Master Python Data Analysis and Modelling Essentials

A Real-World Project using Jupyter notebook, Numpy, SciPy, Pandas, Matplotlib, Statmodels, Scikit-learn, and many more

  Shouke Wei

   Data Analysis, Development, Data Science and AI ML

  Language - English

   Published on 03/2022



We are living in data explosive world where data is ubiquitous, and thus it is essential to build data analysis and modelling skills.  Based on TIOBE Index, Python has overpassed Java and C and become the most popular programming language of today  since October 2021. Python leads the top Data Science and Machine Learning platforms based on KDnuggets poll. 

This course  uses a real world project and dataset and well known Python libraries to show you how to explore data, find the problems and fix them, and how to develop classic statistical regression models and machine learning regression step by step in an easily undrstand way. This course is espeically suitable for beginner and intermediate leverls, but many of the methods are also very helful for the advanced learners. After this course, you will own the skills to:

(1) to explore data using Python Pandas library 

(2) to rename the data column using different  methods

(3) to detect the missing values and outliers in dataset through different methods

(4) to use different methods to fill in the missings and treat the outliers

(5) to make correlation analysis and select the features based on the analysis

(6) to encode the categorical variables with different methods

(7) to split dataset for model training and testing

(8) to normalize data with scaling methods

(9) to develop  classic statistical regression models and machine learning regression models

(10) to fit the model, improve the model, evaluate the model and visulize the modelling results, and many more

What Will I Get ?

  • Data analysis and modelling process
  • Setting up Python data analysis and modelling environment
  • Data exploration
  • Rename the data columns
  • Data slicing, sorting, filtering, and grouping data
  • Missing value detection and imputation
  • Outlier detection and treatment
  • Correlation Analysis and feature selection
  • Splitting data set for model fitting and testing
  • Data normalization with different methods
  • Developing a classic statistical linear regression model
  • Developing a machine linear regression model
  • Interpreting the model results
  • Improving the models
  • Evaluating the models
  • Visualizing the model results


  • Basic Python language knowledge needed to understand the codes
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