10+ Free Data Structures and Algorithms Online Courses with Certificate 2025

Learning Data Structures and Algorithms (DSA) is crucial for developers in 2025. It forms the foundation for solving complex problems efficiently, especially in industries that depend on machine learning, artificial intelligence, and big data. 

Understanding DSA not only enhances your problem-solving abilities but also makes you highly competitive in the job market. For example, in machine learning, understanding DSA helps optimize algorithms, manage data efficiently, and improve model performance.

In this blog, we’ll dive into why learning Data Structures and Algorithms is essential for any developer, and what topics the free course will cover. You’ll also learn how understanding DSA can help you enhance your employability! 

Top 10+ Free Data Structures and Algorithms Courses in 2025

Data Structures and Algorithms (DSA) are the backbone of computer science and are crucial for excelling in various tech roles. If you’re aiming for a career in software development, system architecture, or data science, understanding DSA is essential. However, specialized courses can guide you through this complex field. 

For example, if you're targeting software engineering roles, courses focusing on algorithm optimization and dynamic programming are invaluable. If you're moving into machine learning, DSA courses that elaborate on graph theory, data structures for large datasets, and efficient search algorithms will be more relevant.

Machine learning professionals with a strong grasp of data structures and algorithms are highly sought after for their ability to solve complex problems. If you're looking to boost your skills in AI and ML, our top-rated courses will help you master essential concepts like DSA while diving deep into machine learning techniques. 

• Masters in Artificial Intelligence and Machine Learning
• Executive Diploma in Machine Learning and AI
• Master’s Degree in Artificial Intelligence and Data Science

However, before we jump to the detailed explanation for each course, here’s a brief overview highlighting their components:

1. upGrad’s Data Structures and Algorithms (DSA)

This comprehensive Data Structures and Algorithms course focuses on the key foundational concepts crucial for solving complex problems efficiently. It is essential for software development, data science, and technical interviews. 

By learning algorithms, sorting techniques, and understanding how to work with arrays, linked lists, and other structures, you will be equipped with the problem-solving and coding skills needed for high-level software roles. The course provides a blend of theory and hands-on coding, preparing you for both job interviews and real-world applications.

Duration: 50 hours (self-paced).

Format: Online, Self-paced video lessons, coding exercises, quizzes, and practical examples.

Certification: Official certificate upon successful completion, valid for use on LinkedIn and resumes.

Topics Covered:

• Algorithm Analysis: Learn to evaluate algorithms based on their time and space complexity.
• Searching and Sorting: Understand essential searching algorithms like binary search and advanced sorting algorithms like Quick Sort.
• Arrays and Linked Lists: Understand the fundamentals of arrays and linked lists, including how to insert, delete, and manipulate data structures.
• Stacks & Queues: Learn stack and queue operations for handling data in LIFO and FIFO order.
• Recursion: Delve into recursion techniques for breaking down problems into smaller, manageable sub-problems.
• Graph Algorithms: Study graph traversal algorithms (DFS, BFS) and shortest path algorithms (Dijkstra, A*).
• Dynamic Programming: Learn to solve complex problems using dynamic programming.

Benefits:

• Mastering Problem-Solving: Develop a structured approach to solving complex problems, making your solutions more efficient and scalable.
• Improved Coding Skills: Enhance your ability to write optimized, bug-free code that is production-ready, which is crucial for coding interviews and real-world projects.
• Interview Preparation: This course prepares you for competitive coding and tech interviews, as DSA forms the core of most coding interviews at top companies.
• Career Growth: Understanding DSA is fundamental for advancing in software engineering roles, data science, and Artificial Intelligence, ensuring you stand out in the competitive tech industry.
• Real-World Applications: Learn how to apply DSA concepts to build scalable and efficient systems for real-world problems in industries like finance, healthcare, e-commerce, and tech.

Practical Example: Graph algorithms, such as Dijkstra’s shortest path algorithm, play a crucial role in building GPS systems for route planning. For example, Google Maps utilizes graph-based algorithms to determine the fastest route, taking into account real-time traffic data to refine driving directions.

Also Read: What are Data Structures & Algorithm?

2. upGrad’s Advanced SQL: Functions and Formulas

This course dives into complex SQL concepts, including window functions, query optimization, and stored functions, equipping you with the skills necessary to handle large datasets and write optimized, high-performance queries.

It is designed for those seeking to advance their SQL skills beyond the basics. You'll understand advanced SQL techniques, including window functions, stored procedures, and complex query optimizations.

Duration: 11 hours of expert-led, self-paced learning.

