5 Best Data Science Courses on Coursera for Aspiring Data Scientists

Looking to build your data career? Here are the best data science courses on Coursera to grow your knowledge and credentials.

Coursera is a premium online learning platform for individuals who are interested in developing career-related skills.

Within their extensive range of computer science courses, their data science courses and certificates form a major, standalone category that contains a few gems.

With that said, here is a list of some of the best data science courses on Coursera taught by instructors from top companies and universities around the world.

If you're mathematically minded and enjoy the technical aspects of coding and modeling, these courses will be a great fit.

Even if you're a beginner - don't worry; these courses are designed to be understood by learners who are entering the field of data science for the first time with no prior programming experience.

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What are the Best Data Science Courses on Coursera?

Here are the best data science courses on Coursera to enroll in this year:

1. Introduction to Data Science Specialization

Interested in learning more about data science, but don’t know where to start?

This popular 4-course specialization by IBM will teach you the key foundational skills needed to embark on a career in data science.

First you'll discover what data science is and what data scientists do. Then you'll explore the applicability of data science across fields, and learn how data analysis can help you make data driven decisions.

By the end of the course, you'll be able to:

Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists

Navigate common data science tools including JupyterLab, R Studio, GitHub and Watson Studio

Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems

Write SQL statements and query Cloud databases using Python from Jupyter notebooks

Program Syllabus:

Course 1: What is Data Science?

Course 2: Tools for Data Science

Course 3: Data Science Methodology

Course 4: Databases and SQL for Data Science with Python

Throughout the course, you'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.

The good thing about this course is you’ll find that you can begin your career path in the data field without prior knowledge of computer science or programming languages.

Once you have the basics mastered, you can go on to pursue more advanced learning whenever you're ready!

Key course features:

Beginner Level

No prior experience required

Approximately 4 months to complete

Suggested pace of 5 hours/week

English

Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Turkish, Spanish, Persian

=> Enroll in IBM's Introduction to Data Science Specialization here.

2. Data Science Fundamentals Specialization

This Data Science Fundamentals course provides an overview of the most common techniques used in data science.

The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques.

You will learn:

Key skills needed to work in the data science profession

The benefits of using the cross-industry standard process for data mining (CRISP-DM)

The application of predictive modeling to professional and academic work

Program Syllabus:

Course 1: Intro to Analytic Thinking, Data Science, and Data Mining

Course 2: Predictive Modeling, Model Fitting, and Regression Analysis

Course 3: Cluster Analysis, Association Mining, and Model Evaluation

Course 4: Natural Language Processing and Capstone Assignment

In this program, you'll discover the types of business problems data science can solve as well as ethical considerations required when working with data.

At the end of the program, you'll complete a capstone assignment that involves evaluating a business scenario and choosing the best data science-based analytical approach to solve a problem.

Key course features:

Beginner Level

This specialization is for beginners, no background in the topic is needed

Approximately 4 months to complete

Suggested pace of 1 hour/week

English

Subtitles: English, Indonesian

=> Enroll in the Data Science Fundamentals Specialization here.

3. IBM Data Science Professional Certificate

The IBM Data Science Professional Certificate is a program that consists of 9 online courses that will provide you with the latest job-ready tools and skills to start a career as an entry level data scientist.

Throughout the program, you'll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. I enrolled in this program and really enjoyed the lab exercises that were provided.

Topics covered include open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms.

You will use the following tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio

You will work with the following libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.

By the end of the program, you'll be able to:

Describe what data science is, and the various activities of a data scientist’s job

Navigate the tools, languages, and libraries used by professional data scientists

Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python

Apply various data science skills, techniques, and tools to complete a project using a real-world data set and publish a report for stakeholders

Coursera learners who complete this professional certificate will be awarded a digital badge from IBM as well as special access to join IBM’s Talent Network.

Talent Network members receive job opportunities as soon as they are posted, recommendations to apply matched directly to your skills and interests, and tips and tricks to help you stand apart from the crowd.

