COVID-19 pandemic has been the game-changer for online learning. The year 2020 proved to be the most crucial year where there has been a rapid upsurge in the number of people getting enrolled in online courses and 2021 is witnessing the same upsurge. Popular platforms like Coursera, edX, Datacamp, Udacity, NPTEL, Pluralsight, and Udemy, among others, remained the most high-in-demand online learning platforms. Data science is among the most popular picks for online learners.
To learn more about data science, read our blog on – What is data science?
There has been a huge demand for the best data science certifications and courses from the best platforms. We have handpicked some of the most trending free data science courses for you. Most of these courses are auditable, meaning you can access them for free but would need to pay some amount to avail the certificate. Nonetheless, this is an excellent opportunity for those who were looking forward to learning data science.
These data science online courses are categorized based on their difficulty level. You can pick the one that suits your business requirements and personal aspirations. Let’s take a look.
Data Science Courses for Beginners
Data Visualization and Building Dashboards with Excel and Cognos by edX (Duration – 4 weeks)
This is one of the most popular online data science courses. In this course, you will learn the basics to create data visualizations and dashboards using both Microsoft Excel and IBM Cognos Analytics.
Eigenvectors and Eigenvalues by Udacity (Duration – 1 week)
In this course, you would learn how to easily calculate Eigenvectors and Eigenvalues in Linear Algebra and use them in machine learning applications.
Basic Data Processing and Visualization by University of California San Diego on Coursera (Duration – 5 Weeks)
In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization.
Foundations of Data Science: K-Means Clustering in Python by University of London on Coursera (Duration – 5 Weeks)
This course would introduce you to the core concepts of Data Science. It focuses on the basic mathematics, statistics, and programming skills required for data analysis tasks.
Security and Privacy for Big Data – Part 1 by EIT Digital on Coursera (Duration – 1 Week)
This course covers the concepts of security in Big Data environments and includes cryptographic principles, mechanisms to manage access controls in the Big Data system.
Security and Privacy for Big Data – Part 2 by EIT Digital on Coursera (Duration – 1 Week)
This course covers the concepts of privacy and data protection in Big Data environments and includes privacy-preserving methodologies, and data protection regulations and concepts in your Big Data system.
Data Science Fundamentals with Python and SQL Specialization by IBM on Coursera (Duration – 5 Months)
The specialization will help you learn foundational skills such as open-source tools and libraries, Python, Statistical Analysis, SQL, and relational databases.
Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions by Rice University on Coursera (Duration – 4 weeks)
You will learn about the concepts of Business Statistics, including the concepts of descriptive statistics, descriptive measures, different Excel functions, statistical distributions, and statistical models, among others.
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Introduction to Data Analytics for Managers on edX (Duration – 8 weeks)
Reproducible Research by Johns Hopkins University on Coursera (Duration – 8 weeks)
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. The course would also cover literate statistical analysis tools for publishing data analyses in a single document, allowing easy data analysis.
Data Science Methodology by IBM on Coursera (Duration – 3 weeks)
The course focuses on a data science methodology, to ensure that the data used in problem-solving is relevant and properly manipulated to address the issues. with the help of this course, you will learn –
– Steps involved in tackling a data science problem
– Steps involved in practicing data science, from forming a concrete business or research problem to collecting and analyzing data, to building a model, and understanding the feedback after model deployment
– How data scientists think
Data Science Math Skills by Duke University on Coursera (Duration – 13 Hours)
Data Science Math Skills covers the core concepts of math for data science. Topics covered include:
– Set theory, including Venn diagrams
– Properties of the real number line
– Interval notation and algebra with inequalities
– Uses for summation and Sigma notation
– Math on the Cartesian (x,y) plane, slope, and distance formulas
– Graphing and describing functions and their inverses on the x-y plane
– The concept of instantaneous rate of change and tangent lines to a curve
– Exponents, logarithms, and the natural log function.