Format: Online, free self-paced course with video lessons, practical assignments, quizzes, and real-life examples.

Certification: Upon successful completion of the course, you will receive an official and verifiable e-certificate from upGrad, which can be shared on LinkedIn or added to your resume to boost your career credibility.

Topics Covered:

• Window Functions: Learn how to use advanced window functions like RANK(), DENSE_RANK(), and PERCENT_RANK() to perform complex calculations over a set of rows.
• Query Optimization: Master techniques to optimize your queries for better performance, using indexing, query refactoring, and partitioning strategies.
• Stored Functions & Cursors: Learn to implement reusable logic in SQL using stored functions and explore the role of cursors in processing result sets efficiently.
• Joins & Nested Queries: Explore how to optimize joins and implement nested queries to retrieve and process large datasets faster and more effectively.
• Indexing: Understand how to design and implement indexes to improve query speed and manage large datasets with ease.
• Programming Constructs: Learn how to implement advanced SQL programming constructs like CASE statements and IF conditions to manipulate and query data dynamically.

Benefits:

• Improve Query Efficiency: Learn query optimization techniques to enhance runtime, ensuring your queries execute faster and handle large datasets seamlessly.
• Master Advanced SQL Functions: Gain expertise in SQL functions that improve performance, such as window functions, ranking, and partitioning for complex queries.
• Real-world Application Readiness: Apply learned concepts to solve real-world problems like handling concurrent users, complex reporting, and real-time updates in high-traffic systems.
• Boost Your Job Prospects: Earn a certification that highlights your expertise in advanced SQL, making you stand out in interviews and boosting your career opportunities in data roles.
• Learn to Handle Large Datasets: Master techniques for managing and processing large datasets, improving data retrieval speed, and enhancing overall database performance.
• Enhance Backend Performance: Discover how to optimize your backend systems and databases for maximum efficiency, a crucial step in developing scalable, high-performance applications.

Practical Example: For a large e-commerce platform like Amazon, window functions can be used to track user activity, such as tracking the ranking of products over time based on their purchase history, without recalculating the entire dataset. Using indexing and optimization techniques, you can reduce query execution time for product searches, enhancing the user experience for millions of users in real-time.

Also Read: Data Structures in Javascript Explained: Importance, Types & Advantages

3. upGrad’s Analyzing Patterns in Data and Storytelling

This course introduces the fundamental techniques required for transforming raw data into insightful and persuasive narratives. Through data analysis, pattern recognition, and effective visualization, this course enables learners to communicate complex findings clearly. 

Learning data storytelling is essential in roles where data must be used to inform decision-making, making this skill set highly valuable in industries such as business intelligence, marketing, and strategic management.

Duration: 6 hours of self-paced learning.

Format: Online, video-based lessons with interactive exercises, real-world case studies, and practical applications. The course utilizes hands-on coding challenges to reinforce key concepts.

Certification: Upon completion, learners will receive a verifiable e-certificate from upGrad.

Topics Covered:

• Analyzing Patterns & Generating Insights: Learn to identify key patterns in data, validate hypotheses, and generate actionable insights.
• Documenting Insights: Master documenting insights using the Pyramid Principle and logical structuring to ensure clarity and effectiveness.
• Data Visualization & Storytelling: Transform raw data into visual stories using techniques for both qualitative and quantitative data visualization.
• Pyramid Principle: Apply this logical framework to organize data insights and effectively communicate findings.
• Logical Flow & Communication: Structure your insights with a logical flow and choose the most effective language for clear and impactful communication.
• Data Visualization for Quantitative & Qualitative Variables: Use best practices for visualizing both numerical data and non-numerical insights to tell a compelling story.

Benefits:

• Comprehensive Data Storytelling Skills: Gain the ability to tell data-driven stories that connect insights with action.
• Enhanced Communication: Learn how to communicate complex findings clearly and concisely to non-technical audiences.
• Practical Learning with Real-World Applications: This course provides hands-on experience in data visualization and storytelling frameworks used by industry leaders.
• Boost Your Professional Profile: Earn a certification that can be added to your resume or LinkedIn profile to showcase your data communication expertise.
• Increased Career Opportunities: Master skills that are crucial for roles in data analysis, business intelligence, marketing, and leadership positions.

Practical Example: Imagine working as a marketing analyst where you need to present a report on consumer behavior. By applying the data storytelling techniques learned in this course, you can present sales trends, customer demographics, and behavioral patterns through interactive graphs and visuals that captivate the audience. 

