Module 2: Advanced Excel for Data Analysis

• Why Excel for Data Analysis?

Excel is a powerful and widely-used tool for organizing, analyzing, and visualizing data. It’s great for beginners and professionals alike due to its flexibility and built-in functions. Excel is one of the most widely used tools for data analysis; it offers a comprehensive set of features for cleaning, transforming, analyzing, and visualizing data. It is especially useful for smaller datasets and is ideal for data exploration.

1. Excel environment setup
2. Install Microsoft excel
3. Set up data sources
4. Enable data analysis tool Pak
5. Key Excel concepts For Data Analysis
6. Data cleaning & transformation
7. Remove duplicates
8. Text to columns
9. Find and replaces
10. Filter & sort
11. Handling missing data
12. Functions & formulas
13. Statistical functions
14. Average
15. Median
16. Mode
17. Stdev
18. Conditional functions.
19. If
20. Count if
21. Sum if
22. Lookup Functions
23. VLOOKUP
24. Index & match
25. Data analysis tools
26. Power query
27. Data validation
28. Data visualization
29. Charts
30. Conditional formatting
31. Sparklines
32. Formulas & functions
33. Nested if
34. Data cleaning
35. Trim
36. Remove duplicates
37. Text to columns
38. Power query
39. Pivot Tables
40. Charts and visualization
41. Dashboards

Module 3: SQL for Data Analysis ( Using SQL Server)

• Why SQL for data analysis?

SQL (Structured Query Language) is essential for working with relational databases. It allows you to extract, filter, group, and analyze large datasets directly from the source.

1. SQL Server Environment Setup
2. Install SQL Server
3. Install SQL Server Management Studio (SSMS)
4. Create a sample Database
5. Create Tables and insert Data
6. Key SQL Concepts
7. Select Queries
8. Aggregate Functions
9. Group BY & Having
10. Joins
11. Subqueries
12. Window Functions (Advanced)

Module 4: Python for Data Analysis

• Why Python for Data Analysis?

Python is a versatile programming language widely used in data analysis due to its simplicity, robust libraries (like Pandas, NumPy, Matplotlib, and Seaborn), and excellent support for statistical analysis and machine learning.

1. Python Environment Setup
2. Install Python
3. Install Anaconda (Optional but Recommended)
4. Install required Libraries
5. Install Jupyter Notebook
6. Key Python Libraries for Data Analysis
7. Pandas
8. Create DataFrames
9. Basic Operations
10. NumPy
11. Create Arrays
12. Array Operations
13. Matplotlib and Seaborn
14. Basic Plot with Matplotlib
15. Seaborn Visualization
16. Exploratory Data Analysis (EDA)
17. Descriptive Statistics
18. Handling Missing Data
19. Data Grouping and Aggregation

Module 5: Power BI for Data Analysis

• Why Power BI for Data Analysis?

Power BI is a powerful business analytics tool that helps to visualize and share insights from your data. It allows users to create interactive reports, dashboards, and visualizations in an easy-to-understand format.

1. Power BI Environment Setup
2. Install Power BI Desktop
3. Create a Power BI Account
4. Load Data into Power BI
5. Key Power BI Concepts
6. Data Transformation (Power Query)
7. Data Modeling
8. DAX (Data Analysis Expressions)
9. Visualizations in Power BI
10. Reports & Dashboards

Module 6: Tableau for Data Analysis

• Why Tableau for Data Analysis?

Tableau is a leading data visualization tool used in the industry for transforming raw data into interactive and meaningful visual insights. It allows users to create interactive dashboards and reports that can be shared with others.

1. Tableau Environment Setup
2. Download and Install Tableau
3. Connect to Data
4. Tableau Public (Optional)
5. Key Tableau Concepts
6. Data Connection & Data Source
7. Data Preparation & Transformation
8. Data Visualization
9. Dashboards & Stories
10. Filters and Parameters

Module 7: R Programming for Data Analysis

• Why R for Data Analysis?

R is a programming language and environment built specifically for statistical computing and data visualization. It is widely used for analyzing large datasets, statistical modeling, and creating high-quality visualizations.

1. R Environment Setup
2. Install R
3. Install RStudio (Recommended IDE)
4. Install Required Libraries
5. Key R Libraries for Data Analysis
6. Dplyr
7. Select Columns
8. Filter Data
9. Summarize Data
10. Tidyr
11. Separate Columns
12. Gather and Spread
13. Ggplot2
14. Basic Plot
15. Bar Plot
16. Exploratory Data Analysis (EDA) in R
17. Summary Statistics
18. Missing Data Handling
19. Data Grouping

Module 8: Machine Learning and AI

1. What is Machine Learning?
2. What is Artificial Intelligence?
3. Application of Machine Learning and AI
4. Introduction to Supervised Learning, Unsupervised Learning
5. Introduction to Linear Regression
6. Linear Regression with Multiple Variables
7. Assumptions of Linear Regression
8. Disadvantages of Linear Models
9. Case Study on Linear Regression Model through Python
10. Case Study on Linear Regression Model through R
11. Interpretation of Model Outputs
12. Introduction to Logistic Regression
13. Why Logistic Regression?
14. Odds Ratio
15. Advantages and Disadvantages of Logistic Regression
16. Case Study on Logistic Regression Model through Python