Detailed SQL Syllabus Structure for Data Science Certification

Did you know? Google has introduced an open-source AI Toolbox designed to connect generative AI applications with various databases, including PostgreSQL, MySQL, and Cloud SQL for SQL Server. 

This tool addresses challenges related to scaling, complex connections, and security, and is compatible with frameworks like LangChain.

The SQL syllabus for data science is a key part of preparing for your data science certification. It covers everything from querying databases to advanced data manipulation techniques. However, many struggle with understanding exactly what’s required to pass the SQL certification syllabus. 

This article breaks down the essential topics, offering a clear roadmap to help you master SQL skills. By the end, you'll know exactly what to focus on in the SQL syllabus and be ready to pass your data science certification exam. 

SQL Syllabus: Complete Breakdown for Data Science Certification

In 2025, the global volume of data is projected to reach a staggering 175 zettabytes, equivalent to 175 trillion gigabytes. Managing and making sense of this massive data explosion is where SQL shines as an essential skill for data scientists and analysts. 

Unlike a standard SQL syllabus that focuses mainly on basic querying, data manipulation, and database management, an SQL course tailored for data science dives deeper into practical applications essential for analytics.

Key Subjects in SQL Courses

SQL is often called the universal language of data because it’s the essential tool for data scientists to access, manipulate, and analyze data stored in virtually every database. Its core topics, like querying, joining tables, and aggregating data, are critical for transforming raw data into actionable insights. 

Here’s a detailed breakdown of the essential SQL topics required to succeed in data science: 

1. Basic SQL Commands

This topic is the foundation of SQL; without understanding the basic commands, working with databases would be impossible.

• Topics Covered:
  • SELECT: Retrieve data from one or more tables.
  • FROM: Specify the table from which to retrieve data.
  • WHERE: Filter records based on specific conditions.
  • GROUP BY: Group rows that share a common property.
  • ORDER BY: Sort the result set by one or more columns.
• Tools You'll Use: MySQL, PostgreSQL, SQLite, SQL Server
• Skills You'll Gain: Proficiency in data retrieval and filtering, organizing data, and sorting results to help in analytical tasks such as trend analysis and summarization. 

2. Data Filtering & Aggregation Techniques

Filtering and aggregation are the core techniques for extracting valuable insights from raw datasets.

• Topics Covered:
  • WHERE: Apply filters to query results based on conditions.
  • LIKE: Use pattern matching to filter data.
  • IN, BETWEEN: Filter data within a range or set of values.
  • COUNT, SUM, AVG: Aggregate data for metrics like total and average.
  • GROUP BY: Aggregate data by categories. 
• Tools You'll Use: MySQL, PostgreSQL, SQL Server
• Skills You'll Gain: Strong ability to filter and aggregate data to extract critical business insights, like finding the average revenue per customer or total sales for a product category. 

3. Table Relationships and JOIN Operations

Understanding JOIN operations is crucial to working with relational databases, where data is spread across multiple tables.

• Topics Covered:
  • INNER JOIN: Retrieves rows from both tables where there's a match.
  • LEFT JOIN: Retrieves all rows from the left table and matching rows from the right table.
  • RIGHT JOIN: Retrieves all rows from the right table and matching rows from the left table.
  • FULL JOIN: Retrieves rows when there is a match in either table.
  • CROSS JOIN: Produces a Cartesian product of both tables. 
• Tools You'll Use: PostgreSQL, MySQL, SQL Server, Oracle
• Skills You'll Gain: Ability to combine and analyze data from multiple tables, ensuring comprehensive insights into business operations, customer behavior, and trends. 

4. Subqueries and Views

These advanced SQL tools are essential for handling complex queries and improving performance.

• Topics Covered:
  • Subqueries in SELECT, WHERE, FROM: Write nested queries to retrieve data that is dependent on another query.
  • Views: Create virtual tables to simplify complex queries and reuse common query logic.
  • Correlated Subqueries: Subqueries that reference outer query data. 
• Tools You'll Use: PostgreSQL, MySQL, SQL Server
• Skills You'll Gain: Advanced querying capabilities, including how to create efficient subqueries and views to manage large datasets and improve query readability and maintainability. 

