The simplest definition of data science is the extraction of actionable insights from raw data. Often referred to as the “oil of the twenty-first century,” our digital data is the most important in the field.
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Data science is being used in a variety of areas, including healthcare, banking, education, supply chain, and others which makes it important for people and students to learn Data Science Courses.
Are you looking for some of the best data science courses to help you enhance your career? Here is a list of the best courses 🤓👉
List of FREE Data Science Courses Online in India:
1. What is Data Science? By Coursera:
This course will introduce you to some data science practitioners and provide an outline of what data science is today. You will learn about the various techniques that businesses can take to begin working with data science.
You will learn about some of the characteristics that set data scientists apart from other professionals. You will also learn about analytics, storytelling, and the critical role that data scientists play in producing an effective final deliverable.
The topics that are covered in this course:
- Defining Data Science and What Data Scientists Do
- Data Science Topics
- Data Science in Business
Duration: Approx. 9 hours to complete
2. Executive Data Science Specialization By Coursera:
Even if you have never worked in data science before, you will learn everything you need to know in four intensive courses to start assembling and running a data science organisation.
The key points of this course are:
- You’ll get a crash course in data science so you’re familiar with the subject and understand your job as a leader.
- You’ll also learn how to build a team with complementary skill sets and roles by recruiting, assembling, evaluating, and developing it.
- You’ll learn about the data science pipeline’s structure, the goals of each stage, and how to keep your team on track throughout.
- Finally, you’ll learn some practical methods that can help you overcome the major obstacles that frequently derail data science initiatives.
There are 5 Courses in this Specialization:
- A Crash Course in Data Science
- Building a Data Science Team
- Managing Data Analysis
- Data Science in Real Life
- Executive Data Science Capstone
Duration: Approximately 2 months to complete
3. Data Science for Business Innovation By Coursera:
The course is a compilation of the essential data science capabilities for executives and middle-management to support data-driven innovation. It is made up of introductory lectures on topics such as big data, machine learning, data valuation, and communication. The course covers data science vocabulary and concepts, tools and approaches, use cases, and success stories.
The course defines Data Science and explains why it is so popular. It examines the value that Data Science can provide, the different types of challenges that Data Science can answer, the distinction between descriptive, predictive, and prescriptive analytics, and the roles of machine learning and artificial intelligence.
The topics covered in the course are:
- Introduction to Data-driven Business
- Terminology and Foundational Concepts
- Data Science Methods for Business
- Challenges and Conclusions
Duration: Approx. 7 hours to complete
4. Data Science with Databricks for Data Analysts Specialization By Coursera:
This specialisation is designed for data analysts who want to broaden their data-working toolkit. Traditionally, data analysts have performed their daily workflows using tools such as relational databases, CSV files, and SQL programming, among others.
In this specialisation, you will use your present talents to learn new ones, allowing you to use modern technologies not traditionally associated with your professions, such as Databricks and Apache Spark. By the end of this speciality, you’ll be able to use Databricks and the most popular machine learning approaches to tackle real-world business challenges.
Topics covered in this course are:
- Apache Spark (TM) SQL for Data Analysts
- Data Science Fundamentals for Data Analysts
- Applied Data Science for Data Analysts
Duration: Approximately 5 months to complete
5. Data Science at Scale Specialization By Coursera:
This Specialization covers intermediate data science concepts. You will gain real expertise with scalable SQL and NoSQL data management solutions, data mining algorithms, and statistical and machine learning ideas.
You will also learn how to visualise data and convey results, as well as investigate legal and ethical issues that occur when working with large data. You’ll apply your new skills to a real-world data science project in the final Capstone Project, which was created in collaboration with the digital internship platform Coursolve.
Topics Covered In this course are:
- Data Manipulation at Scale: Systems and Algorithms
- Practical Predictive Analytics: Models and Methods
- Communicating Data Science Results
- Data Science at Scale – Capstone Project
Duration: Approximately 5 months to complete
6. Practical Data Science with MATLAB Specialization By Coursera:
Completing this speciality will provide you with the knowledge and confidence you need to quickly create practical results in Data Science. This specialisation implies you have domain expertise in a technical discipline as well as some experience with computational tools like spreadsheets. You should have a basic understanding of statistics to be able to complete the courses successfully.
MATLAB will be used throughout this speciality. MATLAB is the go-to choice for millions of engineers and scientists, and it provides the features you need to complete your data science jobs. For the length of the specialisation, you will have free access to MATLAB.
Topics covered in this course:
- Exploratory Data Analysis with MATLAB
- Data Processing and Feature Engineering with MATLAB
- Predictive Modeling and Machine Learning with MATLAB
- Data Science Project: MATLAB for the Real World
Duration: Approximately 5 months to complete
7. Introduction to Clinical Data Science By Coursera:
This course will prepare you to finish the Clinical Data Science Specialization in its entirety. This course will teach you how clinical data is generated, the format of these data, and the ethical and regulatory constraints that these data face. You will also master enough SQL and R programming skills to finish the Specialization.
