Data Analytics (Python, SQL, Excel & Power BI)

• Understanding the Role of a Data Analyst
• Industry Expectations and Career Opportunities
• Current Industry Demand and Job Market Trends
• Analytics Technology Landscape
• Why SQL?
• Why Excel?
• Why Python?
• Why Power BI?
• Analytical Tools Overview
• Database Concepts
• Data Warehousing Fundamentals
• Data Pipelines
• ETL vs ELT

Goal: Develop a strong understanding of the data analytics profession, modern data ecosystems, and the technologies that enable data-driven decision making in organizations.

• Introduction to SQL
• Database Objects & Relationships
• SQL Syntax & Query Structure
• SELECT Statements
• WHERE Clause & Filtering
• ORDER BY & Sorting
• Data Types and Data Conversion
• Built-in Functions
• Logical Operators

Goal: Build foundational SQL skills to retrieve, filter, and organize data efficiently from relational databases.

• SQL Join Concepts
• INNER, LEFT, RIGHT and FULL Joins
• Aggregate and Windows Functions (Count, AVG, SUM, Min, Max, etc.)
• GROUP BY & HAVING
• Business KPI Calculations
• Analytical Query Techniques
• Multi-Table Analysis
• Reporting-Oriented Query Development

Goal: Develop the ability to combine data from multiple sources and generate meaningful business insights using SQL.

• Data Manipulation Language (DML)
• Data Definition Language (DDL)
• INSERT, UPDATE, DELETE Operations
• Subqueries
• Common Table Expressions (CTE)
• Set Operations (UNION, INTERSECT)
• Query Optimization Fundamentals
• DISTINCT vs GROUP BY
• SQL Best Practices

Goal: Master advanced SQL techniques for transforming, managing, and optimizing analytical datasets.

• Analytics Workflow in Excel
• Excel Interface & Productivity Features
• Data Entry & Management
• Essential Analytical Functions (Sum, Avg, Count, etc.)
• Sorting & Filtering
• Cell Referencing Techniques
• Data Exploration Fundamentals

Goal: Develop proficiency in using Excel as an analytical tool for data exploration and business reporting.

• Data Quality Assessment
• Handling Missing Values
• Duplicate Detection & Removal
• Text-to-Columns
• Data Standardization
• Data Validation
• Conditional Formatting
• Basic Data Visualization

Goal: Learn how to prepare raw datasets for analysis by applying industry-standard data cleaning and quality assurance techniques.

• Pivot Tables & Pivot Charts
• Advanced Lookup Techniques
• VLOOKUP, HLOOKUP, XLOOKUP
• INDEX-MATCH
• Text Functions
• Logical Functions
• Dynamic Reporting Techniques
• Business Analytics Use Cases

Goal: Use advanced Excel functions and analytical techniques to perform business analysis and automate reporting processes.

• Python Environment Setup
• Introduction to VS Code
• Variables & Data Types
• Operators
• Conditional Statements
• Loops & Iteration
• Functions
• String Manipulation
• Python Programming Best Practices

Goal: Build a strong programming foundation to automate analytical tasks and support data-driven problem solving.

• Introduction to Pandas
• DataFrames & Series
• Importing Data Sources (Reading CSV, Excel files)
• Data Exploration Techniques
• Data Filtering & Selection
• Data Cleaning with Pandas
• Handling Missing Values
• Data Transformation
• Aggregation & Grouping
• Exploratory Data Analysis (EDA)

Goal: Perform end-to-end data preparation and exploration analysis using Python and Pandas.

• SQL Assessment: Data Retrieval; Filtering & Sorting; Joins; Aggregations; GROUP BY & HAVING; Business Query Development
• Excel Assessment: Data Cleaning; Lookup Functions; Pivot Tables; Conditional Formatting; Business Reporting
• Python Assessment: Variables & Functions; Pandas Operations; Data Cleaning; Data Aggregation; Basic EDA

Goal: Evaluate learners’ proficiency in SQL, Excel, and Python and identify areas requiring improvement before progressing to Business Intelligence and Dashboard Development.

• Introduction to Business Intelligence
• Power BI Ecosystem
• Data Connectivity
• Power Query Fundamentals
• Data Transformation
• Creating Reports & Visualizations
• Fields, Measures & Dimensions
• Dashboard Fundamentals

Goal: Build foundational business intelligence skills by creating interactive reports and visualizations in Power BI.

• Data Modeling Concepts
• Relationship Management
• Star & Snowflake Schemas
• Calculated Columns & Measures
• Introduction to DAX
• Advanced DAX Functions
• Advanced Visualizations
• SQL Integration with Power BI
• DirectQuery vs Import Mode
• Enterprise Reporting Best Practices

Goal: Design scalable analytical data models and develop advanced dashboards capable of supporting business decision-making.

• Business Problem Identification
• Requirement Gathering
• Dataset Selection
• Project Scope Definition
• Data Exploration
• Data Cleaning Strategy
• Project Planning & Milestones
• Stakeholder Perspective

Goal: Translate a real-world business problem into a structured analytics project using industry-standard methodologies.

• Dashboard Design Principles
• KPI Framework Development
• Data Storytelling Techniques
• User-Centric Visualization Design
• Interactive Dashboard Development
• Report Publishing & Sharing
• Insight Communication

Goal: Transform analytical findings into compelling visual stories that drive business action and executive decision-making.

• Presentation Techniques
• Insight Communication
• Business Recommendation Development
• Stakeholder Management
• Peer Review & Feedback
• Project Evaluation Framework

Goal: Demonstrate the ability to communicate analytical insights effectively to both technical and business stakeholders.

• Data Analyst Resume Development
• LinkedIn Profile Optimization
• Portfolio Building
• Interview Preparation
• Case Study Discussions
• Career Planning & Growth Strategy

Goal: Prepare students to successfully enter the analytics job market with a professional portfolio, interview readiness, and a clear career development plan.