Data Preparation for Analytics
About This Course
This three-day module provides hands-on practice in data preparation using Python. It emphasises the importance of data types, sizes, and encoding, and covers data structuring with arrays and DataFrames, including reshaping, slicing, appending, dropping, transposing, and melting. Participants will learn data cleaning techniques such as imputing missing values, renaming columns, and handling unbalanced datasets. The module also includes data enrichment through table merging and data aggregation with pivot tables. It concludes with a comprehensive project that demonstrates the application of learned skills in a real-world scenario.
What You'll Learn
• Structure data into arrays and DataFrames, performing operations like reshaping, slicing, appending, dropping, transposing, and melting with Python.
• Apply data cleaning techniques such as imputing missing values, renaming columns, and handling unbalanced datasets.
• Enrich data by merging tables.
• Aggregate data using pivot tables and groupby operations with Python.
Entry Requirements
• Participants should preferably have passed mathematics at least ‘O’ Level or equivalent.
• Participants should be conversant with basic IT skills such as software installation, file management and web navigation.
• Participants are encouraged to complete the Foundation of Data Science before enrolling in this course.
• Participants are required to pass a pre-course assessment to ensure participants have the requisite knowledge of Python programming. This assessment can be waived if participants have completed both Fundamentals in Python (Basic) and Fundamentals in Python (Intermediate).