You can use the Pyramid Principle to summarize key findings first, followed by supporting data, helping senior management understand the data-driven insights quickly and effectively.

Also Read: Top Software Developer Skills You Need to Have: How to Improve them

4. upGrad’s Basics of Inferential Statistics

This course provides an in-depth introduction to inferential statistics, a critical area of statistical analysis that enables conclusions about populations based on sample data. 

By focusing on probability, distributions, hypothesis testing, and sampling techniques, professionals can make accurate predictions and data-driven decisions. These are essential elements that are foundational in fields like data analysis, finance, market research, and healthcare analytics.

Duration: 15 hours (Self-paced)

Format: Self-paced online learning with video lessons, quizzes, and practical exercises.

Certification: Upon successful completion, you will receive a verified certificate from upGrad, which can be shared on LinkedIn and used to enhance your resume.

Topics Covered:

• Probability: Introduction to random variables, probability distributions, and expected value calculations to understand how to quantify uncertainty in data.
• Discrete Distributions: Study of binomial and uniform distributions, covering the modeling of random processes with two possible outcomes and events in fixed trials.
• Continuous Distributions: Exploration of continuous data through probability density functions (PDFs) and normal distributions, highlighting how to calculate areas under the curve to determine probabilities.
• Central Limit Theorem (CLT): Understanding the CLT's significance in sample means distribution and its application to estimating population parameters.
• Confidence Intervals: Learning to calculate and interpret confidence intervals for sample means and understanding their role in estimating unknown parameters.
• Sampling Methods: Covering different sampling techniques like random, stratified, and systematic sampling for data collection, ensuring representativeness in statistical analysis.

Benefits:

• Build Core Analytical Skills: Understand the key statistical techniques used in data-driven decision-making, enhancing your ability to analyze complex datasets.
• Practical, Hands-On Experience: Apply your learning through real-world datasets, understanding the practical application of concepts like hypothesis testing and probability.
• Industry-Relevant Knowledge: Learn inferential techniques that are used by data analysts, market researchers, and financial planners to make decisions from sample data.
• Certification for Career Growth: Showcase your competency in statistics with a recognized certificate, boosting your credibility in fields like data science and market analysis.
• Learn at Your Own Pace: The self-paced nature allows you to tailor your learning to your schedule, revisiting challenging topics whenever needed.

Practical Example: When launching a new product, companies can use sample surveys to apply the central limit theorem to estimate customer preferences. The sampling method helps ensure that the sample reflects the broader population, allowing companies to predict potential market success with statistical confidence.

Also Read: Data Structures in Python

5. upGrad’s Data Science in E-Commerce

The Data Science for E-Commerce course equips learners with the critical skills of data analysis, machine learning, and statistical modeling to optimize e-commerce operations. This training is essential for those seeking to apply data science techniques to solve real-world problems in e-commerce, retail, and digital commerce industries.

By utilizing data analytics, recommendation systems, pricing strategies, and marketing optimization, this course enables students and professionals to drive sales and enhance customer experiences in the competitive e-commerce sector. 

Duration: 13 hours of learning (self-paced)

Format: 100% online, self-paced, with practical projects, case studies, and assessments.

Certification: Official and verifiable e-certificate from upGrad upon successful completion of the course.

Topics Covered:

• Data Analytics in E-commerce: Analyze business models, revenue streams, and how data-driven strategies optimize e-commerce operations.
• Recommendation Systems: Learn content-based filtering, collaborative filtering, and implementation techniques using Python to enhance user experience and boost sales.
• Price Markup & Markdown: Study how price adjustments impact revenue, understand strategic pricing, and implement markup/markdown frameworks.
• Four Force Model for Price Optimization: Learn how demand elasticity, competition, internal economics, and category dynamics help optimize product pricing.
• Sales Factors & Market Mix Modeling: Evaluate advertising, pricing, and product assortment to optimize revenue using regression models.
• Impact of KPIs: Predict and optimize marketing impact on sales with basic linear models and advanced approaches like Koyck and Distributed Lag.
• A/B Testing: Use A/B testing to optimize web pages, analyze results, and apply data-driven decision-making techniques to refine marketing strategies.

Benefits:

• Industry-Relevant Skills: Explore key concepts like data analysis, recommendation systems, and price optimization for real-world e-commerce strategies.
• Hands-On, Practical Training: Apply your skills to real-world case studies and interactive projects that simulate actual e-commerce challenges.
• Personalized Learning: Learn at your own pace with flexible scheduling, designed to fit into busy professional and academic life.
• Certifications for Career Advancement: Earn a verifiable certificate from upGrad to strengthen your resume and LinkedIn profile.
• Comprehensive Knowledge: Understand the entire e-commerce lifecycle, from inventory management to customer personalization and fraud prevention.

