cheat sheet at Dzone.com
Equations and tricks Machine Learning cheat sheet at Github.com
Supervised learning superstitions cheatsheet at Github.com

Cheat sheets for Matlab/Octave

MATLAB (MATrix LABoratory) was developed by MathWorks in 1984. Matlab d has been the most popular language for numeric computation used in academia. It is suitable for tackling basically every possible science and engineering task with several highly optimized toolboxes. MATLAB is not an open-sourced tool however there is an alternative free GNU Octave re-implementation that follows the same syntactic rules so that most of coding is compatible to MATLAB.

Cheat sheets for Cross Reference between languages

Related:

At DataCamp, we always look out for ways to help our students, who are all eager to become more data savvy, reach their objectives even faster. That’s why we recently created a series of Python cheat sheets that target people who are using it for data analysis. The ongoing series already covers some of the most important and fundamental topics in data science and are must-haves for anyone that wants to get started with Python for data science.

And if you haven’t yet, you should consider learning this programming language. Year after year, Python’s popularity is increasing in the data science industry. The use of Python as a data science tool has been on the rise over the past few years: 54% of the respondents of the latest O'Reilly Data Science Salary Survey indicated that they used Python. The results of the 2015 survey showed that 51% of the respondents used Python.

Nobody can deny that Python has been on the rise in the data science industry and it certainly seems that it's here to stay.

So why not start now and make sure that the first steps you take count?

Get a copy of Python for data science cheat sheet and go through DataCamp’s Intro to Python for Data Science course. You’ll cover topics such as variables and data types, strings, lists, the basics of NumPy arrays, and much more. Complete your Python basics with an interactive Python List tutorial, to practice using this built-in data structure in Python for data analysis.

After, it’s time to lay the foundation for learning other data science libraries and dig deeper into (part of) the fundaments of the Pandas and Scikit-Learn libraries: take a look at NumPy, the Python scientific computing library that is excellent for data analysis. You’ll see that this library provides you with an array data structure that is a great alternative to Python lists: it is more compact, allows faster access when you’re reading and writing items, and is more convenient and more efficient overall.

The NumPy cheat sheet will introduce you to array creation, array mathematics, selecting elements (through subsetting, slicing and indexing), array manipulation and much more!

Make sure to use the reference sheet when you’re practicing arrays with DataCamp’s Python NumPy Tutorial or when you go through the Intro to Python for Data Science course. Undoubtedly, you’ll take your first steps with NumPy with confidence!

When you have mastered the basics, it’s time to get your hands dirty and analyze some real-life data. But you cannot start without the Pandas library: it’s all you ever need and want to use if you want to do data manipulation and analysis in Python.

But don’t go in unprepared: take DataCamp’s Pandas Foundations and Manipulating DataFrames with Pandas courses and make sure to keep the Pandas cheat sheet handy when you’re starting the Pandas DataFrame tutorial, where you can get extra practice to use this fast, flexible and expressive data structure.

Just like the tutorial, the cheat sheet not only gives basic information about the Pandas data structures and how to select values or basic statistics from them, but also shows you how inputting and outputting of data, sorting and ranking the data in your DataFrame or Series and data alignment works.

Data Science Models Cheat Sheet 2020

After you have already explored your data with some summary statistics on your DataFrame and manipulated your data in such a way that it’s ready for further analysis, it’s time to visualize your data!

Data Mining Cheat Sheet

The Bokeh library is the one that you need quickly and easily create interactive plots, dashboards, and data applications. What’s more, Bokeh enables high-performance visual presentations of large data sets in modern web browsers!

This Python visualization library is a powerful tool for your data science toolbox, so why not get started straight away?

First, get a copy of our Bokeh cheat sheet: it will make you familiar with the steps you need to go through to plotting and creating statistical charts. It summarizes how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. Also, the creation of basic statistical charts will hold no secrets for you any longer.

But don’t just sit around and look at the cheat sheet: take the Interactive Data Visualization with Bokeh course and get the practice you need to become a data viz wizard in no time!

After exploring your data, you’ll have even more detailed research questions. Here’s where modeling your data gets important if you want to find a solid answer for them.

Machine learning is essential to data science; And everybody that says “machine learning” and “Python” in the same sentence, knows that Scikit-Learn is the way to go for machine learning in Python. This library implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.

However, starting to tackle machine learning problems can be a pain: you don’t necessarily know where to start and how to go about it. That’s why the Scikit-Learn cheat sheet is a perfect companion to your first steps with Scikit-Learn: you'll not only see how to load in your data and how to preprocess it, but you’ll also see how to create your own model to which you can fit your data and predict target labels. Validation and tuning of your models to improve performance are also included in the reference sheet. Keep it handy while you’re going through our Scikit-Learn tutorial with character recognition as a topic.

About DataCamp

DataCamp is an online interactive education platform that focuses on building the best learning experience specifically for Data Science.

Anaconda Perspectives

Data Scientists: Bring the Narrative to the ForefrontRead More

Anaconda Perspectives

There Is No Data – Only Frozen ModelsRead More

Data Science Models Cheat Sheet Answer

Anaconda Perspectives

Why Organizations Should Invest in a Chief Data Officer