Data Analytics Program
Unlock your potential with our Comprehensive Python, Power BI, and SQL Curriculum—a step-by-step, project-driven course designed for aspiring data professionals. Whether you're starting from scratch or looking to deepen your technical expertise, this curriculum delivers a practical, career-focused pathway to mastering key tools in modern data handling and software development.
Who Should Enroll?
This course is ideal for:
- Aspiring data analysts, business analysts, and software developers.
- Beginners seeking a structured introduction to Power BI, SQL, and data workflows.
- Professionals aiming to advance their skills in real-world data modeling, query optimization, and dashboard reporting.
- Students preparing for careers in data science, business intelligence, or software engineering.
No prior experience is required—just a commitment to learn through doing.
What You'll Learn
This curriculum focuses on practical skills and hands-on learning across three core areas:
Power BI
- Install and configure Power BI Desktop.
- Import and transform data from diverse sources (Excel, CSV, Web, XML, PDF, APIs).
- Master Power Query for cleaning and combining datasets.
- Build data models with calculated columns, DAX, and relationships.
- Design and publish professional-level dashboards with advanced visualizations.
- Understand relational databases and SQL fundamentals.
- Perform data retrieval and manipulation using SELECT, INSERT, UPDATE, DELETE.
- Use joins, subqueries, and aggregate functions for complex queries.
- Apply advanced SQL techniques: window functions, CTEs, views, transactions, normalization.
- Explore data integrity with constraints, indexing, and stored procedures.
SQL (Structured Query Language)
Learning Outcomes
By the end of the course, learners will be able to:
- Build end-to-end interactive dashboards and reports using Power BI.
- Write efficient SQL queries to analyze, clean, and manipulate relational data.
- Design normalized and denormalized databases using ER diagrams.
- Optimize queries for performance and scalability.
- Apply Python or R scripts within Power BI for advanced data operations.
- Tackle real-world business challenges with integrated solutions.
Real-World Projects Include
- Sales Performance Dashboard: Import sales data, transform it with Power Query, and build interactive visuals.
- Customer Segmentation Report: Apply SQL joins and filters to categorize customers, then visualize insights in Power BI.
- Financial Performance Analyzer: Use DAX and advanced charting for KPIs and trends.
- Supply Chain Optimization Dashboard: Merge multiple data sources to analyze delays, inventory, and logistics.
- Capstone Project: Design and implement a unview business intelligence solution using Power BI and SQL.
Each project is designed to mimic real business scenarios, reinforcing practical application over theory.
Course Curriculum
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Introduction
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Course Overview
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Power BI Installation
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Software Setup
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Interface Tour
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Power BI Desktop
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Data Import
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Course Dataset Intro
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Manual Data Entry
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Import: Excel, CSV, XML, PDF
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Connect: Database, Web, Web API
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Power Query Basics
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Power Query Basics
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Reuse Data Models, Manage Parameters
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Filtering and Managing Data
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Filter Rows: Number, Text, Date
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Advanced Filtering (Logic)
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Manage Queries & Columns
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Keep/Remove Rows, Duplicates, Blanks
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Handle Errors, Sort Data
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Column Operations
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Split Columns (Delimiter/Other)
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Group By: Count, Sum, Avg, Median, Min/Max
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Find Duplicates in Groups
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Change Data Types, Headers
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Replace Values
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Combining and Transforming Data
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Append Queries, Combine Files
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Merge Queries (All Types)
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Transpose, Reverse, Count Rows
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Rename, Fill Values, Rearrange Columns
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Convert to List, Text Format & Extraction
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Advanced Operations
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Basic Stats, Math, Date-Time Ops
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Run R & Python Scripts
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Adding Columns
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Add Column (Example, Custom, Index, Function, Conditional, Duplicate)
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Viewing
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View Options, Query Dependency
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Data Modeling
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Relationships, Calculated Columns, Measures
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DAX Table, Calculation Groups
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Security in Models
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Q&A Setup
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Basic Charts
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Bar, Column, Line, Area Charts
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Combo Charts (Line + Column)
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Ribbon, Waterfall, Funnel, Scatter
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Pie, Donut, Treemap, Map, Filled Map
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Advanced Visualizations
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Gauge, KPI, Cards, Slicers
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Table, Matrix
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Visuals with Python & R
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Key Influencers, Decomposition Tree, Narrative
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Reports
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Report Creation & Publishing
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SQL Fundamentals
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Introduction to SQL
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Relational Database Management Systems (RDBMS) Concepts
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Database Design Basics (Entities, Attributes, Relationships)
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Data Types
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SQL Syntax and Conventions
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Setting up the Environment
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Download and Install PostgreSQL (or other RDBMS)
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Connect and Register SQL Server
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Create a New Database
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Overview of SQL Clients (e.g., DBeaver, pgAdmin)
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Connecting to a Database using a SQL Client
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Basic Data Retrieval
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Retrieve Data (SELECT statement)
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Selecting specific columns
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Using the WHERE clause to filter data
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Logical operators (AND, OR, NOT)
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Wildcard filters (LIKE, %, _)
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Sorting data (ORDER BY clause)
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Basic Data Manipulation
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Insert new records into a table
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Update existing records
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Delete records from a table
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SQL Server, MySQL, PostgreSQL, or SQLite
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DBeaver, SQL Developer, or other SQL client
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Joining Tables
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Types of Joins (INNER, LEFT, RIGHT, FULL OUTER)
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Understanding join conditions
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Self Joins
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Using aliases for tables and columns
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Aggregate Functions
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Using aggregate functions (COUNT, SUM, AVG, MIN, MAX)
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Grouping data with the GROUP BY clause
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Filtering grouped data with the HAVING clause
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Subqueries
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Subqueries in the FROM clause (derived tables)
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Correlated vs. non-correlated subqueries
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String Functions
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Concatenation, Substring, Length
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Upper/Lower case conversion
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Padding and Trimming
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Numeric Functions
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Basic arithmetic operations
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Mathematical functions (e.g., ABS, SQRT, RAND)
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Date and Time Functions
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Extracting date/time parts
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Date/time arithmetic
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Formatting dates
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Advanced Data Manipulation
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Transactions and Concurrency control
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Using constraints to enforce data integrity (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK)
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Indexes for performance optimization
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Views
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Database Design
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Normalization principles (1NF, 2NF, 3NF)
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Entity-Relationship (ER) Diagrams
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Denormalization
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Window Functions
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OVER, PARTITION BY, ORDER BY clauses
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ROWS and RANGE frames
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Types of window functions (e.g., ROW_NUMBER, RANK, LEAD/LAG)
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Common Table Expressions (CTEs)
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Creating and using CTEs
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Recursive CTEs
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CTEs vs. subqueries
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Stored Procedures and Functions
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Triggers
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Students will unview several hands-on projects throughout the course, culminating in a final project where they design and implement a unview data analysis solution.
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Project examples:
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Developing an interactive sales performance dashboard
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Creating a customer segmentation analysis and report
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Building a financial performance analysis tool
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Designing a supply chain performance dashboard
Jonathan Campbell
- 72 Videos
- 102 Lectures
- Exp. 4 Year
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COURSE BENEFITS
PLACEMENT ASSISTANCE
*MIN 5 COMPANY WALK-INS
GitHub portfolio and Job-ready resume to enhance their career prospects.
Course Features
- Resume & Linkedin Building
- 1-1 Mock Interviews
- 100% Hands-on
- Certification
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