🎯 Objective

• Gain in-depth SQL knowledge from beginner to advanced level.

• Develop practical SQL skills through real-world projects and exercises.

• Prepare for SQL certifications and job roles.

 

📆 Duration: 8 Weeks (6-8 hours per week)

 

Training Plan

Week 1: Introduction to SQL & Databases

• Topics Covered:

  • Introduction to databases (RDBMS concepts)

  • SQL Fundamentals & Syntax

  • Data types and database structure (Tables, Columns, Rows)

• Hands-on Labs:

  • Install & set up SQL Server.

  • Create your first database and basic table structure.

Week 2: SQL Queries (SELECT) & Data Retrieval

• Topics Covered:

  • SELECT statement, WHERE clause, ORDER BY

  • DISTINCT, TOP/LIMIT, and filtering data

  • Logical operators (AND, OR, NOT)

• Hands-on Labs:

  • Practice queries on sample datasets.

  • Filtering and sorting exercises.

Week 3: SQL Functions & Data Aggregation

• Topics Covered:

  • Aggregate Functions (COUNT, AVG, SUM, MIN, MAX)

  • GROUP BY & HAVING clauses

  • Built-in Functions (String, Numeric, Date)

• Hands-on Labs:

  • Aggregation and summarizing datasets.

  • Grouping and summarizing sales data.

Week 4: Intermediate SQL: JOINS & Subqueries

• Topics Covered:

  • INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN

  • Cross joins and self-joins

  • Subqueries (nested queries, EXISTS, IN)

• Hands-on Labs:

  • Joining multiple tables to build complete datasets.

  • Solving business queries with subqueries.

Week 5: SQL Data Manipulation (DML) & Transactions

• Topics Covered:

  • INSERT, UPDATE, DELETE operations

  • Transactions and data integrity

  • COMMIT and ROLLBACK commands

• Hands-on Labs:

  • Performing data insertion, updates, and deletions safely.

  • Transaction management exercises.

Week 6: Database Design & Normalization

• Topics Covered:

  • Database normalization (1NF, 2NF, 3NF)

  • Keys and constraints (Primary, Foreign, Unique, Check)

  • Designing relational database schemas

• Hands-on Labs:

  • Design normalized database schemas.

  • Create a robust e-commerce database design project.

Week 7: Advanced SQL Concepts

• Topics Covered:

  • Views, stored procedures, and functions

  • Triggers & indexes

  • Performance tuning & query optimization basics

• Hands-on Labs:

  • Implement stored procedures for common tasks.

  • Create and optimize indexes for better query performance.

Week 8: Real-world SQL Projects & Certification Prep

• Topics Covered:

  • Real-world SQL project (Retail, Finance, or Healthcare)

  • Data analysis case study

  • SQL Certification exam preparation (optional)

• Hands-on Labs:

  • Execute end-to-end SQL project solving a business problem.

  • Finalize portfolio-ready SQL queries and scripts.

 

📑 Certifications (Recommended)

• Microsoft Certified: Azure Data Fundamentals (DP-900)

• Microsoft Certified: Azure Database Administrator (DP-300)

• Oracle Database SQL Certification (1Z0-071)

 

⚙️ Tools & Technology Recommended

• Database: Microsoft SQL Server / PostgreSQL / MySQL

• Tools: SSMS (SQL Server Management Studio), pgAdmin, MySQL Workbench, or Azure Data Studio

• Platforms: Azure SQL, AWS RDS (optional cloud-based practice)

 

📈 Assessment Plan

• Weekly quizzes to validate learning.

• End-of-course SQL project showcasing skills.

• Regular interactive exercises and real-time query troubleshooting.

🎯 Objective

• Learn Python from fundamental to advanced level

• Develop practical coding skills through real-world projects

• Prepare for Python certifications or job roles in Data Engineering, Data Analytics, or Software Development

 

📆 Duration: 8 Weeks (6-8 hours per week)

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📖 Detailed Week-by-Week Training Plan

Week 1: Python Fundamentals

• Topics Covered:

  • Python Installation & Setup (Anaconda/Jupyter/VS Code)

  • Syntax, Variables, Data Types, Operators

  • Strings, Numbers, Lists, Tuples, Dictionaries

• Hands-on Labs:

  • Setting up Python environment

  • Exercises on variables, data types, and simple programs

Week 2: Control Flow & Functions

• Topics Covered:

  • Conditional statements (if, else, elif)

  • Loops (for, while)

  • Functions, Parameters, and Return statements

• Hands-on Labs:

  • Building logic-driven applications (calculator, number guessing games)

  • Creating reusable functions

Week 3: Python Modules & Libraries

• Topics Covered:

  • Python standard libraries (math, datetime, os, sys)

  • Third-party libraries installation (pip, conda)

  • File handling (reading/writing files)

• Hands-on Labs:

  • Writing scripts to automate file and folder operations

  • Project: Basic file management script

Week 4: Data Handling & Analysis (NumPy, Pandas)

• Topics Covered:

  • Introduction to NumPy (arrays, indexing, slicing)

  • Pandas library (DataFrames, data manipulation, CSV/Excel handling)

• Hands-on Labs:

  • Data analysis using Pandas (Sales data, Weather data)