5. Data Types, Constraints, and Modifications

These are fundamental concepts for maintaining data integrity and ensuring your database is well-structured and reliable.

• Topics Covered:
  • Data Types: INTEGER, VARCHAR, DATE, BOOLEAN, etc.
  • Constraints: PRIMARY KEY, FOREIGN KEY, NOT NULL, UNIQUE
  • Data Modifications: INSERT, UPDATE, DELETE 
• Tools You'll Use: MySQL, PostgreSQL, SQL Server
• Skills You'll Gain: In-depth knowledge of how to define, modify, and enforce data integrity rules, ensuring that your database performs optimally and remains reliable as you manipulate large datasets. 

6. Performance Tuning and Indexing

Optimizing your SQL queries is essential when working with large datasets or databases.

• Topics Covered:
  • Indexing: Creating indexes on frequently queried columns to speed up search operations.
  • Query Optimization: Analyzing and optimizing query execution plans.
  • EXPLAIN Command: Understanding query execution and identifying performance bottlenecks.
  • Partitioning: Breaking large tables into smaller, more manageable pieces for faster queries. 
• Tools You'll Use: MySQL, PostgreSQL, SQL Server, Oracle
• Skills You'll Gain: Proficiency in optimizing SQL queries for performance, which is critical for large-scale data analysis, reducing execution time, and improving overall system efficiency. 

7. Normalization and Database Design

Proper database design ensures that your database remains efficient, scalable, and free of redundancy.

• Topics Covered:
  • First, Second, Third Normal Forms (1NF, 2NF, 3NF): Understanding how to eliminate redundant data and improve data integrity.
  • Boyce-Codd Normal Form (BCNF): Further refinement of 3NF to eliminate even more potential anomalies.
  • Denormalization: When and why to introduce redundancy for performance optimization. 
• Tools You'll Use: MySQL, PostgreSQL, DBDesigner, ER/Studio
• Skills You'll Gain: Expertise in designing relational databases that are both efficient and scalable, ensuring they remain easy to maintain and query over time. 

8. Transactions and Concurrency Control

Learning about transactions and managing data consistency in multi-user environments is essential for maintaining database integrity.

• Topics Covered:
  • Transactions: COMMIT, ROLLBACK, SAVEPOINT
  • ACID Properties: Atomicity, Consistency, Isolation, Durability
  • Concurrency Control: Managing simultaneous access to data by multiple users. 
• Tools You'll Use: PostgreSQL, MySQL, SQL Server
• Skills You'll Gain: A thorough understanding of how to manage and maintain data consistency and integrity, even in complex, multi-user environments.

Start with the basics, build your foundation, and gradually dive into more advanced concepts as you go. To ensure you gain hands-on experience and solidify your learning, enrolling in structured courses will be crucial. 

Choose a course that aligns with your current skill level and career goals, and start applying what you've learned to real-life data. 

Several course options are available to help you get started or advance your skills in SQL, each tailored to different levels of expertise and career goals.

• Training & Certification: Short, focused courses that provide industry-recognized certifications.
• Undergraduate Programs: Comprehensive degree programs that cover SQL along with other key data science concepts.
• Post-Graduate Programs: Advanced courses designed for those seeking in-depth knowledge and specialized skills in data science.

Here’s a list of free SQL courses by upGrad to consider, focusing on options that can help you build your skills and advance in data science.  

These free SQL courses from upGrad are a great starting point for sharpening your skills and gaining practical experience. 

As you progress, consider taking up certifications to validate your expertise further and stand out in the competitive data science field. Certifications not only enhance your credibility but also open up more advanced opportunities for career growth. 

SQL Course Syllabus: Certification Programs

SQL certification programs typically cover core concepts such as writing and optimizing queries, managing and designing databases, data modeling, and ensuring data security. They often include practical assessments on retrieving, filtering, joining, and aggregating data, as well as working with multiple tables and advanced SQL functions.

Let’s look at some top SQL certification programs to help you get started.  

