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Data Preparation and Analysis
A data preparation module built around cleaning, grouping, transforming, and analyzing structured datasets for practical use.
Months 4-5Data Preparation and Numerical ComputingModule 03 of 11
Why This Module Matters
It converts raw datasets into reliable analytical inputs, which is essential before learners move into full machine learning workflows.
Detailed Module Breakdown
- Series and dataframe operations across real tabular datasets
- Missing values, grouping, joins, and transformation workflows
- Descriptive analysis, plotting, and time-series preparation
- Working with CSV, Excel, JSON, and project-style data files
What You Will Study
- Data cleaning, grouping, filtering, and transformation
- Categorical handling, file ingestion, and dataframe workflows
- Time-series basics and exploratory data analysis
Outcomes You Carry Forward
- Prepare messy data for modeling and reporting tasks
- Run exploratory analysis with repeatable analytical steps
- Develop stronger intuition for structured datasets and trends
Module Details
Requirements
- Working familiarity with Python fundamentals
- Willingness to work through messy real-world datasets
Best Suited For
- Students entering data analysis and machine learning preparation
- Learners who need practical data cleaning experience
Delivery Notes
- Module labs center on structured datasets and transformation tasks
- Learners are assessed on clarity, reproducibility, and analytical reasoning
Phase Skills
This phase should build scientific Python capability across arrays, vectorization, data cleaning, tabular analysis, statistics basics, visualization, and time-series preparation.
Vectorized computation and matrix-oriented reasoningData cleaning, transformation, exploratory analysis, and time-series basics
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