15 Best Data Science Courses Online

Data science has been one of the fastest-growing fields in recent years.

It is a field that offers great job prospects and high earning potential, but it can be hard to know where to start.

It affects our everyday lives, from the ads we see when checking out at a grocery store to how well (or not) our national football team is doing and what it means for your wallet or purse.

This means that you need to learn data science, and learning about it is just a Google search away.

Luckily for all of us, there are many MOOCs (online courses) on the subject available now.

This means that if you’re interested in becoming a Data Scientist or expanding your knowledge, this post will help you choose which course might be best for you.

I have selected courses from Datacamp, Coursera, edX, Udemy, Codecademy, and Udacity which I consider to be the best out there at this time.

Some of these are beginner-level while others provide a more in-depth look so choose wisely.

Here are 15 of the best online data science courses from around the web:

Best Data Science Courses Online

1. Data Science for Everyone – Datacamp

Data Science for Everyone is a free and interactive introduction to the essentials of data science.

You’ll learn about SQL, R, and Python libraries like pandas and scikit-learn, the data science process itself (e.g., question formulation, data collection), as well as core concepts such as bias & variance tradeoff and model evaluation.

This data science course has four chapters which are Introduction to Data Science, Python for Data Analysis, Time Series, and Working with Big Datasets.

By the end of this course, you’ll have a practical understanding of how to use R programming tools for data analysis.

You will also gain experience in the basics of writing functions using built-in commands as well as creating your own customized functions.

This course provides a good introduction to the R programming language and covers many of its packages.

These include data types and object classes in R, procedures for importing datasets into R, visualizing relationships among variables with base graphics and ggplot, using statistical modeling techniques such as linear regression models.

This course is free for unlimited use without a subscription or registration with an organization. The cost of this course will be shown before payment information is requested.

2. Data Scientist with Python – Datacamp

This course is a wonderful choice for those looking to become data scientists as it covers all of the basics from setting up your Python environment to using R and SQL.

In addition, you will learn how to use the dplyr package in R, NumPy library in Python with examples on Machine Learning & Statistical Methods such as Classification Algorithms, Clustering, and Regression Algorithms.

It is a fantastic way for beginners to learn about data science with real-world examples that can be replicated on their own computer or with the click of a button in Amazon Cloud.

Another highlight is the last section where you will learn how to use Python, R, and SQL together so that you can analyze your data interactively with one tool and then feed it into another for further analysis or visualization.

You’ll also get an introduction to optimization algorithms (finding minima using gradient descent) and clustering algorithms (k-means).

In total, this career track has 29 courses and over 24 hours of on-demand video.

3. IBM Data Science Professional Certificate – Coursera

This course will provide you with an introduction to IBM’s suite of data science tools.

You’ll learn how to use the Watson Developer Cloud and work on projects that highlight key concepts in computer programming, statistics, and machine learning.

This course is designed for beginners who are interested in applying their data science skills in the cloud to solve complex business problems.

You’ll learn how IBM’s Data Science Experience platform enables you to discover, prepare, analyze and visualize data, and then share your findings with other analysts or non-technical users within your organization.

The course will help you become familiar with how to use the Data Science Experience and Natural Language APIs.

You’ll also get an understanding of how to use statistics in data science with the R programming language, the most popular open-source statistical software used by data scientists worldwide.

This course is intended for participants who are new to cloud development or have some experience using other IDEs like Eclipse or Netbeans.

The only prerequisite is basic knowledge of programming in either Python, Ruby, or Java.

4. Become a Data Scientist – Udacity

In this course, you will learn how to become a data scientist by learning from industry experts and working on projects that solve real-life problems.

You’ll build practical skills in computer science fundamentals (including Python programming), statistics (e.g., A/B testing), and machine learning (clustering and decision trees).

To get the most out of this course, you should already have some programming experience.

The course is free for those who enroll in the Machine Learning Engineer Nanodegree program or with GitHub Student Developer Pack.

The best thing about the Udacity Nanodegree is that it is more likely to land the job.

The Nanodegree program in Udacity’s school of data science is a full-stack data science course that covers everything from statistics and machine learning to databases, cloud computing, web development, and visualizations.

The Machine Learning Engineer Nanodegree program is not available for free but you can get a GitHub Student Developer Pack for this course for free.

The program is organized around three main roles in the data science field: Business Analyst, Data Analyst, and Data Scientist.

The programs include a number of data science projects, which is the best part of this course as you will learn to build real-world machine learning models and end-to-end deep learning applications.

This program can be completed by anyone with an aptitude for math and programming but it would take more time if you are completely new to coding.

To learn machine learning, you would need math knowledge up to the level of Calculus I and basic programming skills in Python or R.

The stack is offered by Udacity and jointly developed with industry leaders like Google, AT&T, and Nvidia.

It includes several projects, quizzes, and coding assignments that will help you master machine learning algorithms, data mining techniques, and statistical modeling approaches.

You will get to work on real-life projects with industry partners like Kaggle and Facebook.

With 17+ projects, this course is the best data science online tutorial created by experts in machine learning (clustering and decision trees).

