We know that there are a lot of online platforms to learn data science and other technology. Coursera is one of the top-notch online course platforms available online. In this article, I try to list the best online courses available on Coursera for data science.
Data science is a very vast field, and there are a lot of niches in data science, such as machine learning, data analysis, etc. Coursera has courses specifically for each niche in data science. Also, it has several courses on data science, both for beginners and advanced developers.
I spend some time reading reviews and watching YouTube videos about all the best courses that Coursera has on Data science. I also did some research on the Coursera platform itself to find the best courses they have.
In this article, I want to share what I’ve found with you. If you’re looking for doing an online course on Data Science, I hope this article will be helpful. Without any further introduction, let’s see what the best courses are.
1. Data Science Specialization from Johns Hopkins University
Specialization programs are one of the best programs offered by Coursera. They have specialization programs in Data Science as well. Data Science Specialization by Johns Hopkins University is one of their best programs.
This course is offered by the famous Johns Hopkins University, and you’ll get a certificate from this University upon completing this program. This program is actually a series of several courses.
The specialization includes a hands-on project, in which you’ll be able to put your knowledge into practice by creating a data product, using real-world data.
This is a beginner-level course. The approximate timeline for completing this course is 11 months. It is a long-term course since it includes a lot of material and projects to complete.
Note that this course is using R as the programming language, not Python. Even if you don’t know the R language, don’t worry. This course covers that too.
You’ll learn how to use the R language and how to clean, analyze, and visualize data with it. The course covers the entire data science pipeline from data acquisition to publication. You’ll also learn how to use GitHub to manage your data science projects.
| Course Name | Data Science Specialization |
| Instructor | Jeff Leek, Roger D. Peng, Brian Caffo |
| Rating | 4.5/5 (82,516+ ratings) |
| Duration | 11 months (considering 7 hours per week) |
This is a paid course. But, it comes up with a 7-day complete free trial. If you want to try this course, click here to view the syllabus and sign-up for a free trial.
If you don’t like the course, you can cancel it in seven days without losing any money. If you have some time and money to invest in learning data science, this is a great course for you.
2. Introduction to Data Science in Python from the University of Michigan
This is a great introductory course to data science using Python programming language. This course will start with the Python fundamentals and then move on to techniques such as lambdas, reading and analyzing CSV files, etc.
The course will walk you through Numpy, which is a numerical library of Python. Then, it will jump on to pandas, which is a data science library that can be used for data analysis. By the end, you’ll be able to take a dataset, clean the data, analyze it, and find significant inferences from the analysis.
This is not a complete course on data science like the data science specialization. This course covers only a portion of the data science field. But, this can be a superb introduction for beginners.
Python and its data science libraries are very important in data science. After completing this course, you can move on to fields such as data visualization and machine learning.
| Course Name | Introduction to Data Science in Python |
| Instructor | Christopher Brooks |
| Rating | 4.5 (23,327+ ratings) |
| Duration | 31 hours |
This is a 4-week course. You’ll get a certificate from the University of Michigan upon completing the course. You can see the syllabus and sign up for a free trial of 7-days.
This course is a part of the Applied Data Science with Python Specialization. This specialization is a complete package of courses covering various topics in data science. If you want to take the specialization, you can click here to check the details of this course.
3. Machine Learning Course from Stanford
The Machine Learning course by the instructor Andrew Ng has been one of the best machine learning courses on the Internet for a long time. Still, this is a great course for beginners who want to learn machine learning.
The best thing is that this is a free course. You’ll get all the content for free. You only have to pay if you want to get a completion certificate from Stanford. The approximate timeline for completing this course is 54 hours.
By taking this course, you’ll be able to learn the theoretical concepts in machine learning as well as practical implementation. The course covers supervised learning, unsupervised learning, and best practices in machine learning.
| Course Name | Machine Learning |
| Instructor | Andrew Ng |
| Rating | 4.9/5 (155,446+ ratings) |
| Duration | 60 hours |
This course is not based on Python. The programming assignments are done using Octave or MATLAB. If you want to learn machine learning, I would highly recommend this course to you. Click here to see the syllabus and enroll for free.
4. Deep Learning Specialization from DeepLearning.ai
If you want to learn deep learning and neural networks, the Deep Learning Specialization is a great course. Deep learning is a highly evolving technology, and there is a lot of scope in the future in various fields. It is actually a package of 5 independent courses combined. This course is offered by deeplearning.ai.
You’ll learn about neural networks, improving the deep neural networks, structuring machine learning projects, convolutional neural networks, sequence models, etc. You’ll also get a chance to do applied learning projects where you can implement deep neural networks and algorithms.
This is an intermediate-level course. Some related experience is required. If you have a bit of experience in machine learning, you can jump on to deep learning easily. This course will take approximately four months to complete.
| Course Name | Deep Learning Specialization |
| Instructor | Andrew Ng |
| Rating | 4.8/5 (269,230+ ratings) |
| Duration | 4 months (Assuming 5 hours per week) |
This is a premium course, but they provide a 7-day free trial. If you want to take a look at this course, click here to see the syllabus and sign up.
5. Mathematics for Machine Learning from Imperial College London
Mathematics is pretty important in data science and machine learning, especially calculus, linear algebra, probability, and statistics. This course will cover every bit of mathematics you need to know to become a machine learning expert.
This specialization contains three courses covering linear algebra, multivariate calculus, and PCA. You’ll be able to learn the mathematical concepts in machine learning and implement those concepts using real-world data.
You’ll also be doing an applied learning project. At the end of this course, you’ll have the mathematical skills that are needed for taking advanced courses in machine learning.
| Course Name | Mathematics for Machine Learning Specialization |
| Instructor | David Dye |
| Rating | 4.5/5 (15,627+ ratings) |
| Duration | 4 months (Assuming 4 hours per week) |
This is a beginner-level course. No prior experience is required. You might need to dedicate 4-hours to complete this course.
Click here to see the syllabus and sign up for the 7-day free trial, if you’re interested.
Final Thoughts
Coursera + Data Science is a great combination. The former is one of the top education course platforms, whereas the latter is a promising technology for the future. That’s why I’ve put up this article to introduce the best data science courses available on Coursera, according to my opinion and research.
I hope this article was helpful for you if you’re looking to launch your career in data science. Click the links given above and see the complete details about the courses before you decide to take one.
If you’ve already taken any of these courses, share your feedback in the comments below. That will help other readers as well. Also, do mention if you have any other suggested courses apart from the ones that I’ve mentioned here.
Share this article if this was helpful. Happy learning!
