1. Excel
Day 1: Introduction to Excel
for Data Analysis
- Overview of Excel interface
- Basics of navigating and working with sheets
- Introduction to cells, rows, columns, and ranges
- Understanding basic functions (SUM, AVERAGE, COUNT)
- Working with mathematical and statistical functions
- Introduction to text functions for data manipulation
Day 2: Advanced Formulas and
Functions
- Working with logical functions (IF, AND, OR)
- Exploring lookup functions (VLOOKUP, HLOOKUP, INDEX,
MATCH)
- Introduction to array formulas
- Identifying and handling missing data
- Removing duplicates and dealing with errors
- Text-to-columns and data-splitting techniques
- Formatting data for analysis
- Creating basic charts and graphs
- Tips for effective data presentation
- Introduction to PivotTables for dynamic data analysis
- Creating PivotCharts for visual insights
- Customizing and formatting PivotTables and
PivotCharts
- Time-saving shortcuts and productivity hacks
- Excel with AI
2. SQL
Day 1: Introduction to SQL and
Database Fundamentals
- Overview of SQL and its applications
- Introduction to Relational Databases
- Basic SQL syntax and structure
- Creating and modifying tables with CREATE and ALTER
- Understanding data types and constraints
Day 2: Retrieving Data with
SELECT Statements
- Basics of SELECT statements
- Filtering data with WHERE clause
- Sorting results with ORDER BY
3. Advanced SQL Techniques
Day 1: Aggregation and Grouping
- Understanding aggregate functions (SUM, AVG, COUNT)
- Grouping data with GROUP BY
- Working with complex WHERE conditions
- Using operators (AND, OR, NOT, etc)
Day 2: Window Functions and
Analytic Queries
- Introduction to window functions
- Performing analytic queries with OVER clause
4. Advanced SQL
Day 1: Joins and Subqueries
- Performing INNER and OUTER joins
- Using subqueries for complex queries
Day 2: Case Statements and CTE
Queries
- Understanding and using CASE statements in SQL
- Applying CASE statements in data analysis scenarios
- Introduction to Common Table Expressions
- Using CTEs for recursive queries and data
manipulation
5. More on SQL
Day 1: Time-saving Shortcuts
and Productivity Hacks
- Optimization of queries
- Optimization of queries using AI
- Interview-based SQL queries
Day 2: Working on Live Project
- Working on industry-oriented data
- Problem-solving using SQL on industrial data
6. Introduction to Python for Data
Analysis
Day 1: Introduction to Python
and Jupyter Notebooks
- Overview of Python programming language
- Introduction to Jupyter Notebooks for data analysis
- Variables, data types, and basic operations
- Lists, tuples, and dictionaries
- Inbuilt functions
Day 2: Data Manipulation with
Python
- Conditional statements and loops
- User defined functions
- Functions such as map, filter, lambda
7. Exploring Data with Pandas &
Matplotlib
Day 1: Data Manipulation with
Pandas
- Overview of Pandas Library
- Reading and writing data along with basic operations
with Pandas
Day 2: Data Cleaning and
Preprocessing with Pandas
- Handling missing data
- Removing duplicates and dealing with outliers
- Cleaning and adjustments in data
8. DA & Data Visualization
Day 1: Exploratory Data
Analysis (EDA) with Pandas
- Descriptive statistics and data summarization
- Grouping and aggregating data
- SQL-like operation in data
Day 2: Data Visualization with
Matplotlib
- Creating basic plots (line plots, scatter plots,
histograms)
- Customizing and styling visualizations
9. Real-time Python
Day 1: Advanced Data Analysis
with NumPy
- Introduction to NumPy for numerical operations
- Working with arrays and matrices
Day 2: Advanced Data
Visualization with Seaborn
- Creating informative and aesthetically pleasing
visualizations
- Pair plots, heatmaps, and advanced plotting
techniques
10. Statistical Analysis
Day 1: Statistical Analysis
with Scipy
- Introduction to statistical tests and hypothesis
testing
- Implementing statistical tests in Python
- Final Project and Case Studies
- Participants work on a real-world data analysis
project
- Applying learned Python skills to analyze and
visualize data
Day 2: Case Studies and
Discussion & Power BI
- Reviewing case studies of Python usage in data
analysis
- Q&A and discussions on best practices
- Introduction to Power BI
- Understanding the Power BI interface
- Importing data from different sources
- Transforming and shaping data within Power BI
11. Power BI
Day 1: Data Modeling and
Relationships in Power BI
- Creating a data model in Power BI
- Understanding relationships between tables
- Implementing calculated columns and measures
- Using DAX (Data Analysis Expressions) for advanced
calculations
Day 2: Visualizations and
Interactivity
- Creating common visualizations (bar charts, line
charts, etc.)
- Customizing visualizations for better insights
- Adding interactivity to reports and dashboards
- Implementing drill-through actions for detailed
analysis
The Art of Storytelling with
Data
- Principles of Effective Data Storytelling
- Importance of narrative in data presentations
- Building a cohesive narrative in Power BI
- Using bookmarks and storytelling features
12 12. Power BI for Real-Time Analytics and
Advanced Features
Day 1: Real-Time Dashboards
- Setting up real-time data streaming in Power BI
- Creating dashboards for live data monitoring
Day 2: Advanced Features and
Custom Visuals
- Exploring custom visuals and visuals from the
marketplace
- Leveraging advanced features like forecasting and
clustering
Case Studies and Discussion
- Reviewing case studies of effective Power BI usage
- Q&A and discussions on best practices in
storytelling with data