Become a Professional Python Data Analyst

This module introduces learners to data analysis and how Python is used to work with real datasets.

• Meaning of data analysis
• Why Python is useful for data analysis
• Data analysis workflow
• Types of data and data sources
• Understanding datasets, rows, columns, and variables
• Career opportunities in Python data analysis

Learners will set up the tools needed to write Python code and work with data projects.

• Installing Python and required tools
• Working with code editors
• Introduction to Jupyter Notebook
• Using Google Colab for data analysis
• Creating and organizing project folders
• Running Python scripts and notebooks

This module teaches the basic Python programming concepts needed for data analysis.

• Variables and data types
• Numbers, strings, lists, tuples, and dictionaries
• Operators and expressions
• Conditional statements
• Loops and reusable logic
• Functions and clean coding practice

Learners will understand how to import, read, and manage data from common file sources.

• Reading CSV files
• Reading Excel files
• Understanding file paths
• Previewing datasets
• Exporting cleaned data
• Organizing raw and processed datasets

This module introduces NumPy and how it helps with numerical calculations and data operations.

• Introduction to NumPy
• Arrays and array operations
• Basic mathematical operations
• Filtering and selecting data
• Working with numerical datasets
• Understanding how NumPy supports pandas and analysis

Learners will use pandas to load, inspect, clean, transform, and analyze datasets.

• Introduction to pandas
• Series and DataFrames
• Loading datasets into pandas
• Selecting rows and columns
• Filtering and sorting data
• Creating new calculated columns

This module focuses on preparing messy datasets for analysis.

• Checking missing values
• Handling duplicates
• Fixing inconsistent text values
• Working with dates and numbers
• Renaming columns and restructuring data
• Preparing clean datasets for reporting

Learners will explore datasets to find patterns, trends, relationships, and useful insights.

• Understanding exploratory data analysis
• Summary statistics
• Grouping and aggregation
• Finding patterns and trends
• Comparing categories and segments
• Answering business questions with data

This module teaches learners how to create charts and visual reports from data.

• Introduction to data visualization
• Using Matplotlib for charts
• Creating bar charts, line charts, and pie charts
• Using Seaborn for statistical visuals
• Choosing the right chart for the right data
• Designing clear and readable visualizations

Learners will understand how to summarize analysis results and present useful business reports.

• Creating summary tables
• Exporting reports to Excel and CSV
• Combining charts and insights
• Writing data interpretation notes
• Building simple business analysis reports
• Presenting findings clearly

This module introduces learners to using Python to reduce repetitive data tasks.

• Automating repeated cleaning tasks
• Combining multiple files
• Generating regular reports
• Creating reusable analysis scripts
• Saving outputs automatically
• Improving speed and accuracy in data work

Learners will be introduced to the basic ideas that connect data analysis with data science.

• Difference between data analysis and data science
• Basic statistics for data work
• Correlation and relationships
• Introduction to prediction concepts
• Machine learning awareness
• Preparing for advanced data science learning

At the end of the course, learners will complete practical Python data analysis projects for their portfolio.

• Sales data analysis project
• Customer data analysis project
• Excel to Python automation project
• Visualization and reporting project
• Project presentation and review
• Portfolio and career guidance