You will have access to an actual clinical data collection as well as a free, online computing environment for data science hosted by our Industry Partner Google Cloud while taking this course. You will be ready to begin your clinical data science education journey at the completion of this course.
Topics covered In this course are:
- Welcome to the Clinical Data Science Specialization
- Introduction: Clinical Data
- Tools: SQL
- Tools: R and the Tidyverse
Duration: Approx. 8 hours to complete
8. Advanced-Data Science with IBM Specialization By Coursera:
You will have a demonstrated deep understanding of huge parallel data processing, data exploration and visualisation, as well as sophisticated machine learning and deep learning. You will comprehend the mathematical underpinnings of all machine learning and deep learning algorithms.
You may use knowledge in practical use cases, justify architectural decisions, grasp the features of different algorithms, frameworks & technologies & how they affect model performance & scalability. If you take this speciality and receive the Coursera specialisation certificate, you will also receive an IBM digital badge.
This is an ADVANCED LEVEL Course.
The topics covered in this course are:
- Fundamentals of Scalable Data Science
- Advanced Machine Learning and Signal Processing
- Applied AI with DeepLearning
- Advanced-Data Science Capstone
Duration: Approximately 4 months to complete
9. Genomic Data Science Specialization By Coursera:
Because genomics is causing a revolution in medical discoveries, it is critical to be able to better comprehend the genome and exploit data and information from genomic databases. Genomic Data Science is the study of the genome using statistics and data science.
This Specialization introduces the concepts and methods needed to comprehend, evaluate, and interpret results from next-generation sequencing research.
This Specialization is intended to serve as a standalone introduction to genomic data science as well as a suitable complement to a major degree or postdoc in biology, molecular biology, or genetics for professionals in these fields looking to obtain data science experience.
Topics covered in this course are:
- Genomic Data Science Capstone
- Statistics for Genomic Data Science
- Introduction to Genomic Technologies
- Genomic Data Science with Galaxy
- Python for Genomic Data Science
- Algorithms for DNA Sequencing
- Command Line Tools for Genomic Data Science
- Bioconductor for Genomic Data Science
Duration: Approximately 10 months to complete
10. Data Science Math Skills By Coursera:
This course was built for learners who have fundamental math skills but have not studied algebra or pre-calculus. It is aimed to teach learners the basic math they will need to be successful in practically any data science math course. Data Science Math Skills offers the fundamental math on which data science is based, with no extraneous complication, introducing unexpected ideas and math symbols one at a time.
Before going on to more advanced topics, learners who complete this course will have mastered the vocabulary, notation, ideas, and algebra rules that all data scientists must know.
The topics covered in this course are:
- Welcome to Data Science Math Skills
- Building Blocks for Problem Solving
- Functions and Graphs
- Measuring Rates of Change
- Introduction to Probability Theory
Duration: Approx. 13 hours to complete
11. Applied Data Science with Python Specialization By Coursera:
This University of Michigan specialization’s five courses teach students to data science using the Python programming language. This skills-based specialisation is designed for students with a basic knowledge of Python or programming.
You will be able to perform inferential statistical analysis. You’ll be able to tell if a data visualisation is good or awful. Analyze the connectedness of a social network and improve your data analysis using applied machine learning. This is an intermediate level course.
The topics covered in this course:
- Introduction to Data Science in Python
- Applied Plotting, Charting & Data Representation in Python
- Applied Machine Learning in Python
- Applied Text Mining in Python
- Applied Social Network Analysis in Python
Duration: Approximately 5 months to complete
12. SQL for Data Science By Coursera:
This course is intended to provide you with a foundation in the principles of SQL and data manipulation so that you may begin analysing it for data science objectives. This course begins with the fundamentals and assumes you have no prior experience or expertise in SQL. It will build on that foundation by gradually requiring you to write both simple and complicated queries to assist you in selecting data from tables.
You’ll start working with other forms of data, such as strings and numbers, and you’ll talk about how to filter and narrow down your results. You will be able to build new tables and move data into them. You will learn about common operations as well as how to combine data.
The topics covered in this course are:
- Getting Started and Selecting & Retrieving Data with SQL
- Filtering, Sorting and Calculating Data with SQL
- Subqueries and Joins in SQL
- Modifying and Analyzing Data with SQL
Duration: Approx. 14 hours to complete
13. Data Science Fundamentals with Python and SQL Specialization By Coursera:
This IBM Specialization will assist anyone interested in pursuing a career in data science by teaching them the core skills required to get started in this in-demand industry. The speciality consists of four self-paced online courses that will teach you the fundamentals of data science.
These data science prerequisites will be taught to you through hands-on practice with genuine data science tools and real-world data sets. You will have the practical knowledge and skills to delve deeper into Data Science and work on more sophisticated Data Science projects after successfully completing these courses.
