Data Science Cheat Sheet

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There are lots of cheat sheets out there of varying quality covering vastly different topics which are all considered to be under the 'data science' banner. Some are great, some are good, many are not worth your time. Occasionally a gem can be found, covering some particular niche to some acceptable level of understanding.

This is where the recent 'Data Science Cheatsheet' by Maverick Lin comes in. It is a relatively broad undertaking at a novice depth of understanding, but it does what it does very well. The 9 page treatment concisely covers such diverse aspects of data science as:

  • stats & probability
  • data preparation
  • feature engineering
  • modeling
  • machine learning
  • deep learning
  • SQL
  • ...and much more

It's worth stressing that this would not be much immediate value to seasoned veterans of data science, but beginners are encouraged to check it out, as are those brushing up for an interview or just looking for some light refresher reading.

You can visit the Github repo for more information, or can download the cheat sheet from this direct download link.

Read and Write to CSV. pd.readcsv('file.csv', header=None, nrows=5). January 11th, 2018 A cheat sheet that covers several ways of getting data into Python: from flat files such as.txts and.csv to files native to other software, such as Excel, SAS, or Matlab, and relational databases such as SQLite & PostgreSQL.

Thanks to Maverick Lin for putting this cheat sheet together, which is an evolving work in progress.


Related:

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.

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.

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

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 Cheat Sheet
  1. R markdown cheatsheet, part 2

Others:

Data Science Cheat Sheet
  1. 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!

  1. SQL for dummies cheat sheet

Cheat sheets for Spark, Scala, Java:

Project

Data Science Cheat Sheet Pandas

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.

  1. Dzone.com’s Apache Spark reference card
  2. DZone.com’s Scala reference card
  3. Openkd.info’s Scala on Spark cheat sheet
  4. Java cheat sheet at MIT.edu
  5. 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.

  1. 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.

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

Cheat sheets for Matlab/Octave

Data Science Cheat Sheet Maverick Lin

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

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