– Probability theory, including Bayes’ theorem
Introduction to Cloud Computing by IBM on Coursera (Duration – 5 weeks)
This course will introduce you to the core concepts of cloud computing. You will learn about various cloud service models (IaaS, PaaS, SaaS), deployment models (Public, Private, Hybrid), and the key components of cloud infrastructure (VMs, Networking, Storage – File, Block, Object, CDN). The course would also cover emergent cloud trends and practices including – Hybrid Multicloud, Microservices, Serverless, DevOps, Cloud Native, and Application Modernization.
Practical Machine Learning by Johns Hopkins University on Coursera (Duration – 5 weeks)
This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications, and will provide basic grounding in concepts such as training and test sets, overfitting, and error rates. The course will also introduce a range of model-based and algorithmic machine learning methods, as well as the entire process of building prediction functions.
Introduction to Computational Thinking and Data Science by MIT (Duration – 8 weeks)
The course is designed for participants with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students write small programs. It uses Python 3.5 programming language.
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership on Coursera (Duration – 4 weeks)
These data science courses will help you understand the end-to-end implementation of machine learning. You will learn how to avoid common management mistakes that hamper machine learning projects.
Managing, Describing, and Analyzing Data from the University of Colorado Boulder on Coursera (Duration – 5 weeks)
Understand the data and learn how to classify it correctly to make correct decisions. You will learn how to describe data both graphically and numerically using descriptive statistics and R software.
Data Science: Machine Learning from Harvard University on edX (Duration – 8 weeks)
The course will help you to learn popular machine learning algorithms, PCA, and regularization by building a movie recommendation system. Besides, you will learn about training data and how to use a set of data to discover potentially predictive relationships.
Cloud computing from IIT Kharagpur on NPTEL (Duration – 8 weeks)
The course covers different aspects of cloud computing. It includes fundamentals, management issues, security challenges, and future research trends.
CS50’s Introduction to Artificial Intelligence with Python by Harvard (Duration – 7 weeks)
This specialized course in Artificial Intelligence with Python is from Harvard. It covers modern artificial intelligence concepts and algorithms.
Artificial Intelligence Search Methods For Problem Solving from IIT Madras on NPTEL (Duration – 12 weeks)
The course discusses a wide variety of search methods that agents can employ for problem-solving.
Data Science: Machine Learning by Harvard on edX (Duration – 8 weeks)
Data Science: Machine Learning by Harvard is a part of the Professional Certificate Program in Data Science. It covers popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
Introduction to Data Science with Google Analytics: Bridging Business and Technical Experts from Future Learn (Duration – 2 weeks)
It will offer you a platform to learn data science through Google Analytics. It helps you to analyze user behavior and website performance. Learn how to create and use tracking codes, and view different types of data it produces.
Data Science for Engineers from IIT Madras on NPTEL (Duration -12 weeks)
It is a full-fledged course for engineers. It aims to introduce R as a programming language, mathematical foundations required for data science. The course also covers first-level data science algorithms and a data analytics problem-solving framework.
Programming for Everybody (Getting Started with Python) by the University of Michigan on Coursera (Duration – 19 Hours)
This course is part of the Python for Everybody Specialization. It covers the basics of programming computers using Python. You will learn how to create a program from a series of simple instructions in Python.
Principles, Statistical and Computational Tools for Reproducible Data Science on edX (Duration – 8 weeks)
The course is designed for students and professionals in biostatistics, computational biology, bioinformatics, and data science.
Intro to Data Science on Udacity (Duration – 2 months)
The course covers foundational topics in data science, including –
- Data Manipulation
- Statistics and Machine Learning
- Data Communication
- Working with Big Data
Introduction to Data Science – Revised by Alison (Duration – 2 – 3 Hours)
You will learn the basics of data science with this course. Get an insight into data science processes, an introduction to machine learning, and learn about data models for structuring data.
Data Science for Business Innovation by EIT Digital on Coursera (Duration – 7 Hours)
If you are a part of executive and middle-management, then this course will be helpful to you in fostering data-driven innovation. Topics cover the essential concepts and intuitions on data needs. It also covers data analysis, machine learning methods, respective pros and cons, and practical applicability issues.
Data Science: Linear Regression by Harvard University on edX (Duration – 8 Weeks)
In this course, you will learn one of the most common statistical modeling approaches in data science, which is using R to implement linear regression. There is no prerequisite to take up this course.
