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Professional Diploma in Data Analytics and Business Intelligence
IT & Software Development

Professional Diploma in Data Analytics and Business Intelligence

Build job-ready skills in Data Analytics and Business Intelligence using Excel, SQL, Python, Power BI and Tableau, with statistics, predictive analytics, data warehousing and a hands-on capstone project.
Course ModeOnline
Duration1 Year
StartsTo Be Announced
Course Fee
₹24,999 /-
Enroll Now
About The Course

Overview

The Professional Diploma in Data Analytics & Business Intelligence is a comprehensive program designed to build practical skills in data analysis, business intelligence and data-driven decision-making.

The program covers the complete analytics lifecycle, including data collection, cleaning, analysis, visualization and interpretation. Learners will work with Excel, SQL, Python, statistics, Power BI and Tableau to solve real-world business problems.

The course also introduces data warehousing, ETL, BI architecture, predictive modelling, machine learning concepts and dashboard development. Through hands-on labs, projects and a capstone project, learners will develop practical experience in analysing data, creating interactive dashboards and presenting actionable business insights.

The program is designed for beginners to early-intermediate learners and does not require a prior coding background.

Learning Goals

Course Outcomes

By the end of this program, learners will be able to:

  1. Understand the fundamentals of Data Analytics, Business Intelligence and the analytics lifecycle.
  2. Use Excel to clean, analyse and visualize business data.
  3. Query, join and aggregate relational data using SQL.
  4. Apply statistical and probability concepts to interpret data and support business decisions.
  5. Use Python, Pandas and NumPy for data cleaning, analysis and visualization.
  6. Create interactive dashboards and reports using Power BI and Tableau.
  7. Understand BI concepts, data warehousing, ETL, data modelling and enterprise BI architecture.
  8. Apply basic predictive modelling and machine learning concepts to business problems.
  9. Integrate analytics skills into an end-to-end capstone project and create a job-ready portfolio.
Curriculum

Course / Modules

Module 1: Foundations of Data Analytics & BI

Topics Covered:

  1. Data Analytics vs. Business Intelligence vs. Data Science
  2. Analytics Lifecycle
  3. Data Collection
  4. Data Cleaning
  5. Data Analysis
  6. Data Visualization
  7. Decision-Making
  8. Descriptive Analytics
  9. Diagnostic Analytics
  10. Predictive Analytics
  11. Prescriptive Analytics
  12. Data Types
  13. Data Sources
  14. Data Quality
  15. Structured and Unstructured Data
  16. Primary and Secondary Data
  17. KPIs and Metrics
  18. Business Problem Framing
  19. Business Case Studies

Assessment: Quiz and short case-study write-up identifying KPIs for a sample business.

Module 2: Excel for Data Analysis

Topics Covered:

  1. Data Sorting
  2. Data Filtering
  3. Text Functions
  4. Data Validation
  5. VLOOKUP
  6. XLOOKUP
  7. INDEX-MATCH
  8. IF Functions
  9. Nested Logic
  10. Array Formulas
  11. PivotTables
  12. PivotCharts
  13. Excel Dashboard Building
  14. Slicers
  15. Conditional Formatting
  16. Sparklines
  17. Power Query
  18. Power Pivot
  19. Introduction to DAX Basics

Assessment: Build an Excel sales dashboard using a raw dataset.

Module 3: SQL for Data Analytics

Topics Covered:

  1. Relational Databases
  2. RDBMS Concepts
  3. SELECT Statements
  4. WHERE Clause
  5. ORDER BY
  6. Aggregate Functions
  7. GROUP BY
  8. HAVING
  9. INNER JOIN
  10. LEFT JOIN
  11. RIGHT JOIN
  12. FULL JOIN
  13. Self-Joins
  14. Subqueries
  15. Common Table Expressions
  16. Window Functions
  17. RANK
  18. ROW_NUMBER
  19. LAG
  20. LEAD
  21. CASE Statements
  22. String Functions
  23. Date Functions
  24. NULL Handling
  25. Views
  26. Indexes
  27. Query Optimization Awareness
  28. Business Questions Using SQL
  29. SQL-Based Business Analysis

Assessment: SQL project solving 15–20 business questions using a retail/e-commerce database.

Module 4: Statistics & Probability for Analytics

Topics Covered:

  1. Descriptive Statistics
  2. Mean
  3. Median
  4. Mode
  5. Variance
  6. Standard Deviation
  7. Percentiles
  8. Data Distributions
  9. Normal Distribution
  10. Skewed Distributions
  11. Visual Diagnostics
  12. Probability Basics
  13. Conditional Probability
  14. Sampling Methods
  15. Sampling Distribution
  16. Hypothesis Testing
  17. t-Test
  18. Chi-Square Test
  19. p-Values
  20. Confidence Intervals
  21. Correlation vs. Causation
  22. Regression Fundamentals
  23. Simple Linear Regression

Assessment: Statistical analysis mini-report on a provided dataset to test a business hypothesis.