5. Data Science Specialization – Coursera

This specialization is created by the University of Washington and hosted on Coursera.

You are required to take four courses in total: Introduction to Data Science, Machine Learning, Data Analysis, and Big Data tools for data science.

The lectures are taught by top instructors from the University of Washington and the online courses are accessible to everyone.

The specialization will guide you through all steps in Data Science, from getting your feet wet with course one, studying machine learning algorithms, and building a model using the Python programming language for course two.

You will also learn about adding database knowledge such as NoSQL databases or Relational Database systems on data storage and querying for course three.

The last two courses will cover data visualization using Python, JavaScript, and DHTML for web-mapping applications in the cloud (Amazon Web Services).

The specialization starts with an intensive introduction to the R programming language.

It will cover R programming, data visualization, and statistical inference.

You will learn how to solve real-world problems with the methods of Data Analytics that include machine learning algorithms for structured and unstructured datasets in course two.

In addition, you have an option to take a bonus course on Financial Computing if interested.

You are going to do projects in R on various datasets provided by them.

The specialization is designed around a single overarching goal: turning you into a professional Data Scientist capable of getting results from analyzing real-world big data using standard tools and techniques.

What these tutorials do is give you a solid understanding of how to use the Python programming language for data munging and analysis.

It will also show you various libraries in Python that are commonly used by Data Scientists, including but not limited to NumPy, SciPy, Pandas, Matplotlib, Seaborn, Scikit-learn.

6. Applied Data Science with Python Specialization – Coursera

The first course of the specialization is a great starting point for those who want to enter this data science field.

It covers topics such as regression, classification algorithms, clustering, and dimensionality reduction using Python libraries.

On the other hand, if you are more interested in learning how to process and visualize data with Python libraries such as Matplotlib and Seaborn, then DS_AIPython_Visualization might be a better fit for your goals.

The course also includes exercises on Jupyter Notebooks along with video lectures.

It doesn’t include statistics for data science and Machine Learning algorithm, instead, it focuses on using Python tools for data analysis.

It’s a four-week course that will introduce you to the basics of Machine Learning with Python libraries such as NumPy, Pandas, Matplotlib, and Scikit Learn.

The founder of this organization is Andrew Ng who has been known for his work on machine learning algorithms.

This course will give you a basic knowledge of many machine learning libraries.

Data analysis is not an easy task, if you are facing issues with it then this course might be perfect for you as it gives step-by-step instructions on how to do data analysis using Python only.

7. Programming for Data Science with Python – Udacity

This course covers how to use Python to work with data. It is a Nanodegree program that teaches you how to teach yourself.

You will learn how to clean data, work with it, visualize the results, and create machine learning models in Python.

In addition to learning how to represent data, you will also learn how to store it.

There are many ways to store data, but you will learn how to use two of the most popular databases for your purpose: SQLite and MongoDB.

This course contains six projects that teach you how to cleanse real-world datasets using Python libraries like Pandas, NumPy, SciKit Learn, and StatsModels; visualize the data using Matplotlib, Seaborn, and ggplot; and create predictive models with machine learning algorithms.

The course will also show you how to fetch real-world datasets from multiple sources such as SQL/NoSQL databases, web APIs, or CSV files and then load them into Python’s in-memory analytics engine Pandas.

This course is full of practical, real-world projects such as building a stock market predictor, finding the best flight route, and predicting real estate prices in Boston.

By the end of this course, you’ll be able to build your own models from scratch using raw data and Python’s scikit learn library for machine learning tasks.

8. Professional Certificate in Data Science – edX

This is a great certificate program in data science designed for professionals.

The course takes about six months to complete and enrolls students from around the world.

It consists of four projects, two courses on big data technologies, three elective courses, and one capstone project using real-world data sets.

All lectures are recorded by leading instructors from MIT, Harvard University, and other top universities.

You will learn about probability, inference, machine learning, big data technologies, and more.

Each course in this program contains case studies such as “Spam Detection” and discusses the best practices for data analysis.

You will also come across trends in World Health and Economics, US Crime Statistics, and more.

In addition, you will learn about visualization techniques to represent data as charts and graphs.

In fact, if your background is not in statistics or programming but you are interested in learning more about Data Science this program is a great place to start.

9. MicroMasters Program in Data Science – edX

The edX MicroMasters program in data science is a unique program that will help you to gain a deep understanding of the data science methodology and tools necessary for looking at, exploring, and analyzing complex real-world datasets.

The MicroMasters has been designed in partnership with leading universities such as MIT, HarvardX, Microsoft Research, and edX.

Each university provides its own course material which is curated by the edX team to provide a complete learning experience.

The material is then divided into the following topics:

• Foundational concepts in data science
• Statistics and probability for data science
• Data management, processing, and visualization & machine learning algorithms

The MicroMaster program provides you with an opportunity to earn your master’s degree in data science by giving you access to the same education offered at leading universities.

