Let’s be practical, most learning platforms have data science courses.
But do all of them have the best data science courses?
For the longest time, I searched for the best data science courses and I was never pleased with what I found on most of the platforms.
Then it hit me like a rock against Goliath’s head, Coursera learning platform.
Coursera has the best data science courses that will not only teach you but also help you master mad data science skills that will make you the talk of the town.
And if you also just want to advance your skills and become the next Geoffrey Hinton and have a legacy that everybody will be talking about, then you are in the right place.
In this article, I have accumulated some of the best Coursera courses that helped me in my journey and that I’m sure are of great help to you too.
These best data science courses on Coursera are suitable for anybody, be it that you want to start from scratch or even if you already have some little knowledge of data science.
If you’re looking for some of the best courses for learning SQL on Coursera, then you have to check out my previous article where I reviewed the best SQL courses on Coursera. .
Here are quick links to these courses on Coursera…
|1. Machine Learning||3, 484, 849|
|2. Neural Networks and Deep Learning||755, 947|
|3. Python Data Structures||573, 078|
|4. R Programming||526, 453|
|5. The Data Scientist’s Toolbox||496, 407|
|6. Introduction to Data Science in Python||480, 615|
|7. Excel Skills for Business: Essentials||340, 692|
|8. What is Data Science?||323, 403|
|9. Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization||317, 940|
|10. Convolutional Neural Networks||284, 149|
|11. Using Databases with Python||283, 500|
We’ll look at what each course is about, what you’ll be able to do after the course as well as the requirements and skill level you need to have before starting any of these courses
I will also mention subtitles for the courses that have some in differnet languages.
Machine learning is the science of getting computers to act without being explicitly programmed.
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of human genetics.
Machine learning is so widespread today that you probably use it dozens of times a day without knowing it.
Many researchers also think it is the best way to make progress towards human-level AI.
In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
More importantly, you’ll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition, which makes it one of the best Coursera courses for data science.
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications so that you’ll also learn how to…
Apply learning algorithms for building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
If you are interested in learning more machine learning courses, here is a link to my article on machine learning courses.
Ratings: 4.9 stars (144, 849 ratings)
Students Enrolled: 3, 484, 131.
Duration: 54 hours.
Subtitles: Chinese (Simplified), English, Hebrew, Spanish, Hindi, Japanese.
If you want to break into the cutting-edge with AI, this course on Coursera will help you do so.
Deep learning engineers are highly sought, and mastering deep learning will give you numerous new career opportunities.
Deep learning is also a new “superpower” that will let you build AI systems that just weren’t possible a few years ago.
In this best selling course, you will learn the foundations of deep learning.
When you finish this class, you will:
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network’s architecture
This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.
So after completing it, you will be able to apply deep learning to your own applications.
If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.
This is the first course of the Deep Learning Specialization.
Rating: 4.9 stars (91, 732 ratings).
Students Enrolled: 755, 947.
Duration: 20 hours.
Subtitles:Chinese (Traditional), Arabic, French, Ukrainian, Chinese (Simplified), Portuguese (Brazilian), Vietnamese, Korean, Turkish, English,Spanish, Japanese
In this course on Coursera, you will learn how to program in R and how to use R for effective data analysis.
You will also learn how to install and configure software necessary for a statistical programming environment.
And describe generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing which includes:
- Programming in R
- Reading data into R
- Accessing R packages
- Writing R functions
- Profiling R code
- Organizing and commenting on R code.
Topics in statistical data analysis will provide working examples.
The course is part of multiple programs.
This course can be applied to multiple Specializations or Professional Certificates programs.
Completing this course will count towards your learning in any of the following programs: Data Science: Foundations using R Specialization and Data Science Specialization.
Ratings: 4.5 stars (18, 809).
Students Enrolled: 526, 453.
Duration: 58 hours.
Subtitles: Arabic, French, Chinese (Simplified), Portuguese (Brazilian), Vietnamese, English, Spanish, Japanese.
This course on Coursera will introduce the core data structures of the Python programming language.
You will move past the basics of procedural programming and explore how you can use the Python built-in data structures such as:
… to perform increasingly complex data analysis.
This Specialization builds on the success of the Python for Everybody course and will introduce you to the fundamental programming concepts, including:
Data structures, networked application program interfaces, and databases, using the Python programming language.
In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization
This course will cover Chapters 6-10 of the textbook “Python for Everybody”.
The course covers Python 3.
Students Enrolled: 573, 078.
Duration: 19 hours.
Subtitles: English, Korean, Arabic.
In this data science course on Coursera, you will get an introduction to the main tools and ideas in the data scientist’s toolbox.
The course gives you an overview of the data, questions, and tools that data analysts and data scientists work with.
There are two components to this best Coursera course for data science.
The first is a conceptual introduction to the ideas behind turning data into actionable knowledge.
The second is a practical introduction to the tools that will be used in the program like:
- Version control
This course is part of multiple programs
The course can be applied to multiple Specializations or Professional Certificates programs.
Completing this course will count towards your learning in any of the following programs:
Data Science Specialization and Data Science: Foundations using R Specialization.
Rating: 4.6 stars (27, 864).
Students Enrolled: 496, 407.
Duration: 13 hours.
Subtitles: English, Korean
This course on Coursera will introduce you to the basics of the python programming environment…
Including fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the NumPy library.
The course will introduce you to data manipulation and cleaning techniques using the popular python pandas data science library.
