Data Analytics Masters - 2025 Edition

The most intense Data Analytics Course of 2025 with detailed teaching.

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Course curriculum

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1. Welcome
2. Course Resources
3. Support
1. Understanding Generative AI Basics
2. Introduction
  FREE PREVIEW
3. Introduction to Generative AI
4. Introduction to AI Tools (ChatGPT, Grok)
5. Prompt Engineering Basics
6. Applications of Generative AI
7. Ethical Considerations in AI
1. Welcome Page - Free Preview
  FREE PREVIEW
2. Welcome to the Course!!
3. Join our Community!!
4. What is Data Analytics
5. Importance of Data Analytics
6. Types of Data
  FREE PREVIEW
7. Types of Statistical Analysis
8. Steps to obtain a Data Analytics solution
9. 1 Business Understanding
10. 2 Data Understanding
11. 3 Data Collection
12. 4 Data Preparation
13. 5 Data Modelling
14. 6 Deployment
15. Use Case
1. Lets install Python together
  FREE PREVIEW
2. Google Colab, what's that?
3. Let's get familiar with ChatGPT
1. Course Contents
2. Python Introduction
  FREE PREVIEW
3. Variables & Keywords
4. Datatypes & Operators
5. Data Structures - Lists
6. Data Structures - Tuples
7. Data Structures - Sets
8. Data Structures - Dictionary
9. Loops & Iteration
10. Functions in Python
11. File Handling
12. Map Reduce Filter
13. OOPS Concepts
14. Control Structures in Python
15. Python Quiz #1
16. NumPy
17. Pandas
18. Data Visualization
19. Matplotlib
20. Seaborn
21. Python Quiz #2
1. Course Contents
2. Agenda
3. Data Analytics/Science Process
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4. What is EDA
5. Visualization
6. Steps involved in EDA (Data Sourcing)
7. Steps involved in EDA (Data Cleaning)
8. Handle Missing Values (Theory)
9. Handle Missing Values (Code)
10. Feature Scaling
11. Feature Scaling (Standardization)
12. Feature Scaling (Normalization)
13. Feature Scaling (Code)
14. Outlier Treatment (Theory)
15. Outlier Treatment (Code)
16. Invalid Data
17. Types of Data
18. Types of Analysis
19. Univariate Analysis
20. Bivariate Analysis
21. Multivariate Analysis
22. Numerical Analysis
23. Analysis (Code)
24. Derived Metrics
25. Feature Binning (Theory)
26. Feature Binning (Theory)
27. Feature Binning (Code)
28. Feature Encoding
29. Feature Encoding Detailed
30. Feature Encoding (Code)
31. Case Study
32. Data Exploration (Case Study)
33. Data Cleaning (Case Study)
34. Univariate Analysis (Case Study)
35. Bivariate Analysis (Case Study)
36. Bivariate Analysis (Case Study)
37. EDA Report (Case Study)
38. EDA Quiz
1. Course Contents
2. Intro to Stats
3. Chapter 1 - Agenda
4. Descriptive Stats
5. Inferential Stats
6. Qualitative Data
7. Quantitative Data
8. Chapter 2 - Agenda
9. Population vs Sample
10. Why sampling is important?
11. Types of sampling
12. Probability Sampling
13. Cluster Random Sampling
14. Non probability sampling
15. Chapter 3 - Agenda
16. Measures of Central Tendency
17. Mean
18. Median
19. Mode
20. Measures of Dispersion
21. Range
22. IQR
23. Mean Deviation
24. Variance & Standard Deviation
25. Why n-1 and not n
26. Chapter 4 - Agenda
27. Probability
28. Addition Rule
29. Independent Events
30. Cumulative Probability
31. Conditional Probability
32. Bayes Theorem Part 1
33. Bayes Theorem Part 2
34. Chapter 5 - Agenda
35. Uniform Distribution
36. Binomial Distribution
37. Poisson Distribution
38. Normal Distribution Part 1
39. Normal Distribution Part 2
40. Skewness
41. Kurtosis
42. Calculating Probability with Z-Score
43. Z-Score Calculation Table
44. Example
45. Chapter 6 Agenda
46. Correlation vs Covariance
47. Covariance
48. Correlation
49. Chapter 7 - Agenda
50. Hypothesis Testing
51. Tailed Tests
52. What is p-value?
53. Types of Tests
54. T Test
55. Z Test
56. ANOVA
57. Chi Square
58. Correlation
59. Statistcs Quiz
1. Course Contents
2. Installation of MySQL Workbench
3. Data Architecture - File server vs Client server
4. Introduction to SQL
5. Constraints in SQL
6. Table Basics - DDLs
7. Table Basics - DQLs