Linear Regression and Modeling by Duke University on Coursera (Duration – 9 Hours)
This course introduces simple and multiple linear regression models. This will help you to learn how to assess the relationship between variables in a data set and a continuous response variable.
Intro to Data Analysis via Udacity (Duration – 6 weeks)
The course will help you to explore a variety of datasets and will use Python libraries like NumPy, Pandas, and Matplotlib to understand the concepts better.
SQL for Data Science by University of California, Davis on Coursera (Duration – 14 Hours)
This course is designed for those who wish to learn about the fundamentals of SQL and working with data. You will also learn about working with different types of data like strings and numbers, and methods to filter and get insights.
Executive Data Science Specialization by Coursera (Duration – 2 months)
This program is mainly designed for business executives and leaders with no experience in data science. With this course, business leaders can understand their roles as a leader and be able to develop and work with a team.
Leaders will learn the structure of the data science pipeline, understand the goals, and develop the skills to overcome the real-world data science project challenges.
Databases and SQL for Data Science by IBM on Coursera (Duration – 15 Hours)
This course will help you to create and access a database instance on the cloud, write basic SQL statements, and access databases from Jupyter using Python, among others.
Data Science Courses at Intermediate Levels
Machine Learning: Unsupervised Learning by Georgia Institute of Technology on Udacity
This course will help you to use Unsupervised Learning approaches to find structure in unlabeled data. This includes randomized optimization, clustering, and feature selection and transformation.
Python for Data Engineering Project by IBM on edX (Duration – 1 week)
You would learn how to use Python for data engineering tasks including large data set extraction from multiple sources using web scraping and APIs, data transformation to gain valuable business insights, and data extraction in multiple file formats from different sources, etc.
Applied Statistics with Python by Southern New Hampshire University on edX (Duration – 16 weeks)
The content covers three components of Applied Statistics, including – descriptive statistics and probability, hypothesis tests and confidence intervals, and linear regression analysis.
Hands-on Text Mining and Analytics by Yonsei University on Coursera (Duration – 6 weeks)
With the help of this course, you can learn key components of text mining techniques including text preprocessing, sentiment analysis, and topic modeling.
Bayesian Statistics: From Concept to Data Analysis by the University of California on Coursera (Duration -10 Hours)
With the help of this course, you will understand the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.
Causal Diagrams: Draw Your Assumptions Before Your Conclusions on edX (Duration – 9 Weeks)
In this course, you will translate expert knowledge into a causal diagram, learn how to draw causal diagrams under different assumptions, use causal diagrams to identify common biases, etc.
Genomic Data Science Specialization by Johns Hopkins University on Coursera (Duration – 10 months)
This Specialization will help you to understand, analyze, and interpret data from next-generation sequencing experiments. Learn working with tools like the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy
Data Science with Databricks for Data Analysts Specialization by Databricks on Coursera (Duration – 5 months)
This course would enable you to learn new technologies like Databricks and Apache Spark and you will complete a series of hands-on lab assignments and projects. The lab assignments will allow you to test-drive Databricks and Apache Spark to streamline today’s most popular data science workflows.
Data Science: Statistics and Machine Learning Specialization by Johns Hopkins University on Coursera (Duration – 5 Months)
This course covers topics like statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you will apply the skills learned by building a data product using real-world data.
Data Science by IBM on Coursera (Duration – 6 Hours)
The course covers key topics like data science introduction and algorithms, Big data & Hadoop, Neural networks and Deep Learning, application of Data science, and more.
Regression Models by Johns Hopkins University on Coursera (Duration – 5 weeks)
This course covers regression analysis, least squares, and inference using regression models. It also focuses on modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
Exploratory Data Analysis by Coursera (Guided Project)
It is a project-based course that will help you to learn exploratory data analysis techniques and create visual methods to analyze trends, patterns, and relationships in the data. You will apply EDA on a real-world dataset.
Machine Learning Using SAS Viya by SAS on Coursera (Duration – 4 weeks)
The course covers the theoretical foundation for different techniques of supervised machine learning models. This course uses Model Studio, the pipeline flow interface in SAS Viya. You will learn to prepare, develop, compare, and deploy advanced analytics models.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning from deeplearning.ai on Coursera (Duration – 4 weeks)
This course will teach you the best practices for using TensorFlow, a popular open-source framework for machine learning.