Module 5: Python for Data Analytics

Topics Covered:

  1. Python Basics
  2. Variables
  3. Data Types
  4. Loops
  5. Functions
  6. Control Flow
  7. NumPy Fundamentals
  8. Numerical Operations
  9. Pandas Series
  10. Pandas DataFrames
  11. Indexing
  12. Filtering
  13. Sorting
  14. Data Cleaning with Pandas
  15. Missing Values
  16. Duplicate Handling
  17. Data Type Handling
  18. Merging Datasets
  19. Joining Datasets
  20. Exploratory Data Analysis
  21. GroupBy
  22. Pivot Tables
  23. Aggregation
  24. Correlation
  25. Matplotlib
  26. Seaborn
  27. Data Distributions
  28. Data Trends
  29. Data Comparisons
  30. Jupyter Notebook
  31. Google Colab
  32. Reproducible Analysis
  33. End-to-End EDA

Assessment: Python EDA notebook to clean a messy dataset, generate insights and visualize findings.

Module 6: Data Visualization & BI Tools — Power BI & Tableau

Topics Covered:

  1. Principles of Data Visualization
  2. Dashboard Design
  3. Chart Selection
  4. Data Storytelling
  5. Power BI Data Import
  6. Power Query
  7. Data Modelling
  8. Star Schema
  9. Relationships
  10. DAX Measures
  11. Calculated Columns
  12. Power BI Report Building
  13. Dashboard Building
  14. Slicers
  15. Drill-Through
  16. Publishing Reports
  17. Tableau Data Connections
  18. Calculated Fields
  19. Table Calculations
  20. Tableau Dashboard Design
  21. Filters and Actions
  22. Parameters
  23. Story Points
  24. Choosing the Right BI Tool
  25. Sharing and Publishing Reports
  26. Version Control Basics for BI Assets
  27. Business Dashboard Development

Assessment: Create and deliver a polished interactive Power BI or Tableau dashboard answering a defined business brief.

Module 7: BI Concepts, Data Warehousing & ETL

Topics Covered:

  1. Data Warehouse
  2. Data Lake
  3. Data Mart
  4. Star Schema
  5. Snowflake Schema
  6. Fact Tables
  7. Dimension Tables
  8. ETL Concepts
  9. ELT Concepts
  10. Data Pipelines
  11. OLAP
  12. OLTP
  13. Data Modelling for BI
  14. Data Governance
  15. Data Quality
  16. Data Security
  17. GDPR and Privacy Awareness
  18. Cloud BI Ecosystems
  19. Azure Analytics Stack
  20. AWS Analytics Stack
  21. GCP Analytics Stack

Assessment: Design a star-schema data model for a given business scenario.

Module 8: Advanced Analytics & Introduction to Predictive Modeling

Topics Covered:

  1. Introduction to Machine Learning
  2. Supervised Learning
  3. Unsupervised Learning
  4. Business Use Cases of Machine Learning
  5. Regression Models
  6. Forecasting
  7. Linear Regression
  8. Multiple Regression
  9. Regression Using Python
  10. Classification
  11. Logistic Regression
  12. Decision Trees
  13. Clustering
  14. K-Means
  15. Customer Segmentation
  16. Time Series Basics
  17. Trend
  18. Seasonality
  19. Simple Forecasting
  20. Model Evaluation
  21. Accuracy
  22. RMSE
  23. Confusion Matrix
  24. Ethical and Bias Considerations

Assessment: Build a simple predictive model such as churn prediction or sales forecasting with an interpretation report.

Module 9: Capstone Project & Portfolio

Topics Covered:

  1. Project Scoping
  2. Business Problem Definition
  3. KPI Identification
  4. Data Source Identification
  5. Data Collection
  6. Data Cleaning Using SQL, Python and Excel
  7. Statistical Analysis
  8. Exploratory Data Analysis
  9. Dashboard Development
  10. Power BI or Tableau
  11. Data Presentation
  12. Data Storytelling
  13. Stakeholder-Style Presentation
  14. Portfolio Development
  15. GitHub Case Study
  16. PDF Case Study
  17. LinkedIn-Ready Case Study
  18. Final Capstone Presentation

Assessment: Complete end-to-end capstone project covering dataset, cleaned data, SQL/Python analysis, interactive dashboard and presentation deck.

Need Help?

Frequently Asked Questions

No. The program requires basic computer literacy and does not require a prior coding background.
You will learn Data Analytics, Business Intelligence, Excel, SQL, Statistics, Python, Power BI, Tableau, data warehousing, ETL and introductory predictive modelling.
Yes. The program includes an Excel dashboard project, SQL project, Python EDA project, Power BI/Tableau dashboard, predictive modelling mini-project and a final capstone project.
The capstone integrates the complete analytics workflow, from defining a business problem and collecting data to cleaning, analysis, dashboard development, presentation and portfolio creation.
The program is designed for beginners to early-intermediate learners interested in developing practical Data Analytics and Business Intelligence skills.