Data Science Cheatsheets


I took these data science cheat sheets from DataCamp. These cheat sheets really useful especially for everyone who want to learn data science using Python. In this page, I ordered the cheat sheets according to the steps that should be taken to learn data science using Python.

  1. Python Jupyter Notebook:  Jupyter notebook is very helpful. I strongly recommend to use Jupyter notebook. Here the cheat sheet of Jupyter notebook syntax. If you are not familiar with Jupyter Notebook, I have a couple of old posts that may useful for you: 1) setup anaconda 2) understand python libraries for data science.
  2. Basic Python data types: If you are not familiar with data type in Python. You should understand what kind of data types in Python first and how to use those data types (e.g., string, int, float, bool, including how to manipulating list, array, and dictionary). Here the cheat sheet of Basic Python for data science.
  3. Numpy Tutorial: After you understand Python Basic, you can start to learn Numpy, one of the most powerful library in Python to handle big array/matrix. I strongly recommend to understand Numpy first before you go to Pandas/Scipy/or Scikit-learn. Here the Numpy cheat sheet.
  4. Pandas Tutorial: Numpy is very powerful library, but it’s too hard to manipulate our data using this library, especially if our data in the form of table. If you ever use Excel spreadsheet and you feel very easy to manipulate your data on Excel table. Hence, that’s the reason, why Python has Pandas. Pandas library makes you feels very easy to manipulate your data. Here 1) the cheat sheet of Basic Pandas Python, and here 2) for the more advance data manipulation in Pandas (e.g., combine, join, concat, merge, etc).
  5. Import data in Python: How to import data to your python environment using pandas or numpy? Here the cheat sheet of importing data in Python (e.g., from excel, csv, postgre, etc).
  6. SciPy /Linear Algebra Tutorial: Sometimes we forget that Python has very useful library such as SciPy. It seems that If we want to learn data science, we could not escape from machine learning. In fact, we must understand linear algebra to go there. SciPy is linear algebra library in Python. If you want to learn deep learning for example (i.e., image classification), you will deal with large matrix from your image and you need to do many operation on your matrix. That’s why we need SciPy. Here the cheat sheet of SciPy library in Python.
  7. Machine Learning Library in Python: Have you ever heard about linear regression, logistic regression, bayesian classification, SVM, Decision Tree, and any others machine learning algorithms? It’s very easy to call those function to your Python environment. Just import scikit-learn and everything is ready. Here the cheat sheet of Scikit-Learn library Python.
  8. Data Visualization/Plotting in Python: Data visualization is one of the most important part in data analysis. Usually we will plot our result in the form of data visualization to get the insight from our dataset. Python supports a lot of data visualization libraries, the basic one is Matplotlib, here the cheat sheet of matplotlib library. If you need more beautiful or more advanced visualization, you can use 1) Seaborn library (here the cheat sheet of Seaborn library) or 2) Bokeh library (here the cheat sheet of Bokeh library).
  9. Advanced Python: There are three data science cheat sheet for advance python, 1) NLP using Python (here the cheat sheet of NLP Python) ; 2) Deep learning using Keras Python (Keras deep learning cheat sheet); 3) Data Science Python for Business (here the cheat sheet of Data Science for Business).
  10. Summary Data Science CheatSheet: Summary of a lot of things including machine learning, data science, etc. Here.

I hope this page can help everyone who wants to start learning data science using Python.


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