The topics covered in this course are:
- Tools for Data Science
- Python for Data Science, AI & Development
- Python Project for Data Science
- Statistics for Data Science with Python
- Databases and SQL for Data Science with Python
Duration: Approximately 6 months to complete
14. Intro to Data Science By Udacity:
The core concepts in data science will be covered in the Introduction to Data Science class, which will include:
- Manipulation of Data
- Statistics and Machine Learning are used to analyse data.
- Data Communication in conjunction with Information Visualization
- Data on a Large Scale — Utilizing Big Data
Instead of focusing on a single topic in-depth, the class will focus on breadth and deliver the themes briefly. This will allow you to experiment with and apply basic data science techniques.
Perks of this course:
- Rich Learning Content
- Interactive QuizzesInteractive Quizzes
- Taught by Industry ProsTaught by Industry Pros
- Self-Paced LearningSelf-Paced Learning
Duration: Approx. 2 Months
15. Essentials of Data Science By Udemy:
This course is intended to provide you with an overview of the three basic areas of Data Science that any good data scientist should be familiar with, and proficiency in these areas can be the key to your success.
After completing this course, you will have a thorough grasp of what Data Science is all about and will be able to decide if it is the correct subject for you. You will also understand what topics are crucial in Data Science and will be able to make informed judgments about where to focus your learning efforts.
The topics included in this course are:
- Course Introduction
- Statistics
- Data Visualization
- Programming
Duration: 1hr 41min
16. Data Science 101 Data Analytics Class Python Bootcamp NYC By Udemy:
The course is not designed to make you an expert in Python Analytics, but rather to expose you to simple code and provide a basic overview of all themes in Data Analytics in Python to help you start a career in Data Science. Python 101 is suggested but not required for this course. This course is designed for beginners and non-programmers.
The topics in this course include:
- Introduction
- Getting And Cleaning The Data
- Wrangling The Data
- Visualization
Duration: 1hr 43min
17. Intro to Data for Data Science By Udemy:
We will learn about data as a foundation for data science in this course. We’ll discover what data is and why it’s vital. We’ll also cover data types, data structures, tabular data, and the data life cycle. By the end of this course, you will have a solid understanding of data and how it is used in data science.
You’ll learn about:
- What data are and why they are important
- How data are used in data science
- The types of data that exist in data science
- How data are represented in a computer
- How to extract information from a table of data
- About the data life-cycle from data collection to action
The topics in this course include:
- Introduction
- Data
- Types of Data
- Data Types
- Tabular Data
- Life Cycle
- Conclusion
Duration: 1hr 1min
18. Maths for Data Science by DataTrained By Udemy:
In Python, you’ll learn how to work with vectors. With this course, you will learn how to use the Python programming language to apply key mathematical ideas related to linear algebra. This course necessitates a basic understanding of math from the tenth grade.
The topics covered in this course:
- Lessons on Math for Data Science & Machine Learning:
- Understand how to work with vectors in Python
- Basis and Projection of Vectors: Understand the Basis and Projection of Vectors in Python
- Work with Matrices: Understand how to work with matrices in Python
- Matrix Multiplication: Understand how to multiply matrices in Python
- Matrix Division: Understand how to divide matrices in Python
- Linear Transformations: Understand how to work with linear transformations in Python
- Gaussian Elimination: Understand how to apply Gaussian Elimination
- Determinants: Understand how to work with determinants in Python
- Orthogonal Matrices: Understand how to work with orthogonal matrices in Python
- Eigenvalues: Recognize how to obtain eigenvalues from eight decompositions in Python
- Eigenvectors: Recognize how to obtain eigenvectors from eigendecomposition in Python
- PseudoInverse: Recognize how to obtain pseudoinverse in Python
Duration: 55min
19. Introduction to Data Science for Complete Beginners By Udemy:
This course will help you become familiar with and comprehend some of the fundamental concepts underlying data science if you have no idea what the discipline of data science is and are seeking a very short introduction to data science. If you are not an expert in data science, attending this course will provide you with a general overview of the topic.
This short course will establish a solid basis for comprehending the most fundamental topics taught in advanced data science courses, and it is ideal if you have no prior knowledge of data science and want to begin learning it from the ground up.
The topics covered in this course are
- Introduction
- Data Science Basics
Duration: 1hr 56min
20. Data Science Crash Course By Udemy:
In this Data Science Crash Course, you’ll learn about Data Science through a comprehensive set of lecture notes and extra books.
You’ll be able to run Python on your laptop and conduct basic Data Science experiments. You will comprehend the distinction between supervised and unsupervised learning, classification and clustering, and the fundamental methods for each. This course will cover Keras and Tensorflow, as well as data visualisation with matplotlib and Dash.
The topics in this course include:
- Introduction
- Fundamentals Of Data Science
- Working With Data
- Data Science Methods
- Visualizing Data
Duration: 51min
Conclusion: Data science is being used in a variety of areas, and the demand for skilled professionals in data science is growing. We hope this article has provided you with some useful information about the data science industry. We hope it solved any other questions about data science courses. Thank you for reading, and we are always excited to provide you with helpful information like this! 🤗🌈