Practical Example: You’ll build a recommendation system similar to Amazon’s, where you will implement collaborative and content-based filtering algorithms using Python to suggest products based on user behavior and preferences. This will help optimize conversion rates by providing personalized shopping experiences that increase customer retention and drive sales.

Also Read: How Data Mining in Retail & E-commerce is changing the future

6. upGrad’s Introduction to Data Analysis using Excel

This course offers a comprehensive introduction to the key techniques in Excel for performing effective data analysis, including data manipulation, visualization, and complex formulas. It is designed to equip beginners and professionals with the tools required to handle data-driven tasks in real-world settings. 

As industries increasingly rely on data-driven decision-making, understanding Excel for analysis is a crucial skill for enhancing your career in data science, analytics, marketing, operations, and finance.

Duration: 9 hours of self-paced learning.

Format: Online, self-paced course with hands-on exercises.

Certification: Free Certification upon completion, verifiable and shareable on platforms like LinkedIn.

Topics Covered:

• Introduction to Excel: Learn how to navigate Excel, understand its interface, and leverage its built-in functionalities for efficient data handling.
• Data Analysis in Excel – I: Dive into foundational data analysis, covering sorting, filtering, and basic report generation techniques.
• Data Analysis in Excel – II: Get to know about advanced functions, such as logical formulas, and data visualization tools, ensuring your data is insightful and actionable.
• PIVOT Tables: Learn how to summarize large datasets and perform complex analyses with PIVOT tables for actionable insights.
• Slicers: Gain experience using slicers to filter data dynamically and enhance user interactivity in reports.
• VLOOKUP: Understand the concepts of VLOOKUP and its related functions to search for data across tables efficiently.
• Formulas and Functions: Understand and implement basic and advanced Excel formulas, including SUMIF, COUNTIF, and INDEX-MATCH.
• Charting and Graphs: Learn how to create various types of charts (line, bar, pie) to represent data insights visually.

Benefits:

• Learn for Free: Acquire in-demand Excel skills with no cost involved, ideal for beginners aiming to enter the data analysis field.
• Build Job-Ready Excel Skills: Gain proficiency in creating dynamic reports and dashboards, which are critical in marketing, finance, and analytics roles.
• Boost Your Resume & LinkedIn: The course completion certificate can be added to your profile, making you stand out to recruiters in data-driven roles.
• Flexible Learning: Learn at your own pace with no deadlines, making this course ideal for working professionals or students.
• Gain Practical Experience: Get hands-on experience with real-life data scenarios, preparing you for actual data analysis challenges in the workplace.

Practical Example: You can use Excel's PIVOT Tables to analyze sales performance across different regions in a retail company. For example, a retail business could use pivot tables to break down sales by region, identify top-selling products, and track inventory performance. This would allow decision-makers to take action, such as adjusting marketing strategies or optimizing inventory based on real-time sales data.

7. upGrad’s Introduction to Database Design with MySQL

This course provides a comprehensive foundation in database design, with a focus on MySQL. It covers essential database management concepts, including relational schemas, normalization, entity-relationship (ER) models, and SQL queries. Designed for beginners, this course provides practical, hands-on experience with MySQL Workbench, enabling you to structure, create, and manage relational databases. 

This course provides a solid foundation in the fundamental concepts essential for database management and design, whether you're starting from scratch or seeking to enhance your database knowledge. 

Duration: 8 hours of self-paced learning.

Format: Free online course with a structured curriculum, video lessons, hands-on exercises, and quizzes.

Certification: Upon successful completion, you will receive a verifiable certificate from upGrad.

Topics Covered:

• Introduction to Database Design: Learn the importance of database design and the role MySQL plays in building efficient systems.
• Entity-Relationship Models (ER Models): Understand how to represent data and relationships between data using ER diagrams to model real-world scenarios.
• Normalization and Data Integrity: Learn how to structure data to eliminate redundancy and ensure data consistency with normalization techniques (1NF, 2NF, 3NF).
• SQL Queries and Statements: Understand basic and advanced SQL commands, including SELECT, INSERT, UPDATE, DELETE, and JOIN operations.
• DDL (Data Definition Language) and DML (Data Manipulation Language): Gain hands-on knowledge of defining database structures and manipulating data efficiently with DDL and DML commands.
• MySQL Workbench: Practice directly with MySQL Workbench to create databases, tables, and manage queries in a real-world tool.
• Relational Database Design: Understand how to design a database schema for real-world applications while ensuring scalability and maintainability.