  • Project: Data Cleaning and preparation for analytics

Week 5: Data Visualization (Matplotlib, Seaborn)

• Topics Covered:

  • Introduction to Matplotlib and Seaborn

  • Creating visualizations (bar charts, histograms, scatter plots)

• Hands-on Labs:

  • Visual analysis of datasets

  • Project: Creating dashboards and visual reports

Week 6: Object-Oriented Programming (OOP)

• Topics Covered:

  • Classes & Objects

  • Methods, Inheritance, Polymorphism

  • Encapsulation and Abstraction

• Hands-on Labs:

  • Building reusable Python classes

  • Project: Develop a simple banking application using OOP concepts

Week 7: Web & API Interaction

• Topics Covered:

  • Web Scraping Basics (BeautifulSoup)

  • API Requests (requests library, JSON handling)

  • Basic automation scripts

• Hands-on Labs:

  • Web scraping exercises

  • Project: Fetching data from an API and visualizing the output

Week 8: Advanced Python Concepts & Projects

• Topics Covered:

  • Error handling (Exceptions, debugging)

  • Introduction to databases (SQLite, PostgreSQL integration)

  • Final project and portfolio building

• Hands-on Labs:

  • Error handling exercises

  • Final Capstone Project: Data analysis tool, automation script, or API-driven application

 

📑 Python Certifications (Recommended)

• Microsoft Certified: Azure Developer Associate (AZ-204)

• PCAP – Certified Associate in Python Programming

• Google IT Automation with Python Professional Certificate

 

🛠️ Tools & Technology

• IDEs: Jupyter Notebook, Visual Studio Code, PyCharm

• Python Libraries: NumPy, Pandas, Matplotlib, Seaborn, Requests, BeautifulSoup, SQLite

• GitHub: Version control & portfolio building

 

📈 Assessment Plan

• Weekly coding exercises & quizzes

• Real-world project implementation

• Final Capstone Project review & assessment

🎯 Objective

• Gain proficiency in Azure data engineering services and Power BI

• Achieve practical knowledge through hands-on projects

• Obtain relevant Azure and Power BI certifications

 

🗓️ Duration: 8 weeks (2 months)

(Approximately 8-10 hours per week)

 

📚 Course Content

Week 1-2: Azure Fundamentals

• Azure Basics:

  • Introduction to Cloud Computing & Azure Platform

  • Azure Portal Overview

  • Azure Storage (Blob, Data Lake, File Storage)

• Azure SQL Database:

  • Deploy and manage databases

  • Querying SQL Databases

Week 3: Azure Data Factory (ADF)

• ETL Concepts & ADF Pipelines

  • Pipeline creation & management

  • Data flow transformations

  • Parameterization & expressions

  • Scheduling & triggers

Hands-on Lab:

• Build ETL pipelines to ingest data from Blob storage to Azure SQL Database.

Week 4: Azure Databricks

• Databricks Basics:

  • Workspace setup

  • Notebooks & Spark basics

• Delta Lake:

  • Introduction to Delta Lake

  • Batch and streaming data processing

Hands-on Lab:

• Data transformation using PySpark & Delta Lake.

Week 5: Azure Synapse Analytics

• Introduction to Azure Synapse

  • SQL pool vs Spark pool

  • Data warehousing concepts & design

• Serverless and dedicated SQL pools

Hands-on Lab:

• Data ingestion and analytics in Synapse workspace.

Week 6: Power BI Basics & Modeling

• Power BI Desktop & Service

  • Data connection and transformation (Power Query)

  • Creating relationships & data modeling

  • DAX Introduction (Calculated columns, measures)

Hands-on Lab:

• Create a Power BI report using data from Azure SQL.

Week 7: Advanced Power BI & DAX

• DAX Advanced concepts

  • Time intelligence

  • Advanced calculations and measures

  • Optimizing Power BI reports

• Data Visualization Best Practices

  • Dashboard designing techniques

  • Performance optimization

Hands-on Lab:

• Advanced visualization dashboard creation with real-time data integration.

Week 8: Security, Monitoring & Deployment

• Azure Data Security

  • Role-based access control (RBAC)

  • Azure Key Vault integration

  • Data encryption

• Monitoring and Troubleshooting

  • Azure Monitor

  • Diagnostic logs & alerts

• Deployment Automation & CI/CD

  • Azure DevOps integration for data pipelines

  • Power BI deployment pipelines and workspace governance

Hands-on Lab:

• Set up automated deployment and monitoring alerts for data engineering pipelines.

 

🎯 Certifications (Optional, Recommended post training)

• Azure Data Engineer Associate (DP-203)
  Microsoft Certified: Power BI Data Analyst Associate (PL-300)

 

🚀 Projects (Recommended)

• End-to-End Data Pipeline Project:

  • Data ingestion (ADF) → Processing & Storage (Databricks/Synapse) → Visualization (Power BI)

• Real-Time Dashboard Project:

  • Streaming data integration with Power BI and Azure Stream Analytics

 

📊 Assessment

• Weekly quizzes/tests on key topics

• Final project & peer review session

 

🗂️ Tools & Technology Used

• Azure Portal, Azure Data Factory, Databricks, Azure Synapse Analytics

• SQL Server Management Studio (SSMS)

• Power BI Desktop and Service

• Azure DevOps & GitHub