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A Real-World Project using Jupyter notebook, Numpy, SciPy, Pandas, Matplotlib, Statmodels, Scikit-learn, and many more
Updated on Sep, 2026
Programming, Data Science, Python
Training 5 or more people ?
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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
Check out the detailed breakdown of what’s inside the course
Nearly 20 years of research and teaching experience and 10 years of entrepreneur and management experience in computer modelling and simulation, big data analysis, machine learning algorithms; Ph.D. in Environment and Resource Management; Postdoctoral scientist, and Ph.D. supervisor in Environmental System Modelling; Research associate and Visiting scientist in Forest Hydrological Ecosystem Modelling; Industrial Professor, Adjunct Professor teaching AI and machine learning courses and Postgraduate supervisor in Deep reinforcement learning and Computer vision; Senior Research in R&D of real time monitoring and early warning system platform for water protection, human safety and health; Participated in or hodeling 12 international research projects; Participated as an invited key speaker in 12 scientific conferences and workshops; Having 27 software copyrights, 4 patents and over 40 pulications.
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