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.
To excel data analysis/data science/machine learningin Python, Pandasis a library you need to master. Here is a cheat sheetof some of the most used syntax that you probably don’t want to miss. The Pandas package is the most imperativetool in Data Science and Analysis working in Python nowadays. Contribute to dinskutty/Data-Science-Cheat-Sheet development by creating an account on GitHub.
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.
- We’ve collated a collection of cheat sheets for you to get to grips with the main libraries used in data science. They are grouped into the fields for which each library is designed: Basics, Databases, Data Manipulation, Data Visualization, Analysis, Machine Learning.
- Data Science Cheat Sheets Quick help to make a data scientist's life easier.
- Data Science Cheet Sheet Data science is a concept to unify statistics, data analysis, machine learning, domain knowledge and their related methods in order to.
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
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.
- R markdown cheatsheet, part 2
- 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
Python Data Science Cheat Sheet
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
R For Data Science Cheat Sheet
The Microsoft Azure Machine Learning automated data pipeline cheat sheet helps you navigate through thetechnology you can use to get your data to your Machine Learning web service where it can be scored by your predictive analytics model.
Depending on whether your data is on-premises, in the cloud, or real-time streaming, there are different mechanisms available to move the data to your web service endpoint for scoring.This cheat sheet walks you through the decisions you need to make, and it offers links to articles that can help you develop your solution.
Download the Machine Learning automated data pipeline cheat sheet
Once you download the cheat sheet, you can print it in tabloid size (11 x 17 in.).
Download the cheat sheet here: Microsoft Azure Machine Learning automated data pipeline cheat sheet
More help with Machine Learning Studio
- For an overview of Microsoft Azure Machine Learning, see Introduction to machine learning on Microsoft Azure.
- For an explanation of how to deploy a scoring web service, see Deploy an Azure Machine Learning web service.
- For a discussion of how to consume a scoring web service, see How to consume an Azure Machine Learning Web service.