The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have

You know what? While learning data science we tend to write code, learn new things, we try to develop ourselves by learning new algorithms, statistical methods, and Concepts. Most of the time, we use Google to search for various information like code syntax, math formulas, etc. And we use it in our code or in Jupyter notebooks and go straight to the next point, forgetting about its use in the next iteration️. Or We make a cheat sheet to store the information in a compact and concise manner.

Today, we will have a brief review of data science cheat sheets. Along with various tool use, concepts and cheat sheets on best practices. Comprehensive data is lined up next, before that, we should know what we need, right? We should know, data science is a multidisciplinary field, and it has many things to offer. If your new to this journey you should have a look at this Data Science Bootcamp syllabus.

Let’s start!!!

What is Cheat Sheet?

It is a piece of paper tend to refresh or give quick reference intended to aid one’s memory. We tend to use it by, putting formulas, code syntax or concepts on paper for quick scan.

Cheat sheets are a great recourse for quick information about various data science topics, they are best for beginner to experienced data scientists looking for brush-up their skills. When I was in high school and college, I used to make cheat sheet the old-school way, using pen and paper for various hard topics I wanted to learn better. For me It took time, but it was worthy, all information I wanted I had on cheat sheets.

Thanks to internet, now we don’t have to use the old-school method much often. People with designing and representation skills have created many data science cheat sheets in different languages which are more than sufficient for our beginner requirement.

Note: Always add example with concepts, you will not forget concept.

Comprehensive Data science Cheat sheets

Following are topic-wise cheat sheets. Do suggest if you need any specific topic cheat sheet.

1. Probability: It is one of the basic concepts in data science and based on its various methods and concepts are derived like probability theory, probability distributions, types of variables, properties of distributions, etc.
   Link: Probability data science cheat sheet 
2. Statistics: Statistics helps you to analyse data to predict data. Also, patterns and trends in the data are discovered using statistical methods. Also, statistical methods help to discover the value distributions in the data.
   Link: Statistics for data science cheat sheet 
3. Python: It is language of machine learning and computer vision. With great libraries to deal with data science application, it is also very easy to use and understand. Hence, it is very easy to adapt for beginner.
   Link: Python for data science cheat sheet 
4. R: It is beast while dealing with data wrangling. As it aids with many pre-processed modules for data wrangling. Also, well known ggplot2 gives easy visualization to data. Link: R for data science cheat sheet 
5. Machine learning: It is field which tends to aid data understanding, building models that learns various trends and patterns from data to improve the performance of defined task.
   Link: Machine learning cheat sheet 
6. Artificial Neural networks (ANN): ANN are part of machine learning and base of deep learning architectures. ANNs name and structure are based on human brain, considering knowledge signal transfer.
   Link: Neural Networks cheat sheet 
7. PySpark: PySpark is python API for Apache Spark framework. It is combination of Python and Apache Spark. PySpark can manage large amounts of data much quicker than other frameworks like pandas.
   Link: PySpark data science cheat sheet 
8. NumPy: It’s a Python library mostly deals with numeric, large multi-dimensional arrays and matrices. NumPy also supports for high-level math functions to manipulate the arrays and matrices.
   Link: Numpy cheat sheet
9. Algebra: Algebra is one of the important part of data science and machine learning algorithms design and architecture. Various algebraic operations are implemented in algorithms.
   Link: Algebra for data science cheat sheet 
10. Linear algebra: Linear Algebra used in matrix manipulation, data pre-processing, data transformation and model evaluations. Topics you need to familiarise with: Vectors, Matrices, Transpose of a matrix, Inverse of a matrix, Determinant of a matrix, Trace of a matrix, Dot product, Eigenvalues and Eigenvectors.
    Link: Linear Algebra for data science cheat sheet 
11. Calculus: Every model implements basic to advanced calculus in algorithms. One of the well-known examples is Gradient Descent which minimizes an error function bases on the computation of the rate of change.
    Link: Calculus for data science cheat sheet 
12. SciPy : SciPy is known as Scientific Python. An open-source library of python used for scientific computing. SciPy contains packages of linear algebra, integration, interpolation, ODE solvers and signal- image processing which are general equations of science and engineering.
    Link: SciPy for data science cheat sheet 
13. Matplotlib: It is a plotting library for the python language. It is comprehensively used for creating static, animated and interactive visualizations in python. It’s a numerical extension NumPy.
    Link: Matplotlib for data science cheat sheet 
14. Seaborn: Seaborn is a library that uses Matplotlib underneath to plot graphs. It will be used to visualize random distributions. It provides a high-level interface for drawing attractive and informative statistical graphics.
    Link: Seaborn for data science cheat sheet 
15. Keras: Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.
    Link: Keras for data science cheat sheet 
16. Jupyter Notebook: The Jupyter Notebook is an open-source web application that you can use to create and share documents that contain live code, equations, visualizations, and text.
    Link: Jupyter notebook cheat sheet 
17. Bokeh: Bokeh is a python package which deals with interactive visualizations for web browsers. It assists to create graphs and interactive visualization dashboards.
    Link: Bokeh for data science cheat sheet 
18. Pandas: Pandas is a python package used for data manipulation, import-export and analysis. It is mainly used with dataframes for machine learning.
    Link: Pandas for data science cheat sheet 
19. SQL: SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.
    Link: SQL for data science cheat sheet 
20. ggplot2: ggplot2 is an open-source data visualization package for the statistical programming language R. It is a well-known library in R based on the concept of layered grammar of graphics. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties.
    Link: ggplot2 cheat sheet for data science 

Well, these are some best cheat sheets for kickstart to data science journey. If you think you need a helping partner for learning data science and want to know more about bootcamp must follow upGrad Data Science Bootcamp syllabus. 

Benefits of Using Cheat Sheets

A data scientist has to take many decisions based on various statistical knowledge, visualization. Also, he has to deal with data manipulation, aggregation, model building, model evaluation, etc. Keep track of these manipulation, building and evaluation metrics. This way cheat sheet makes life easier in terms of task handling. If you want to know more about data science role and responsibility or more motivated to learn data science have a look at Data Science Courses online.