We are living in a 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 understand way. This course is especially suitable for beginner and intermediate levels, but many of the methods are also very helpful for the advanced learners.
What Will You Learn?
- 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
Download Courses Notes04:56
Introduction to Data Analysis and Modelling05:41