Data Analysis

Course Overview

The Data Analysis course is designed to help learners collect, clean, analyze, and visualize data to make meaningful business and research decisions. The course covers essential tools, techniques, and real-world applications of data analysis using spreadsheets, programming languages, databases, and visualization tools. It is ideal for students, graduates, working professionals, and aspiring data analysts.

Module 1: Introduction to Data Analysis

• What is data analysis?
• Importance of data in decision making
• Types of data (structured & unstructured)
• Quantitative vs qualitative data
• Data analysis lifecycle
• Roles and responsibilities of a data analyst
• Career opportunities in data analysis

Module 2: Data Fundamentals & Statistics

• Understanding datasets
• Population and sample
• Mean, median, and mode
• Range, variance, and standard deviation
• Probability basics
• Correlation and regression overview
• Data distributions

Module 3: Microsoft Excel for Data Analysis

• Excel interface and basics
• Data entry and formatting
• Sorting and filtering data
• Excel formulas and functions
  • SUM, AVERAGE, COUNT
  • IF, VLOOKUP, HLOOKUP, XLOOKUP
• Conditional formatting
• Pivot tables and pivot charts
• Data cleaning using Excel

Module 4: SQL for Data Analysis

• Introduction to databases
• What is SQL?
• Database concepts (tables, rows, columns)
• Writing SQL queries
  • SELECT, WHERE, ORDER BY
  • GROUP BY, HAVING
• Joins (INNER, LEFT, RIGHT)
• Subqueries
• Data extraction and analysis using SQL

Module 5: Python for Data Analysis

• Introduction to Python
• Python installation and environment setup
• Python basics (variables, loops, functions)
• Data analysis libraries
  • NumPy
  • Pandas
• Data manipulation and cleaning
• Handling missing values
• Exploratory Data Analysis (EDA)

Module 6: Data Visualization

• Importance of data visualization
• Visualization principles
• Charts and graphs
• Data visualization using Excel
• Data visualization using Python (Matplotlib, Seaborn)
• Creating dashboards
• Storytelling with data

Module 7: Statistics for Data Analysis

• Descriptive statistics
• Inferential statistics
• Hypothesis testing
• Confidence intervals
• A/B testing basics
• Statistical interpretation of results

Module 8: Power BI / Tableau

• Introduction to BI tools
• Power BI / Tableau interface
• Connecting data sources
• Data modeling
• Creating interactive dashboards
• DAX basics (Power BI)
• Sharing reports and dashboards

Module 9: Data Cleaning & Preprocessing

• Data quality issues
• Handling missing and duplicate data
• Outlier detection
• Data normalization and transformation
• Preparing data for analysis

Module 10: Business & Real-World Applications

• Data analysis in business
• Sales and marketing analysis
• Financial data analysis
• Customer behavior analysis
• HR analytics basics
• Case studies