Statistics and Python are two blades of data science. You should master these two skills if you want to be a data scientist.
This is a Statistics Tutorial via Python and Jupyter notebook. This tutorial makes use of Jupyter notebook to demonstrate how some concepts of statitics are implemented in Python.
- Lesson 1: Mean, Median and Mode
- Lesson 2: Quartiles and Percentiles
- Lesson 3: Standard Deviation and Variance
- Lesson 4: Covariance and Correlation Coefficient
- Lesson 5: Linear Regression
- Lesson 6: Probability Distribution
Each lesson introduces a concept of statistics. In addition to built-in libraries and common libraries such as Pandas and NumPy, it shows plain python codes to illustrate the underlying logics. The coding style is to generalize key function usages such that you can reuse the coding pattern.
In order to view each lesson's notebook, you could view directly here or execute in your local machine. To run in a local machine, some libraries such as Jupyter Notebook should be installed in advance. You could either follow the uploaded requirements.txt by pip install -r requirements.txt or install them one by one:
pip install notebook
pip install numpy
pip install scipy
pip install pandas
pip install matplotlib
pip install seabornTeki Chan tekichan@gmail.com