Benefits:

• Explore Core Database Concepts: Learn essential database principles like relational modeling, SQL queries, and normalization.
• Hands-On MySQL Workbench Experience: Use the industry-standard MySQL Workbench to execute real-time queries and database operations.
• Improved Problem-Solving Skills: Applying SQL and database design techniques helps you develop problem-solving skills that can be applied to any data-related challenge.
• Enhanced Career Opportunities: Completing this course enhances your profile with a verifiable certificate that makes you an attractive candidate for roles in data analytics.
• Foundational Knowledge for Advanced Roles: This course is ideal for beginners and also prepares you for more advanced database courses.

Practical Example: A hospital uses MySQL to manage patient records, doctors’ schedules, and billing systems. With this course, you will learn to structure a database that handles patient details, appointment histories, and transactions while ensuring the integrity and security of sensitive information. 

Also Read: MySQL vs. MongoDB: Difference Between SQL & MongoDB

8. upGrad’s Programming with Python: Introduction for Beginners

This course provides a robust foundation in Python, focusing on core programming concepts essential for software development, data science, and automation. It covers the basics of Python, including data types, control flow, object-oriented programming (OOP), and data structures like lists, dictionaries, and tuples. 

The course is ideal for individuals seeking hands-on experience in coding, as it develops problem-solving skills, prepares learners for entry-level tech roles, and lays the foundation for learning advanced Python libraries used in data science and machine learning.

Duration: 13 hours of learning. Self-paced, with step-by-step lessons designed for complete beginners.

Format: The course includes interactive lessons, quizzes, and hands-on projects to reinforce learning. It’s designed for easy integration into your schedule, allowing you to learn at your own pace.

Certification: Upon successful completion, you will receive an official certification from upGrad, which can be shared on your LinkedIn profile or included in your resume.

Topics Covered:

• Python Basics: Learn how to set up the Python environment, write your first program, and understand syntax and variable types.
• Data Types: Introduction to basic Python data types, including strings, integers, floats, and booleans, and how to perform operations on them.
• Control Flow: Understand decision-making using if-else conditions, loops, and logical operators to control the flow of a Python program.
• Data Structures: Dive into Python’s built-in data structures like lists, dictionaries, tuples, and sets to store and manage data efficiently.
• Functions: Learn to create and utilize functions to organize code into reusable blocks, and understand function parameters, scope, and return values.
• Object-Oriented Programming (OOP): Introduction to OOP concepts like classes, objects, inheritance, and polymorphism to structure code in a modular way.
• File Handling: Learn how to read from and write to files, enabling persistent data storage in Python applications.
• Error Handling: Understand how to handle exceptions and errors in your code to prevent crashes and ensure smooth program execution.

Benefits:

• Build Job-Ready Skills: This course helps you understand the fundamental skills needed for tech roles in software development and data analysis.
• Free Certification: Complete the course and earn a shareable certificate to showcase your skills on LinkedIn or your resume.
• Hands-on Learning: Apply your knowledge through practical exercises and real-world projects, ensuring a strong grasp of Python.
• Industry-Aligned: Gain skills that are in high demand in the tech industry, including OOP, data manipulation, and software development best practices.
• No Cost: This free course removes financial barriers, allowing anyone to learn and gain valuable programming knowledge at no cost.

Practical Example: For instance, you could create a Personal Finance Tracker that tracks income, expenses, and savings. The app would store and categorize financial data in Python’s data structures, such as dictionaries and lists, allowing you to filter and analyze your expenses based on user-defined criteria.

Also Read: Top 36+ Python Projects for Beginners and Students to Explore in 2025

9. upGrad’s Logistic Regression for Beginners

This course dives into the fundamentals of Logistic Regression, one of the most widely used machine learning algorithms for binary classification tasks. It provides a clear pathway to understanding the key concepts and real-world applications, from building models to interpreting them, using Python and popular libraries such as scikit-learn. 

Logistic regression is crucial for decision-making in industries such as healthcare, finance, and marketing, where predicting binary outcomes (e.g., disease presence vs. absence, fraud detection) is essential.

Duration: 17 hours of structured, self-paced learning.

Format: Online video lectures, hands-on coding tutorials, quizzes, and practical projects using Python.

Certification: Upon completion, you’ll receive a verifiable certificate, which can be added to your resume or LinkedIn profile to boost your employability in data science, machine learning, and analytics.