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership on Coursera (Duration – 4 weeks)
This course will help you understand the end-to-end implementation of machine learning. You will learn how to avoid common management mistake that hampers machine learning projects.
Statistical Inference and Modeling for High-throughput Experiments from Harvard University on edX (Duration – 4 weeks)
In this specialized data science course, will explore different statistics topics. These topics include multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values, and exploratory data analysis. You will then learn statistical modeling and its application in high-throughput data.
Data Visualization with Python (Duration – 10 hours)
Data Visualization with Python will cover various techniques for presenting data visually. Apart from this, the course also covers data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.
Data Science For Business Leaders from Altius
The course is designed for business leaders, including executives, strategists, and innovators. This course will help them to drive a competitive advantage using data science.
AI Workflow: Business Priorities and Data Ingestion by IBM on Coursera (Duration – 6 Hours)
The course is a part of the IBM AI Enterprise Workflow Certification specialization. It covers the scope of the specialization and prerequisites, the concept of design thinking, and the basics of scientific thinking. You will also get to build a data ingestion pipeline using Python and Jupyter notebooks.
Fundamentals Of Artificial Intelligence from IIT Guwahati on NPTEL (Duration – 4 weeks)
With this course, you can have a basic understanding of problem-solving, knowledge representation, reasoning, and learning methods of AI. It will also offer an overview of the principles and practices of AI to address complex real-world problems.
Statistics and R on edX (Duration – 4 weeks)
The course will offer you an introduction to basic statistical concepts and R programming skills, required for data analysis in the field of life sciences.
Data Analysis with R by Facebook on Udacity (Duration – 2 Months)
The course is a part of the Data Analyst Nanodegree program. It is an approach towards summarizing and visualizing the important characteristics of a data set. You will also get to understand the data’s underlying structure and variables.
Essential Math for Machine Learning: R Edition by edX (Duration – 6 weeks)
With this machine learning course, you will learn about the essential mathematical foundations for machine learning and artificial intelligence. To complete this course successfully, you should have basic knowledge of math, along with some programming experience.
Databases and SQL for Data Science by IBM on Coursera (Duration – 15 Hours)
The course will introduce you to relational database concepts, which can help you learn and apply foundational knowledge of SQL. To take this course, you should have knowledge of SQL, Python, or programming.
Applied Data Science with Python Specialization by the University of Michigan on Coursera (Duration – 5 months)
This data science course is a series of 5 courses that would help you to gain new insights into your data. You would learn to apply data science methods and techniques and acquire analytical skills.
Data Analytics in Health – From Basics to Business on edX (Duration – 4 weeks)
The course covers the application of data analytics and big data in ensuring better diagnosis, care, and curing.
Intro to Data Science by Udacity (Duration – 2 Months)
This data science course will cover the basics of data science, including –
- Statistics and Machine Learning for Data Analysis
- Data Communication
- Information Visualization
- Working with Big Data
Getting and Cleaning Data by Johns Hopkins University via Coursera (Duration – 19 Hours)
This course will cover the basic ways to obtain data from the web, APIs, databases, and other sources in various formats. You will also learn the basics of data cleaning and ways to achieve tidy data. This course also includes the basics needed for collecting, cleaning, and sharing data.
The Data Scientist’s Toolbox by Johns Hopkins University via Coursera (Duration – 13 Hours)
This free data science course covers the basics of data analysis tools. It also covers a practical introduction to the tools for version control, markdown, git, GitHub, R, and RStudio.
Process Mining: Data science in Action by Eindhoven University of Technology via Coursera (Duration – 22 Hours)
The course will shed light on the key analysis techniques in process mining and various process discovery algorithms. It will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory across different applications.
Data Science Courses for the Experts
Advanced Machine Learning on Google Cloud Specialization by Google Cloud Training on Coursera (Duration – 3 Months)
This specialization focuses on advanced machine learning topics using Google Cloud Platform, offering hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs.