After earning your SQL certification, don’t let your momentum stop, consider advancing to a Postgraduate (PG) Diploma program in data science or analytics. 

They provide hands-on experience that can propel you into specialized, high-impact roles in the data industry.

SQL Course Syllabus: PG Diploma Programs

Before enrolling in a PG Diploma program, evaluate your current skills and career goals. Make sure you have a solid foundation in SQL basics and some hands-on experience with data manipulation and analytics. 

When choosing a program, consider factors like curriculum depth, practical projects, industry relevance, and opportunities for real-life application. 

Let’s take a closer look at these programs:   

Completing a PG Diploma in SQL and Data Science positions you to take on advanced roles in data analytics, database management, and machine learning. After graduation, you can pursue specialized certifications, contribute to real-life projects, or even explore leadership opportunities in data-driven teams. 

To keep your skills sharp and stay ahead, follow a structured learning path. The next section provides curated resources and guidance to help you grow and succeed in your data career. 

SQL Learning Path: Step-by-Step Roadmap

The demand for skilled SQL professionals in data science is soaring. Data scientist roles are projected to grow by 36% between 2023 and 2033, much faster than the average for all occupations. However, the field is highly competitive and requires more than basic knowledge.

Start by focusing on the basics, then progressively dive into more complex topics, making sure to practice as you go. Let’s look at the steps you can take to advance your SQL skills: 

Step 1: Start with the Basics of SQL for Data Science

Before diving deep into data science, you need to understand how SQL works and how to query data. 

• Learn Basic Syntax: Start with simple SELECT queries and gradually introduce WHERE, ORDER BY, and filtering techniques.
• Build Small Projects: Create simple databases for projects like movie or book catalogs and practice writing queries to fetch specific information.
• Practice on Platforms: Use LeetCode, HackerRank, or SQLZoo to solve basic SQL problems and challenges.
• Free Resources: Take CS50's Introduction to Computer Science by Harvard to get a solid foundation in SQL. 

Step 2: Learn Data Filtering and Aggregation for Analysis

Filtering and aggregation are key skills in data science, allowing you to extract valuable insights from large datasets. 

• Work with Real Datasets: Find datasets (like customer sales, weather data, or survey results) and practice filtering and aggregating data using SQL commands like WHERE, COUNT, SUM, AVG, and GROUP BY.
• Build Reporting Dashboards: Create a simple dashboard for sales or customer data where you aggregate total sales by product or region.
• Practice with Kaggle Datasets: Use Kaggle’s public datasets to practice filtering and aggregation. For instance, calculate total revenue, average purchase per customer, or region-specific trends.
• Free Resources: Learning SQL by Alan Beaulieu (good book to go deeper into these techniques). 

Step 3: Master JOINs and Table Relationships

Working with multiple tables is crucial in data science. JOIN operations allow you to bring together data from different sources. 

• Build Relational Databases: Design an e-commerce or customer service database where you link tables for customers, orders, and products using JOINs.
• Practice Complex Queries: Combine data from different tables to get meaningful insights, such as calculating customer lifetime value by joining customer data with purchase history.
• Explore Sample Databases: Use databases like Chinook or Sakila to practice JOIN operations. These databases simulate real-world scenarios and are great for hands-on learning.
• Free Course: SQL for Data Science by UC Davis (covers JOINs and table relationships in-depth). 

Step 4: Dive into Subqueries, Views, and Indexing

As your datasets grow, you’ll need advanced techniques like subqueries, views, and indexing to manage and optimize data. 

• Create Subqueries: Start by writing subqueries to filter data dynamically based on other query results. For example, extract customers who made the highest purchases within a certain period using a subquery.
• Use Views for Efficiency: Create views to simplify complex queries and reuse common logic. For instance, create a view that calculates average revenue per customer and use it in multiple reports.
• Optimize Queries with Indexing: Learn how to use indexing to speed up data retrieval, especially with large datasets. Practice indexing frequently queried columns like customer_id or product_name.
• Free Resource: SQL Performance Explained by Markus Winand (focuses on indexing and query optimization). 

