Data Analytics ( SQL + Power BI + Python )
Build a Strong Data Mindset and Turn Information Into Action
This Data Analytics course gives you the skills to understand how data works and how businesses use it to make decisions. You’ll start with the foundations of data, learning SQL, Excel and basic statistics. You’ll work with real datasets to explore patterns, clean messy information and understand how data flows through modern systems. The goal is to help you think analytically and build confidence with core tools.
As you move forward, you’ll learn how to transform raw data into meaningful insights. You’ll work with Python, Power BI and essential analytical techniques that companies rely on every day. You’ll practice cleaning, modeling and visualizing data while learning to communicate insights clearly. Each module includes hands-on tasks that build your practical skills step by step.
Toward the end, you’ll apply everything you’ve learned to real business problems. You’ll build dashboards, create reports, analyze trends and present findings like a professional analyst. You’ll also learn how analysts work inside organizations and how to build a portfolio that stands out. By the time you finish, you’ll be ready to solve real-world data challenges and take the next step in your analytics career.
This Data Analytics Course Curriculum is designed to take students from beginner to expert level.
Core Content
➤ What is Data Analytics? Overview of the analytics workflow
➤ Types of data: Structured, Semi-structured, Unstructured
➤ Introduction to Databases: DBMS vs RDBMS
➤ SQL Server installation and environment setup
➤ Database objects: Tables, Views, Schemas
➤ SQL Commands: CREATE, ALTER, UPDATE, DELETE, DROP, TRUNCATE
➤ Constraints: Primary Key, Foreign Key, Unique, Check, Default
Practical Task
Set up SQL Server, create your first database and build tables with constraints.
Pro Tip
Data integrity starts with clean schema design.
Learning Outcome
You’ll be able to set up a relational database and perform basic CRUD operations.
Core Content
➤ WHERE, IN, BETWEEN, LIKE, ORDER BY
➤ Aggregate Functions: COUNT, MIN, MAX, SUM, AVG
➤ GROUP BY & HAVING for summarization
➤ CASE expressions for conditional logic
Practical Task
Analyze employee or sales datasets with filtering, sorting and aggregation.
Pro Tip
Check query logic using small subsets before running on large datasets.
Learning Outcome
You’ll be comfortable writing analytical queries for business reports.
Core Content
Functions
➤ String: LEN, SUBSTRING, REPLACE, CONCAT
➤ Numeric: ROUND, CEILING, FLOOR
➤ DateTime: GETDATE, DATEADD, DATEDIFF, FORMAT
➤ CONVERT and CAST
Joins & Normalization
➤ INNER, LEFT, RIGHT, FULL
➤ Normalization: 1NF, 2NF, 3NF
➤ Keys and referential integrity
Practical Task
Clean text/date fields and write join queries across customer, order and product tables.
Pro Tip
Normalize for consistency, then denormalize for reporting when needed.
Learning Outcome
You’ll master data cleaning inside SQL and understand relational modeling.
Core Content
➤ Subqueries & Nested SELECTs
➤ Stored Procedures (with/without parameters)
➤ Functions (Scalar, Inline, Multi-statement)
➤ Temporary tables and variables
➤ Views and Triggers
➤ Transactions & ACID properties
➤ Indexing basics
➤ Backups and security roles
Practical Task
Create a stored procedure that generates a daily sales report.
Pro Tip
Avoid unnecessary indexes; they slow down inserts and updates.
Learning Outcome
You’ll be able to automate workflows and manage secure, optimized SQL databases.
Core Content
➤ Installing Python, Anaconda and Jupyter
➤ Variables, data types, loops, conditions
➤ Lists, dictionaries, sets, tuples
➤ Functions & Lambda utilities
➤ File handling: CSV, JSON
➤ Error handling with try-except
Practical Task
Write a program that loads, cleans and exports data using Python.
Pro Tip
Use functions to make your code more modular and reusable.
Learning Outcome
You’ll understand Python fundamentals for data analysis tasks.
Core Content
➤ NumPy arrays and vector operations
➤ Pandas DataFrames: loading, cleaning, merging
➤ Handling missing values & duplicates
➤ Grouping data and aggregations
➤ Creating charts with Matplotlib
➤ Simple EDA workflows
Practical Task
Load a real dataset, clean it, analyze patterns and visualize insights.
Pro Tip
Use Pandas chaining to make transformations cleaner.
Learning Outcome
You’ll be ready to handle real-world datasets end-to-end using Python.
Core Content
Data Extraction
➤ Connecting to Excel, CSV, SQL, PDF, Web, APIs
➤ Import vs DirectQuery
➤ Power Query Editor and transformation steps
Data Cleaning
➤ Remove duplicates, blanks
➤ Transform data types
➤ Split, merge, append tables
➤ Pivot, unpivot and custom columns
Data Modeling
➤ Fact vs Dimension tables
➤ Star vs Snowflake schema
➤ Relationships, cardinality, filter direction
➤ Model optimization
Practical Task
Build a star schema model combining sales, product and customer data.
Pro Tip
A clean model makes DAX easier and dashboards faster.
Learning Outcome
You’ll be able to prepare and model datasets for enterprise analytics.
Core Content
➤ Row vs Filter context
➤ Calculated Columns, Measures, Tables
➤ Iterators: SUMX, AVERAGEX
➤ Filter functions: CALCULATE, ALL, FILTER
➤ Relationship functions: RELATED, USERELATIONSHIP
➤ Time Intelligence: YTD, QTD, MTD, PY, PM
➤ KPI measures & ranking metrics
Practical Task
Build DAX measures for YTD Sales, Profit Margin and MoM Growth.
Pro Tip
Test every new measure inside a simple table before using it in visuals.
Learning Outcome
You’ll confidently write DAX for dynamic dashboards and insights.
Core Content
➤ Table, Matrix, KPI, Card, Bar/Line charts
➤ Drill-down, drill-through and bookmarks
➤ Custom tooltips
➤ Slicers, filters, buttons and navigation
➤ Themes and design best practices
➤ AI visuals (Q&A, Key Influencers)
Practical Task
Create a multi-page dashboard using sales, customer and product insights.
Pro Tip
Each dashboard page should answer one clear business question.
Learning Outcome
You’ll build interactive dashboards that tell a clear data story.
Core Content
➤ Workspaces, Dashboards, Apps
➤ Sharing, publishing and collaboration
➤ Scheduling refresh
➤ Data Gateway setup
➤ RLS: static and dynamic
➤ Deployment pipelines: Dev → Test → Prod
➤ Usage metrics, alerts, subscriptions
Practical Task
Publish a dashboard to the Service, configure refresh and set RLS.
Pro Tip
Separate your development dashboards from production dashboards.
Learning Outcome
You’ll deploy secure, scalable BI solutions in a business environment.