Skills in Data Analysis

Data Analysis – 3-Month Course Outline

Duration: 12 Weeks (Approx. 120 Hours)
Mode: 5 classes per week, 2 hours per class

Month 1: Foundations of Data Analysis

Week 1: Introduction to Data Analysis

• What is data analysis?
• Types of data: qualitative vs. quantitative
• Data analysis process: collect, clean, analyze, visualize
• Tools overview: Excel, Google Sheets, Python, Power BI

Week 2: Microsoft Excel for Data Analysis – Part 1

• Data entry and formatting
• Basic formulas and functions
• Sorting, filtering, and conditional formatting
• Creating simple charts

Week 3: Microsoft Excel – Part 2 (Intermediate)

• Lookup functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
• PivotTables and PivotCharts
• Data cleaning techniques
• Descriptive statistics in Excel

Week 4: Data Collection and Cleaning

• Sources of data: surveys, APIs, datasets
• Importing data (CSV, Excel, web)
• Identifying and handling missing data
• Data validation and cleaning techniques

Month 2: Applied Data Analysis Tools

Week 5: Introduction to Python for Data Analysis

• Python basics and syntax
• Jupyter Notebooks
• Working with lists, dictionaries, and loops
• Installing and using pandas and numpy

Week 6: Data Analysis with Pandas

• Loading and exploring datasets
• DataFrame operations
• Filtering, grouping, and aggregation
• Merging and joining datasets

Week 7: Data Visualization with Matplotlib & Seaborn

• Plotting line, bar, and pie charts
• Histograms, scatter plots, box plots
• Customizing charts and plots
• Visualization for trends and comparison

Week 8: Introduction to SQL

• Basics of relational databases
• Writing simple queries (SELECT, WHERE)
• Joining tables and filtering
• Aggregate functions (COUNT, AVG, SUM)

Month 3: Advanced Projects & Business Insight

Week 9: Business Intelligence with Power BI

• Power BI interface and workflow
• Connecting to data sources
• Creating dashboards and reports
• Data modeling and DAX basics

Week 10: Data-Driven Decision Making

• Interpreting analysis for business goals
• Presenting findings to non-technical stakeholders
• KPIs and performance metrics
• Case studies (sales, marketing, customer data)

Week 11: Capstone Project Development

• Defining a problem and selecting a dataset
• Performing EDA (Exploratory Data Analysis)
• Creating visualizations and insights
• Drafting a project presentation

Week 12: Capstone Presentation & Career Pathways

• Presenting final project
• Feedback and critique
• Job roles in data analysis
• Resume tips and portfolio building