Step 5: Understand Data Normalization and Database Design

Efficient database design ensures your data is structured, scalable, and easy to manage. This is crucial when working with big data. 

• Normalize Databases: Apply 1NF, 2NF, and 3NF normalization rules to create efficient databases and eliminate data redundancy.
• Design Scalable Databases: Practice designing a normalized database for a business application, like inventory management, to handle millions of records efficiently.
• Work on Real Projects: Build a database that supports real-time applications, such as an e-commerce store, and apply normalization techniques.
• Free Course: Database Design for Mere Mortals by Michael J. Hernandez (teaches you the principles of effective database design). 

Step 6: Learn Transactions and ACID Properties

In data science, maintaining data consistency is crucial, especially when working in multi-user environments. ACID properties ensure data integrity and safe transactions. 

• Practice Transactions: Implement COMMIT, ROLLBACK, and SAVEPOINT commands in your SQL projects to ensure that multiple queries are handled as a single unit of work.
• Simulate Real-life Use Cases: Create a project that involves multiple users accessing and modifying the same data (e.g., an online booking system), and use transactions to maintain data consistency.
• Work with ACID: Understand and apply ACID properties when working on large data science projects that require strong consistency (like in financial systems).
• Free Resource: SQL for Data Scientists by Renee M. P. Teate (covers ACID properties and real-life applications).

Step 7: Work with Window Functions for Advanced Data Analysis

Window functions are powerful tools for advanced data analysis, such as calculating running totals or rankings over a set of rows related to the current row. 

• Practice Window Functions: Use window functions like ROW_NUMBER(), RANK(), and PARTITION BY to rank customers, products, or sales over time.
• Real-Life Projects: Create projects that require you to analyze trends, such as calculating monthly sales running totals or ranking top-performing customers.
• Free Course: Advanced SQL: Window Functions by Programming with Mosh (YouTube channel, a great resource to learn advanced querying techniques). 

Step 8: Apply SQL Knowledge in Data Science Projects

At this point, you’re ready to apply everything you’ve learned in real-world data science projects. This is where you’ll consolidate your skills and start solving complex data problems. 

• Capstone Projects: Work on projects like building an analytics dashboard or data pipeline, integrating SQL with Python, Tableau, or Power BI for data visualization and reporting.
• Collaborate on GitHub: Join open-source projects or contribute to data science repositories where SQL is used for large-scale data analysis.
• Leverage Real-life Data: Use real-life datasets (such as sales, customer behavior, or marketing campaign data) to conduct analyses and provide actionable insights using SQL queries.
• Free Resource: Data Science for Business by Foster Provost and Tom Fawcett (explains how to connect SQL techniques to data science concepts).

Step 9: Stay Current and Keep Practicing

To remain competitive in the fast-evolving field of data science, it’s crucial to keep honing your SQL skills and stay up-to-date with emerging tools and technologies. 

• Engage with the Community: Join SQL and data science forums, attend webinars, and follow industry blogs to stay updated.
• Experiment with New Tools: Work with cloud databases (like Amazon RDS, Google BigQuery) and NoSQL databases to broaden your skill set.
• Keep Practicing: Regularly solve SQL challenges and contribute to Kaggle competitions to apply your knowledge in real-world scenarios.

Once you’ve completed your SQL courses, the learning journey doesn’t stop there. To truly excel, you should continue building on your foundation by exploring more advanced SQL topics and expanding into related areas like data modeling, machine learning, and business intelligence. 

SQL remains the core skill, but mastering these additional concepts will prepare you for competitive roles such as data analyst, data scientist, or data engineer, where complex data manipulation and analysis are key. 

Also Read: SQL For Data Science: Why Or How To Master Sql For Data Science

Conclusion

For many learners, the challenge lies in finding a focused approach that connects the dots between theory and practice. SQL can seem daunting, but with a structured approach, hands-on practice, and targeted resources like those provided by upGrad, you can break through that barrier and build real expertise. 

 Not sure where to go next with your SQL journey? upGrad’s personalized career guidance can help you explore the right learning path based on your goals. You can also visit your nearest upGrad center and start hands-on training today!  

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