Introduction to Data Science and Basic Statistics for Business by TecdeMonterreyX on edX (Duration – 4 Weeks)
This course will help you to acquire statistical methods and technological tools for decision-making in business. It covers the analysis of statistical elements of information, as well as concepts and statistical foundations for data science applications.
Advanced Linear Models for Data Science 2: Statistical Linear Models by Johns Hopkins University on Coursera (Duration – 6 Hours)
This class is an introduction to least squares from a linear algebraic and mathematical perspective.
Developing AI Applications on Azure on Coursera (Duration – 6 Hours)
This course introduces the concepts of Artificial Intelligence and Machine learning. It will discuss machine learning types and tasks, and machine learning algorithms.
Advanced Statistics for Data Science Specialization by Johns Hopkins University on Coursera (Duration – 22 Hours)
This specialization includes Mathematical Statistics bootcamps, specifically concepts, and methods used in biostatistics applications such as probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.
Python for Data Science on edX (Duration – 8 weeks)
Google Cloud Computing Foundations from IIT Kharagpur, Google Cloud on NPTEL (Duration – 8 weeks)
The course covers the basics of cloud, big data, and machine learning and its applicability to the Google Cloud Platform. 26 labs on Qwiklabs are a part of the course.
Deep Learning in Computer Vision by National Research University Higher School of Economics on Coursera (Duration – 17 hours)
This data science course is part of the Advanced Machine Learning Specialization. It covers topics like computer vision, modern deep learning models, image and action recognition, new image generation, among others.
Python for Data Science from IIT Madras on NPTEL (Duration – 4 weeks)
Learn how to use python programming for solving data science problems.
Predictive Analytics and Data Mining by University of Illinois at Urbana-Champaign on Coursera (Duration – 24 hours)
This data science course is designed for businesses and managers to enable them to apply data analytics to real-world challenges. It will help them to identify the ideal analytical tools, understand valid and reliable ways to collect, analyze, visualize data, and utilize data in decision-making.
AI Workflow: Machine Learning, Visual Recognition and NLP by IBM (Duration – 7 hours)
The program is designed for existing data science practitioners with expertise in building machine learning models. It aims to sharpen the skills of building and deploying AI in large enterprises. It includes lectures and case studies focusing on natural language processing and image analysis to provide a realistic context for the model pipelines.
Statistical Inference and Modeling for High-throughput Experiments by Harvard on edX (Duration – 4 weeks)
This course covers various statistics topics such as multiple testing problems, error rate controlling procedures, false discovery rates, q-values, and exploratory data analysis. You will then learn about statistical modeling and its application in high-throughput data.
Big Data, Genes, and Medicine by The State University of New York (Duration – 23 hours)
You will get to explore different topics on Big Data Science and Bioinformatics, including Big Data analytics on real datasets in a healthcare and biological context. It will also help you in preparing data, interpreting and visualizing the results, and sharing them.
Knowledge-Based AI: Cognitive Systems by Georgia Tech on Udacity (Duration – 7 Weeks)
This is a core course in artificial intelligence. It covers structured knowledge representations, problem-solving methodologies, planning, decision-making, and learning methods. Learn to design knowledge-based AI agents and human cognition, and build a relationship between knowledge-based artificial intelligence.
Artificial Intelligence for Robotics by Georgia Tech Masters on Udacity (Duration – 2 Months)
The course covers basic methods in Artificial Intelligence, including probabilistic inference, planning and search, localization, tracking, and control, with a focus on robotics. The program also covers programming examples and assignments for building self-driving cars.
Python for Data Science by the University of California, San Diego on edX (Duration – 4 Weeks)
In this advanced data science course, you will get to explore topics like Introduction to Spyder, Python for Data Science, Variables and Datatypes, Operators, Tuples, Dictionary, etc.
Big Data and Education by the University of Pennsylvania on edX (Duration – 8 Weeks)
The course will explore different methodologies for educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You will learn how and when to apply these methods.
Applied AI with DeepLearning by IBM on Coursera (Duration – 22 Hours)
The data science course will help you to understand different aspects of deep learning and models. It will also cover the usage of these modes by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.
Hope this list helps you in making your decision to pick the most suitable data science courses. All the best!
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