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:
to explore data using Python Pandas library
to rename the data column using different methods
to detect the missing values and outliers in dataset through different methods
to use different methods to fill in the missings and treat the outliers
to make correlation analysis and select the features based on the analysis
to encode the categorical variables with different methods
to split dataset for model training and testing
to normalize data with scaling methods
to develop classic statistical regression models and machine learning regression models
to fit the model, improve the model, evaluate the model and visulize the modelling results, and many more
Who this course is for:
Business analysts
Data analytics professionals
Statisticians
Engineers and scientists for data analysis, modelling and machine learning
Anyone who wants to learn data analysis and modelling with Python for his/her projects
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SKILLS YOU WILL GAIN
Data exploration
Rename the data columns
Data slicing, sorting, filtering, and grouping data
Missing value detection and imputation
WHAT YOU WILL LEARN
Key Python packages required for basic data analysis and modelling
Data analysis process
Data modelling process