8. Table Basics - DMLs
9. Joins in SQL
10. Data Import & Export
11. Aggregation Functions
12. String Functions
13. Date Time Functions
14. Regular Expressions
15. Nested Queries
16. Views
17. Stored Procedures
18. Windows Function
19. SQL-Python connectivity
20. SQL Quiz
1. Course Contents
2. Pre-defined Functions
3. Datetime Functions
4. String Functions
5. Mathematical Functions
6. Lookup (Hlookup,Vlookup)
7. Logical & Error Functions
8. Statistical Functions
9. Images in Excel
10. Excel Formatting
11. Custom Formatting
12. Conditional Formatting
13. Charts in Excel
14. Data Analysis using Excel
15. Pivot Tables
16. Dashboarding in Excel
17. Others
18. Excel Quiz
1. Course Contents
2. Introduction
3. Introduction to Power BI
4. Life Hack to get Power BI Pro [2022]
5. Power BI Desktop
6. Power BI Services
7. Power Query Editor
8. Data Profiling
9. PBI Group by
10. PBI Applied Steps
11. Append vs Merge
12. PBI Visuals
13. Power BI Charts
14. Introduction to DAX
15. Implicit Measures
16. DAX Formula
17. Basic DAX Functions
18. Date Functions
19. CALENDAR Functions
20. Contexts Row vs Filter
21. CALCULATE & FILTER Functions
22. IF ELSE Conditions
23. Time Intelligence Functions
24. X vs Non X Functions
25. Tool tips , Drill Throughs
26. Relationships
27. KPIs
28. Administration in Power BI
29. Security in Power BI
30. PBI Best Practices
31. Formatting
32. EDA
33. Power BI Quiz
1. Course Contents
2. What is Data Visualization
3. BI Process
4. About Tableau - What is Tableau?
5. About Tableau - how to use Tableau?
6. About Tableau - Features of Tableau
7. Tableau Architecture
8. Tableau vs Power BI
9. Tableau Desktop
10. Relationships, Joins & Unions
11. Sets in Tableau
12. Groups in Tableau
13. Hierarchies in Tableau
14. Filters in Tableau
15. Highlighting
16. Device Deisgner
17. Parameters
18. Data Blending & Mark Size
19. Transparency
20. Date Aggregation
21. Generated Fields
22. Discrete vs Continuous
23. Charts in Tableau
24. Pivot Tables in Tableau
25. LOD Expressions
26. Calculated Fields
27. Formatting
28. Forecasting in Tableau
29. Analytics in Tableau
30. Dashboarding
31. Tableau Quiz
1. Course Contents
2. Introduction to PA
3. Predictive Analytics Process
4. How does the Model Work?
5. Why Predictive Modelling?
6. Applications of Predictive Modelling
7. What is Machine Learning
8. Types of Machine Learning
9. Classification
10. k-nearest neighbors (Theory)
11. k-NN Example (Excel)
12. Classification Practicals Part 1
13. k-nearest neighbors (Code)
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14. Decision Trees (Theory)
15. Decision Tree (Code)
16. Random Forests
17. Random Forests (Code)
18. Boosting Algorithms
19. Boosting (Code)
20. Regression (Theory)
21. Regression(Code)
22. Clustering (Theory)
23. Clustering (Code)
24. Time Series Forecasting (Theory)
25. Time Series Forecasting (Code)
26. Predictive Analytics Quiz
1. Course Contents
2. Introduction to ETL & Data Warehouse
3. What is ETL?
4. ETL Tools & Azure Data Factory Architecture
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5. What is Data Warehouse?
6. Benefits of Data Warehousing
7. Data Warehouse Structure
8. Why do we need Staging environment?
9. What are Data Marts?
10. Data Lakes
11. Data Lake vs Data Warehouse
12. Elements of Data Lake
13. ETL & Data Warehousing Quiz#1
1. Interview Guides
1. Power BI - Live Project
2. Tableau - Live Project
3. DA - Live Project Part I (Banking)
4. DA - Live Project Part II (Banking)
1. Final Assignment

About this course

₹4,999.00
293 lessons
49.5 hours of video content

Zep Analytics

Data Analytics Masters - 2025 Edition

Course curriculum

Welcome to the Course

Leveraging Generative AI for Data Analytics

Introduction to Data Analytics

Pre-Requisites

Python for Data Analytics

Exploratory Data Analysis

Business Statistics

SQL for Data Analysis

Microsoft Excel

Power BI

Tableau

Predictive Analytics

ETL & Data Warehousing

Interview Q&A Guides

Capstone Project

Assignments

About this course