And also introduce the abstraction of the Series and DataFrame as the central data structures for data analysis.
Along with tutorials on how to use functions such as GROUP BY, merge, and pivot tables effectively. This course stands in as one of the best courses on Coursera for data science.
By the end of this course, you will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
This course should be taken before any of the other Applied Data Science with Python courses:
- Applied Plotting
- Charting & Data Representation in Python
- Applied Machine Learning in Python
- Text Mining in Python Applied
- Applied Social Network Analysis in Python
Rating: 4.5 stars ( 21, 007).
Students Enrolled: 480, 615.
Duration: 16 hours.
Subtitles: Chinese (Traditional), Portuguese (Brazilian), Vietnamese, Korean, English, Hebrew
This data science course on Coursera will teach you the “magic” of getting deep learning to work well.
Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results.
If you are interested in learning more about Deep Learning, check out my other article on deep learning courses and tutorials.
You will also learn TensorFlow.
I added this course to the list of the best Coursera courses for data science because after 3 weeks, you will:
- Understand industry best-practices for building deep learning applications.
- Be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop, and Adam, and check for their convergence.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
- Be able to implement a neural network in TensorFlow.
It is the second course of the Deep Learning Specialization.
Rating: 4.9 stars (52, 902).
Students Enrolled: 317, 940.
Duration: 18 hours.
Subtitles: Chinese (Traditional), Chinese (Simplified), Portuguese (Brazilian), Korean, Turkish, English, Spanish
The art of uncovering the insights and trends in data has been around since ancient times.
The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year.
Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science.
In this course, you will meet some data science practitioners and you will get an overview of what data science is today.
This course is part of multiple programs
This course can be applied to multiple Specializations or Professional Certificates programs.
The completion of this course will count towards your learning in any of the following programs:
- IBM AI Foundations for Business Specialization
- Key Technologies for Business Specialization
- IBM Data Science Professional Certificate
- Introduction to Data Science Specialization
Rating: 4.7 stars ( 38, 363 ratings).
Students Enrolled: 323, 403.
Duration: 10 hours.
Subtitles: English, Russian.
This course on Coursera will teach you how to build convolutional neural networks and apply them to image data.
Thanks to deep learning, computer vision is working far better than just two years ago…
And this is enabling numerous exciting applications ranging from safe autonomous driving to accurate face recognition, to automatic reading of radiology images.
In this course you will:
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of images, videos, and other 2D or 3D data.
You will master not only the theory but also see how it is applied in industry.
But that’s not all, you will practice all these ideas in Python and in TensorFlow, which the instructors will teach you.
In this Data Science course, you will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
After finishing this specialization, you will likely find creative ways to apply it to your work.
This is the fourth course of the Deep Learning Specialization.
Rating: 4.9 stars (34, 911).
Students Enrolled: 284, 149.
Duration: 20 hours.
Subtitles: Chinese (Traditional), Chinese (Simplified), Korean, Turkish, English, Spanish, Japanese.
This course on Coursera will introduce you to the basics of the Structured Query Language (SQL).
As well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort.
The course will use SQLite3 as its database.
You will also build web crawlers and multi-step data gathering and visualization processes.
For this course, you will use the D3.js library to do basic data visualization.
This course will cover Chapters 14-15 of the book “Python for Everybody”.
To succeed in this course, you should be familiar with the material covered in Chapters 1-13 of the textbook and the first three courses in this specialization.
In this course, you will cover Python 3.
Rating: 4.8 stars (17, 391).
Students Enrolled: 283, 500.
Duration: 14 hours.
Subtitles: English, Korean.
In this first course of the specialization Excel Skills for Business on Coursera, you will learn the essentials of Microsoft Excel.
Within six weeks, you will be able to:
- expertly navigate the Excel user interface
- perform basic calculations with formulas and functions
- professionally format spreadsheets
- create visualizations of data through charts and graphs
Whether you are self-taught and want to fill in the gaps for better efficiency and productivity, or whether you have never used Excel before…
This course will set you up with a solid foundation to become a confident user and develop more advanced skills in later courses.
It is one of the best courses for data science on Coursera because it has a broad range of practice quizzes and challenges… that will provide great opportunities to build up your skillset.
You will work through each new challenge with the instructors and in no time you will surprise yourself with how far you have come.
Spreadsheet software is one of the most universal pieces of software used in workplaces across the world.
Learning to confidently operate this software means adding a highly valuable asset to your employability portfolio.
At a time when digital skills jobs are growing much faster than non-digital jobs, make sure to position yourself ahead of the rest by adding Excel skills to your employment portfolio.
Rating: 4.9 stars (22, 432).
Students Enrolled: 340, 692.
Duration: 26 hours.
Subtitles: English, Vietnamese, Hungarian, Arabic.
According to expert360, data science is one of the fastest-growing industries in 2020.
It’s an extremely important and high-demand role that can have a significant impact on a business’s ability to achieve its goals, whether they are financial, operational, strategic, and so on.
Company’s collect a ton of data, and much of the time it’s neglected or underutilized.
This data, through meaningful information extraction and discovery of actionable insights, can be used to make critical business decisions and drive significant business change.
It can also be used to optimize customer success and subsequent acquisition, retention, and growth.
As mentioned, data scientists can have a major positive impact on a business’s success…
And sometimes inadvertently cause financial loss, which is one of the many reasons why hiring a top-notch data scientist is critical.
Have you ever taken any of these best data science courses on coursera before?
If yes, please share your experience in the comments below.