In addition, The IBM Data Science Professional Certificate recently secured a credit recommendation from the American Council on Education (ACE®), which is the industry standard for translating workplace learning to college credit.

As a result, course participants can earn a recommendation of 12 college credits for completing the program. This aims to help open up additional pathways to learners who are interested in higher education and prepare them for entry-level jobs.

Key course features:

11 Months

Under 4 hours of study a week

Beginner Level

No prior experience required

English

Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Turkish, Spanish, Persian, Korean

=> Enroll in the IBM Data Science Professional Certificate here.

4. Applied Data Science Specialization

Applied Data Science is one of the best data science courses on Coursera for getting hands-on experience tackling interesting data problems from start to finish.

In this 5-course Specialization, you'll learn how to leverage computer science and statistical analysis to analyze data and make data-driven business decisions.

No prior programming experience is necessary, however during the course you will learn Python.

You’ll also utilize tools used by real data scientists like Numpy and Pandas, practice predictive modeling and model selection, and learn how to tell a compelling story by visualizing data and findings in an approachable and stimulating way.

Program objectives:

Develop an understanding of Python fundamentals

Gain practical Python skills and apply them to data analysis

Communicate data insights effectively through data visualizations

Create a project demonstrating your understanding of applied data science techniques and tools

Program Syllabus:

Course 1: Python for Data Science, AI, and Development

Course 2: Python Project for Data Science

Course 3: Data Analysis with Python

Course 4: Data Visualization with Python

Course 5: Applied Data Science Capstone

In this program, you'll get to build your data science portfolio as you gain practical experience from producing artifacts in the interactive labs and projects.

In the final capstone course, you'll apply what you’ve learned from previous courses into one comprehensive project.

In this project, you will train and compare machine learning models, including support vector machines, classification trees, and logistic regression, to predict if a SpaceX launch can reuse the first stage of a rocket.

Like the IBM Data Science Professional Certificate, this program is ACE® recommended - when you complete, you can earn up to 12 college credits.

Key course features:

Beginner Level

No prior experience required.

Approximately 6 months to complete

Suggested pace of 3 hours/week

English

Subtitles: English, Korean, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Turkish, Spanish, Persian

=> Enroll in the Applied Data Science Specialization here on Coursera.

5. Data Science Fundamentals with Python and SQL Specialization

This specialization will teach you foundational skills required for data science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases.

First, you'll gain a working knowledge of tools such as Jupyter Notebooks, R Studio, GitHub, and Watson Studio.

Next, you'll learn about Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy.

Then you'll explore Statistical Analysis techniques including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression.

Program Syllabus:

Course 1: Tools for Data Science

Course 2: Python for Data Science, AI, & Development

Course 3: Python Project for Data Science

Course 4: Statistics for Data Science with Python

Course 5: Databases and SQL for Data Science with Python

A highlight of this course is that you will learn SQL (Structured Query Language) inside out. Much of the world's data resides in databases. SQL is a powerful language used for communicating with and extracting data from databases.

Working knowledge of SQL is a must for data professionals like data scientists, data analysts and data engineers.

Here are some cool projects you'll complete as part of this program:

Extracting and graphing financial data with the Pandas data analysis Python library

Generating visualizations and conducting statistical tests to provide insight on housing trends using census data

Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

This program is ACE® recommended - when you complete, you can earn up to 8 college credits.

Key course features:

Beginner Level

Just basic computer literacy and willingness to self-learn online. No prior knowledge of computer science or programming languages required.

Approximately 7 months to complete

Suggested pace of 4 hours/week

English

Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Spanish, Persian, Korean

=> Enroll in the Data Science Fundamentals with Python and SQL Specialization here.

Final Thoughts

As you can see, the IBM Skills Network currently wins the prize when it comes to producing the best data science courses on Coursera.

Their courses are very comprehensive, approaching data science from multiple angles and through a wide range of applications.

Whether you're enrolling in a professional certificate, an individual course, or a specialization, I hope you take advantage of these great online resources that can kick start your data science career.

Happy learning!