Topics Covered:

• Univariate Logistic Regression: Introduction to binary classification, sigmoid function, likelihood function, and model building using Python.
• Multivariate Logistic Regression (MLR-I & II): Extension of univariate logistic regression to multiple predictors.
• Model Evaluation: Learn evaluation metrics like accuracy, precision, recall, ROC curves, and AUC.
• Feature Selection Methods (RFE, VIF, p-value check): Use techniques like RFE, VIF, and p-value checks to improve model performance and remove irrelevant features that might distort predictions.
• Applications of Logistic Regression: Real-world applications in areas like customer segmentation, marketing, fraud detection, and medical diagnostics using logistic regression.

Benefits:

• Hands-on Learning with Python: Build practical experience in implementing logistic regression models with Python and scikit-learn to analyze real datasets.
• Free Certificate: Receive an official, verifiable certificate upon course completion to demonstrate your proficiency to potential employers.
• Industry-Relevant Skills: Gain skills directly applicable to high-demand roles in data science, machine learning, and analytics, focusing on predictive modeling.
• Focus on Real-World Applications: Learn how logistic regression is used in various industries like healthcare for predicting diseases or in finance for detecting fraud.
• Self-Paced Learning: Learn at your own pace with lifetime access to the course materials, perfect for busy professionals or students.

Practical Example: In healthcare, logistic regression can be used to predict whether a patient has a specific condition based on factors like age, blood pressure, and cholesterol levels. By training a logistic regression model on patient data, healthcare providers can identify individuals at high risk, enabling early intervention and more effective resource allocation.

Also Read: Difference Between Linear and Logistic Regression: A Comprehensive Guide for Beginners in 2025

10. Great Learning’s Data Structures in C

This Data Structures in C course is designed for those looking to build a solid foundation in the implementation and optimization of common data structures using the C programming language.

It covers fundamental concepts, such as arrays, stacks, queues, and more advanced structures, including heaps and binary trees. A thorough understanding of these structures is critical for efficiently solving algorithmic problems and excelling in technical interviews for software engineering roles.

Duration: 3 hours

Format: Video lectures with hands-on demonstrations and examples. You will implement data structures directly in C, reinforcing the theoretical knowledge with practical coding exercises.

Certification: Upon successful completion of all modules and the quiz, you will receive a certificate, which can be added to your resume or LinkedIn profile.

Topics Covered:

• Arrays in C: Understanding static data storage, accessing elements via indices, and performing operations like insertion and deletion.
• Linked List: Dynamic memory allocation, implementing a singly linked list, and operations like insert, delete, and traverse.
• Stack: Implementation of stack using arrays/linked lists, push/pop operations, and use cases like expression evaluation.
• Queue: FIFO data structure implementation, circular queues, and its applications in scheduling tasks and buffering.
• Binary Tree and Binary Search Tree: Tree structures for hierarchical data, traversal algorithms (pre, in, post-order), and searching in a BST.
• Heap: Min-heap and max-heap, heap sort, and priority queues for efficient sorting and managing data with priority.
• Hashing: Hash maps and hash tables, collision resolution techniques, and applications like implementing caches and fast lookups.

Benefits:

• Comprehensive understanding of key data structures: You will learn how each data structure works, its time and space complexity, and practical implementation in C.
• Hands-on implementation in C: Solidifies theoretical knowledge with practical coding exercises and real-world examples.
• Interview Preparation: Comprehending these structures prepares you for technical interviews where these concepts are often tested.
• Optimized Problem Solving: Learn to apply these structures efficiently for problem-solving, reducing time complexity and enhancing performance.
• Real-World Applicability: Gain the ability to design systems that need efficient data management and manipulation like search engines or real-time systems.

Practical Example: In real-time systems such as networking or OS scheduling, a queue data structure is essential for managing tasks or packets that must be processed in order. Similarly, a binary search tree enables the construction of efficient databases, enabling searching, inserting, and deleting records to be performed in logarithmic time.

Also Read: What are Data Structures in C & How to Use Them?

11. ScholarHat’s Free Data Structures & Algorithms Certification Course

This course offers a comprehensive introduction to fundamental and advanced data structures and algorithms (DSAs). It focuses on optimizing your coding skills by understanding the theory and practical applications of algorithms, essential for tackling real-world problems, coding interviews, and competitive programming. 

This course ensures you develop a deep understanding of DSA to write clean, efficient, and optimized code, regardless of whether you're a beginner or intermediate coder. 

