IBM Data Science VS Johns Hopkins Data Science- Which is good? [2024]

IBM Data Science VS Johns Hopkins Data Science

 IBM Data Science and Johns Hopkins Data Science are the specialization programs for data science available on Coursera. Both specialization programs have their own identification and popularity. That’s why many people get confused about which one to choose?. So if you are not sure and stuck between IBM Data Science and Johns Hopkins Data Science, then read this comparison- IBM Data Science VS Johns Hopkins Data Science. This comparison will help you to decide which one is better for you.

So, without ado, let’s get started-

IBM Data Science VS Johns Hopkins Data Science

Before going into details, let’s have a quick comparison between both specializations-

IBM Data ScienceJohns Hopkins Data Science
Time to Complete
NOTE- The time, I mentioned here is according to Coursera, but you can finish the whole specialization in less time.
10 Months (If you spend 5 hours/week)
11 Months (If you spend 7 hours/week)
Price7 Day Free Trial & then $39/month7 Day Free Trial & then $49/month
Suitable for• Complete Beginners with no previous programming knowledge.• Who has some previous programming knowledge and working knowledge of Algebra.
Programming Language Used-PythonR Programming
No. of Courses-9 Courses10 Courses
Pros• Well Structured Content.
• Provides a free IDE on IBM Cloud (limited).
• Great course for beginners
• Cover different parts of the whole data science pipeline.
• The final project is interesting and gives you hands-on experience of the processes.
• Well organized content.
Cons• The first What is Data Science? course is very basic and you may get bored if you have some previous understanding of data science.
• The Python for Data Science and AI course is not a complete Python course. This course teaches only the required knowledge of Python for data science.
• In the Statistical Inference course, the instructor covers a lot of content in less time. So, it’s harder to catch everything. You need to take other statistics courses to fill the gaps.
• Without having previous knowledge in programming and statistics, this course is harder to understand.
Check  IBM Data Science Professional Certificate Check Johns Hopkins Data Science Specialization

So, this is a quick comparison between  IBM Data Science Professional Certificate and Johns Hopkins Data Science. Now let’s see the topics covered in both specialization programs-

Topics Covered by IBM Data Science Professional Certificate

  1. What is Data Science?– This course covers the basic Introduction to Data Science by taking interviews with students and professionals, explaining their experience in the data science field.
  2. Tools for Data Science– This course explains the tools used for data science. Jupyter Notebooks, RStudio, Zeppelin, GitHub, and IBM Watson are discussed in this course.
  3. Data Science Methodology– This course explains the methodology used for data science. For example, how to understand the business problem, which kind of data is required for certain problems, how to collect the data, how to prepare and clean the data, and how to build and deploy a model for a business problem.
  4.  Python for Data Science and AI – This course covers Python basics, Pandas, and NumPy. There is one project in this course where you have to analyze a set of economic data using Watson Studio.
  5. Databases and SQL for Data Science– This course covers the basic and advanced SQL and databases like how to build databases, how to collect and analyze the data using Python
  6. Data Analysis with Python– In this course, you will learn a wide range of data analysis techniques, starting from importing and wrangling data to statistical analysis and modeling.
  7. Data Visualization with Python– This course covers data visualization techniques like line graphs, pie charts, bar charts, and specialized visualizations like Waffle and Folium.
  8. Machine Learning with Python– This course covers a lot of machine learning topics like simple regression models, classification, clustering, and recommendation systems.
  9. Applied Data Science Capstone– This is the last course- The Capstone Project!. The capstone project has two parts. In the first part, there is another learning module, where you have to cover the Foursquare API to get location details. The final project of this course is totally open-ended. The only requirement for this project is to use the Foursquare API, use data analytics, and create a Folium map as a part of the presentation.

Topics Covered by Johns Hopkins Data Science Specialization

  • The Data Scientist’s Toolbox– This course gives a basic introduction to data science, how to install R and RStudio, what is version control, how to use Git and GitHub, and about R Markdown.
  • R Programming– As the name suggests, this course is all about R programming. You will learn R programming concepts in this course. If you are a complete beginner in R, then I will not recommend starting your R journey with this course. As a beginner in R, you will find this course a bit challenging. It’s better to begin your R programming journey with these courses- Best Online Courses on R Programming You Should Know.
  • Getting and Cleaning Data-This course covers everything from loading data to transforming and wrangling data. This course also covers regular expressions, a very popular data-wrangling package ( dplyr &  tidyr). I personally love this course.
  • Exploratory Data Analysis– This course covers the analytic graphics basics, the base plotting system in R, the Lattice and the ggplot2 system, clustering and dimension reduction techniques, and ways to specify colors in R.
  • Reproducible Research– This course covers about Reproducible Research, literate programming tool knitr, and Evidence-based Data Analysis.
  • Statistical Inference-This course covers the basics of statistics with the following topics- basics of probability, random variables, and expectations, variability, distributions, limits, confidence intervals, testing, pvalues, power, bootstrapping, and permutation tests.
  • Regression Models– This course explains linear regression, multivariable regression, logistic regression, Poisson regression, residuals, diagnostics, variance inflation, and model comparison.
  • Practical Machine Learning– As the name suggests, this course covers the basics of machine learning. This course covers the following topics- prediction, cross-validation, types of errors, Caret package, tools for creating features and preprocessing, machine learning algorithms, regularized regression, and combining predictors.
  • Developing Data Products– This course teaches the basics of creating data products using Shiny, R packages, GoogleVis, and Plotly. Besides this, this course covers R Markdown and Leaflet.
  • Data Science Capstone– This is the last course where you have to create a public data product. This project will force you to learn a certain amount about natural language processing in R (the topic of the project).

Now, let’s see who should enroll in which specialization program-

Who Should Enroll in IBM Data Science Professional Certificate?

  • Perfect course for Data science beginners who are planning to enter in Data Science field. 
  • This program is also good for those who have been off the tools recently.

Who Should Enroll in Johns Hopkins Data Science Specialization?

  • Who has some prior knowledge in programming language and statistics.
  • Who want to use R instead of Python for data science.

NOTE- Completing any course will not make you a data scientist. These courses will only provide the necessary knowledge of data science and a few hands-on projects. So after completing these courses, you have to work on projects with the skills you learned in these courses and expand your portfolio with some other unique projects.


I hope this IBM Data Science VS Johns Hopkins Data Science comparison has cleared your doubts and now you can easily choose the one which suits you. If you have any questions, feel free to ask me in the comment section. I am here to help you. And If you found this article helpful, share it with others to help them too.

All the Best for your Data Science Journey!

Happy Learning!

Thank YOU!

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Though of the Day…

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Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.

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