You will learn about the following topics:

• Introduction to data science, machine learning & data exploration, and wrangling & cleaning
• Data analysis and statistics with Pandas (Python) and R Programming Language
• Expanding your skills into statistical inference, causal inference, and A/B testing using the Python programming language
• Statistical machine learning, deep learning, and artificial intelligence (Python & R)
• How to collect large data set from Amazon Web Services using Python programming language
• Data analysis with the Pandas library of Python for exploratory data analysis
• Use statistical models including linear regression, logistic regression, and random forests to make predictions in Python with the scikit-learn package.
• How to use the R programming language for data wrangling, exploration, and visualization of data sets.

This program is offered 100% online and is completed in as little as 12 months.

The only prerequisite knowledge for it is a little programming experience and an understanding of basic high school-level math.

10. The Data Science Course 2020: Complete Data Science Bootcamp – Udemy

This is a comprehensive course that starts from the basics and teaches you how to become a data scientist.

You will learn all of the essential concepts, techniques, and tools, as well as explore real-world datasets which are necessary for your career in Data Science.

From the R programming language to Hadoop MapReduce job implementation through Spark, every concept is explained in detail while each technique is demonstrated by using real-world datasets.

This course is divided into four sections: Data Science Essentials, Python for Data Sciences, Real World Data Analytics, and Hadoop & Spark for Big Data Analytics.

You will learn about data visualization, machine learning techniques such as Classification algorithms (Decision Tree Learning, K-Nearest Neighbor, Naive Bayes), Clustering (K-Means), and Association Rule Learning.

11. Business Analytics Specialization – Coursera

Udacity’s Business Analytics course is a great option for those interested in learning the fundamentals of data analytics.

It has over 100 hours of video lectures, and more than 30 hands-on projects to help you learn how to create effective business models using tools like SAS, R programming language (RStudio), SQL queries, and Tableau.

This specialization is broken down into four courses:

The first course, Introduction to Data Analytics for Business has lectures on the basics of data analytics.

The second course covers descriptive statistics and inferential analysis using tools like Excel PivotTables, charts, and graphs. It also teaches you how to build a regression model in the R programming language (RStudio).

The third course focuses on predictive analytics, where you will learn how to build a regression model in R programming language (RStudio), implement ARIMA, and cluster data.

The last course teaches you about text mining using Apache Spark with Python programming language.

It also covers natural language processing for sentiment analysis.

12. Data Engineering, Big Data, and Machine Learning on GCP Specialization – Coursera

This specialization covers the most common big data and machine learning tasks performed by a Data Engineer or Machine Learning Specialist, such as training models using TensorFlow or implementing scalable data processing pipelines.

The courses in this program provide practical knowledge of Big Data and Machine Learning on Google Cloud Platform (GCP), including hands-on experience with platform technologies like Cloud Dataflow, Cloud Datalab, and TensorFlow.

You will learn how to process large-scale datasets, visualize data with Cloud Datalab, implement Cloud Dataflow pipelines in Python and Java, and build machine learning models using TensorFlow.

13. Python Certification Training for Data Science – Edureka

This course will give you an introduction to Python and teach you how it works with various datasets, including its benefits over other programming languages like R.

Once this introductory module is completed, you can choose from three different paths: Data Analyst, Data Scientist, and Machine Learning Engineer.

At the end of the course, you will be given a certificate of completion.

14. Data Scientist Masters Program – Edureka

The Edureka Data Scientist Masters Program is one of the best online data science courses. The course will teach you how to analyze data using Python, R, and Tableau.

This Data Science masters program has been designed in collaboration with Hadoop experts from Silicon Valley who have trained thousands of students worldwide on Big Data.

You will learn all the necessary concepts of data science like statistics, machine learning algorithms, and how to implement them on Hadoop.

This course has been designed by real-time industry experts with 100% practical sessions for hands-on experience.

A dedicated team will be there at your service round the clock to answer all your queries related to career growth after the course.

15. Career Path Data Science – Codecademy

This course teaches you how to use the tools of Data Science.

It is best for anyone interested in learning how to use data science tools and techniques.

This course is also suitable if you’re considering a career as a Data Scientist, or if your job currently involves working with data.

It is mainly focused on using tools in the R language, but it also provides an introduction to Python and SQL for those who are unfamiliar with them.

The lessons cover all kinds of data science workflows, from collecting your first dataset through exploring it interactively to building models that help answer interesting questions about that data.

You’ll learn about SQL and databases, data wrangling with Pandas and Python, and exploring data with Dplyr & RStudio.

The course will also teach you advanced visualization in Python (including Plotly), and machine learning methods including regression and classification algorithms such as Support Vector Machines.

At the completion of the course, you will have a strong foundation in Data Science, which can be applied to any problem.

Final Words

On a final note, the best thing about these data science courses is that you can take them anytime, anywhere.

You have complete flexibility in terms of how many courses you want to take up at a time along with the pace at which you wish to learn new skills on your own personal computer or laptop.

With this flexibility, you can take your learning to the next level by diving deep into one area of data science while also getting a broad understanding of another.

There are no limits on what you want to learn or do. It’s up to you.