Comprehensive SQL & Database Curriculum

Module 1: Database Architecture & Relational Modeling: Relational theory, Entity-Relationship Diagrams (ERDs), Normalization (1NF, 2NF, 3NF, BCNF), Primary and Foreign keys, constraints, and data integrity. Module 2: Advanced Querying & Multi-Table Joins: DDL & DML statements, complex multi-table joins (INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF joins), aggregation with GROUP BY, and HAVING filter conditions. Module 3: Subqueries & Common Table Expressions (CTEs): Correlated subqueries, inline views, Common Table Expressions (WITH clauses), recursive CTEs for hierarchical organizational data, and set operators (UNION, INTERSECT, EXCEPT). Module 4: Analytical SQL & Window Functions: Ranking functions (ROW_NUMBER, RANK, DENSE_RANK, NTILE), value window functions (LAG, LEAD, FIRST_VALUE), rolling aggregations, and running totals. Module 5: Stored Procedures, Triggers & ACID Transactions: Writing stored functions and procedures, trigger events, transaction control (BEGIN, COMMIT, ROLLBACK), concurrency control, and row-level locking. Module 6: Indexing, Query Optimization & Backend Integration: B-Tree, Hash, and GIN indexing, analyzing query execution plans (`EXPLAIN ANALYZE`), query tuning, and connecting databases to Node.js/Python via ORMs (Prisma / SQLAlchemy).

SQL Program Highlights for Rohini Learners

Training Modes: In-Person Studio Lab Training at South Extension & Preet Vihar Campuses + Live Online Option Program Duration: 2 to 3 Months (Intensive Practical Database & Query Optimization Bootcamp) Key DBMS Technologies: PostgreSQL, MySQL 8+, SQLite & Transact-SQL (T-SQL) Core Focus: Relational Schema Modeling, Complex Multi-Table Joins, CTEs, Window Functions, Indexing & Query Plan Tuning Portfolio Output: 4+ Enterprise Database Projects (E-Commerce Schema, Financial Audit Ledger, SaaS Multi-Tenant Architecture) Certification: TGC India Industry-Endorsed Database Specialist Certification + 100% Placement Support

Validates your skills, strengthens your portfolio, and opens opportunities in data science, analytics, and machine learning roles.

Yes — recognised by IT firms, analytics companies, e-commerce, and startups.

Will I get a certificate?

Yes — participants receive the TGC India Data Science Course Certification upon completion.

IT companies, analytics firms, e-commerce platforms, fintech, and startups.

Typical salary after this course?

Entry-level: ₹3–6 LPA, Mid-level: ₹7–12 LPA, Senior Level: ₹15+ LPA

Yes — classroom and live online training options available.

Does TGC provide placement support?

Yes — resume, portfolio, and placement guidance provided.

Will I work on live projects?

Yes — real datasets and predictive analytics projects included

Do I need prior knowledge?

No — course starts from basics and advances to machine learning & big data.

Duration of the course?

Typically 3–4 months with weekday, weekend, or fast-track batches.

Who can join this course?

Beginners, students, IT professionals, and analytics aspirants.

Will I receive placement support and live project guidance?

Yes. Every student designs real-world enterprise databases, undergoes resume reviews, SQL query interview tests, and mock tech rounds with our dedicated placement team.

Are weekend batches available for working professionals?

Yes. TGC India offers flexible weekday batches as well as dedicated Saturday-Sunday batches designed specifically for working analysts, developers, and college students.

Do I need prior coding knowledge to enroll in Rohini?

No prior programming background is required. The course starts with relational concepts and basic queries before advancing to complex analytical window functions and query tuning.

Why is SQL essential for developers and data professionals?

SQL remains the universal standard language for managing and querying structured data across nearly all web applications, cloud services, and enterprise software systems worldwide.

SQL Course in Rohini | Database Design & Analytics Training

Customer Churn Prediction

This project focuses on predicting customer churn for a subscription-based service, such as telecom or streaming platforms. Starting with Problem Definition and Dataset Selection, participants define key objectives and choose a dataset containing customer information, subscription details, and historical churn data. The next phase, Exploratory Data Analysis (EDA) and Visualization, involves analyzing patterns, identifying factors influencing churn, and visualizing trends using tools like Matplotlib or Tableau. In Building and Validating Predictive Models, participants apply machine learning algorithms like Logistic Regression, Random Forest, or Gradient Boosting to classify customers as likely to churn or retain. After model validation with metrics like precision, recall, and F1 score, participants proceed to Presenting Insights and Final Deliverables by creating a business report detailing actionable recommendations to reduce churn.

House Price Prediction

In this project, participants predict house prices based on features like location, size, and amenities. The Problem Definition and Dataset Selection phase focuses on understanding key variables and using datasets like Kaggle’s House Price dataset. Through EDA and Visualization, participants analyze correlations between variables and visualize trends such as price variations by location. Using algorithms like Linear Regression or Decision Trees, participants build predictive models, evaluating performance with RMSE (Root Mean Square Error). Finally, Presenting Insights and Final Deliverables includes a detailed report with visualizations and recommendations for buyers or real estate companies.

Retail Sales Forecasting

Participants predict future sales for a retail store, using historical data. Problem Definition and Dataset Selection involves understanding sales trends and selecting datasets with transaction histories. During EDA and Visualization, participants examine seasonality, product trends, and customer behavior. In Building Predictive Models, techniques like ARIMA or LSTM are applied for time series forecasting. Final Deliverables include actionable insights to optimize inventory management and marketing strategies.

Sentiment Analysis on Product Reviews

This project analyzes customer sentiment from online reviews. Dataset Selection includes gathering reviews from platforms like Amazon or Yelp. EDA explores word frequencies and common themes using word clouds. Participants use Natural Language Processing (NLP) techniques and models like Naive Bayes or BERT for Building and Validating Models. Insights and Deliverables provide visualizations and sentiment-based recommendations for product improvements.