Duration: 50 Hours of self-paced learning, with lifetime access to materials.

Format: The course consists of 83 detailed video lessons, 18 quick notes for rapid review, and 54 hands-on labs to reinforce practical learning.

Certification: Upon successful completion, you will receive a free certificate to showcase your thorough understanding of Data Structures and Algorithms. This certificate is valuable for enhancing your resume and LinkedIn profile, helping you stand out in the competitive job market.

Topics Covered:

• DSA and Big O: Introduction to the fundamentals of DSA and analyzing algorithm efficiency using Big O notation to optimize solutions for time and space complexity.
• Array Data Structures: A deep dive into array-based structures, covering operations like insertion, deletion, and search, and their time complexities.
• String Data Structures: Learn how to handle string manipulation, including searching, pattern matching, and understanding string algorithms.
• Searching Algorithms: Gain expertise in linear and binary search algorithms, their use cases, and how they are optimized for performance.
• Sorting Algorithms: Understand sorting algorithms like Bubble Sort, Merge Sort, Quick Sort, and their time complexity analysis.
• Stack and Queue: Learn to implement stack and queue data structures, essential for handling recursive problems and managing tasks with LIFO and FIFO principles.

Benefits:

• Enhances problem-solving skills: Develop the ability to design efficient algorithms and optimize code for time and space efficiency.
• Prepares for technical interviews: Understand DSA to tackle technical interview questions from top tech companies confidently.
• Builds scalable applications: Learn how to structure data efficiently to support scalable and performant applications.
• Improves coding proficiency: Strengthen your coding abilities by learning how to implement algorithms and data structures effectively.
• Industry-relevant certification: Earn a certification that adds credibility to your resume, making you job-ready for software development roles.

Practical Example: Optimizing a search function in an e-commerce platform. By utilizing binary search or hash-based algorithms, you can significantly reduce the time complexity of product search functionality in an online store, thereby improving the user experience.

Also Read: Top 58 Coding Interview Questions & Answers 2024 [For Freshers & Experienced]

12. Cognitive Class’s Exploratory Data Analysis (EDA) for Data Science and ML

Exploratory Data Analysis (EDA) is a critical first step in any data science or machine learning project. In this course, you will learn the fundamental techniques to analyze and understand data using EDA. This will provide insights into the characteristics of the data, including distributions, outliers, and relationships between features.

These skills are essential for building robust and accurate machine learning models, and the knowledge gained in this course will help improve prediction models, guide feature engineering, and inform data-driven decisions.

Duration: 45 Minutes

Format: Hands-on guided project with interactive lessons and exercises.

Certification: Earn a certificate upon completion to demonstrate your proficiency in EDA, which can enhance your resume and portfolio.

Topics Covered:

• Introduction to EDA: Learn the importance of exploratory data analysis in the machine learning lifecycle, focusing on data exploration before building predictive models.
• Statistical Analysis: Apply basic statistical concepts, such as mean, median, and standard deviation, to summarize data.
• Visualization Techniques: Utilize plots such as histograms, box plots, and scatter plots to identify trends and relationships within the data.
• Handling Missing Data: Learn techniques like imputation and removal to manage missing data and ensure model accuracy.
• Outlier Detection: Identify and handle outliers using statistical methods or visual cues from the data.
• Feature Engineering Basics: Understand the importance of transforming data into more useful formats, enhancing the quality of input features for better model performance.
• Data Preprocessing for Regression and Classification: Learn how to preprocess data specifically for regression and classification tasks.

Benefits:

• Practical Hands-on Experience: Apply EDA techniques on real datasets, preparing you for data exploration tasks in your job or projects.
• Better Data Understanding: Gain a deeper understanding of your data, leading to more informed decisions about model building and feature selection.
• Improved Model Accuracy: Utilize EDA to identify patterns, biases, or correlations that lead to more accurate models, thereby enhancing predictive accuracy.
• Skills for Advanced Projects: Learn EDA fundamentals that are applicable for more advanced data science and machine learning projects.
• In-demand Skills: Data scientists and machine learning engineers often perform EDA as a preliminary step, making it a critical skill for your career growth.

Practical Example: Imagine you are building a predictive model to forecast customer churn for an e-commerce platform. By using EDA, you could identify features that correlate with churn (e.g., customer activity patterns), detect any outliers in spending behavior, and handle missing data, all of which are crucial for improving the accuracy of your predictive model.

Enhance your expertise in ML and AI with upGrad’s Executive Diploma in Machine Learning and AI. Apply Data Structures and Algorithms concepts to real-world ML challenges, and prepare to become an AI Engineer. Enroll in India's leading program, access the latest 2025 curriculum, and join an alumni network from top companies like Amazon, Microsoft, and more.

Also Read: Top 50+ Data Structures and Algorithms Interview Questions!

Now that you've discovered some of the top free DSA courses, let's explore why learning Data Structures and Algorithms with a certificate is so crucial for your career growth.

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Why Learn a Free DSA Course with a Certificate?

Thoroughly understanding Data Structures and Algorithms (DSA) is essential for any aspiring developer. A free DSA course with a certificate provides a structured learning path, boosts your credibility, and makes you stand out in the competitive job market without any financial investment. 

Here are some of the key benefits of enrolling in a free DSA course with a certificate.

• Interviews:
  A solid grasp of DSA helps you confidently tackle complex coding problems, making you stand out in technical interviews.In technical interviews, companies like Google or Facebook often ask coding questions that require DSA knowledge. 
  For example, solving problems like finding the shortest path in a graph using algorithms like Dijkstra or A* demonstrates your ability to solve complex problems.
• Efficiency:
  Thoroughly understanding DSA enables you to write optimized, efficient code with reduced time complexity. When working on large-scale projects, such as optimizing an e-commerce platform, efficient algorithms are crucial. 
  For example, using binary search to look up data in sorted arrays instead of linear search can drastically reduce the time complexity, especially with large datasets.
• Scalability:
  Strong DSA knowledge ensures you can design scalable solutions that handle growing data and user demands seamlessly.Think of a social media platform like Instagram, where you need to design systems that handle millions of users. 
  Implementing data structures such as hash maps or balanced trees helps in efficiently handling large datasets, enabling fast lookups and updates.
• Salary:
  Expertise in DSA is highly valued, directly linking to high-demand job roles with attractive compensation.Companies like Amazon, Microsoft, and Uber value developers who can efficiently solve complex problems using data structures and algorithms (DSA). 
  For instance, understanding the implementation of graph algorithms to model transportation networks or recommend products boosts your value. Advanced DSA skills increase your employability and lead to higher salaries due to the in-demand expertise.
• Problem-Solving:
  DSA sharpens your ability to break down complex problems and find optimal solutions efficiently.If you work at a company building a recommendation engine, you may face complex challenges like determining the most relevant products for a customer. 
  DSA techniques, such as dynamic programming or greedy algorithms, help you break down these problems and find optimal solutions efficiently.
• Improved Coding Skills:
  Practicing DSA enhances your coding abilities, enabling cleaner and more efficient code, as well as better debugging skills. Coding challenges, such as building an autocomplete feature in a search engine, require knowledge of trees and tries. 
  As you learn DSA, you’ll develop better coding practices that emphasize clean, efficient code. Your debugging skills will also improve, as you’ll be able to quickly identify issues in your algorithm’s logic and fix them effectively.

Also Read: Dynamic Programming Algorithms for Data Structures Explained

Now that you understand the importance of understanding DSA, let's explore how upGrad's structured courses can help you gain deeper insights and apply these concepts effectively in real-world scenarios.

Learn Data Structures and Algorithms with upGrad!

In 2025, gaining a solid understanding of Data Structures and Algorithms (DSA) is essential for developers seeking to stay competitive in the rapidly evolving tech landscape. With industries relying on AI, machine learning, and large-scale applications, understanding DSA gives you the tools to build efficient, scalable, and optimized solutions. Strong DSA knowledge helps you solve complex problems, improve your coding speed, and ace technical interviews at top companies.

To solidify your DSA skills and gain a deeper understanding, upGrad offers structured learning paths, hands-on projects, and expert-led mentorship. These courses are designed to bridge any gaps in your knowledge, boost your confidence, and prepare you for high-demand roles in the tech industry.

Explore some additional advanced courses designed to enhance your expertise and empower you with the required skills:

• Advanced Generative AI Certification Course
• Executive Programme in Generative AI for Leaders
• DBA in Emerging Technologies with Concentration in Generative AI
• Master’s Degree in Artificial Intelligence and Data Science
• Professional Certificate Program in Cloud Computing and DevOps

Uncertain about which course to choose for your specific career path? upGrad offers personalized guidance to help you gain clarity and confidence in your journey. Visit an upGrad center for immersive, hands-on training that equips you with in-demand skills and practical insights, ensuring you're prepared for real-world challenges and career growth.

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References:
https://fortune.com/education/articles/software-engineering-jobs/