Kick Start Your Data Science Career with this IBM Program by Coursera

Parth Gandhi

08 June 2023

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Course Overview

IBM Data Science Professional Certificate is an online, self-paced certification course developed by IBM to help learners make a career in data science or Machine Learning (ML). The program consists of 9 online courses that cover the latest tools and skills, such as open-source tools and libraries, Python, databases, Structured Query Language (SQL), data visualization, data analysis, statistical analysis, predictive modeling, and ML algorithms. You get to learn data science on the IBM Cloud with real data science tools and real-world data sets.

After completing these courses, you will have a portfolio of data science projects that will give you the confidence to pursue an exciting career in data science.

Joseph Santarcangelo, Azim Hirjani, Aishruthi Swaminathan, Alex Aklson, Polong Lin, Rav Ahuja, Hima Vasudevan, Aije Egwaikhide, Svetlana Levitan, Romeo Kienzler, and Saeed Aghabozorgi are the faculty for this course. Most of the courses are taught by Joseph Santarcangelo while others take up specific sub-courses under the certification. The teaching style is perfect and to the point with emphasis laid on hands-on lab work.

"My ability to leverage different tools and skills like SQL, web scraping, data visualization, and ML has enhanced tremendously after doing this course."

- Parth Gandhi

Course Structure

The course curriculum covers all data science topics needed by a beginner. The concepts are very well explained, and the labs are the best for hands-on learning.

It is a self-paced course, and it is recommended to take the courses in the following order:

What is Data Science?
Tools for Data Science
Data Visualization with Python
Python for Data Science
AI and Development
Python Project for Data Science
Databases and SQL for Data Science with Python
Data Analysis with Python
Data Visualization with Python
Machine Learning with Python
Applied Data Science Capstone

In the course, first the foundations of data science are built (using Python), then more tools are explored, including SQL and visualization, and then data analysis and ML are taught. Finally, students are required to work on a Capstone project that includes all the skills learned in the project.

The quizzes and weekly assignments can be attempted 3 times in a row then only after a gap of 8 hours. The study material is provided, and there are external resources provided for reference. The discussion forums are very helpful in connecting and develop relationships with peers.

Insider Tips

To get the best out of this course, I have included some important tips that you might find useful.

Complete the Course Step-by-step

Take the courses in order to complete them with ease.

Save All Lab Work

Save all the labs in your GitHub portfolio and try to add a personal exercise using them as a base to showcase your skills.

Highlight Key Learnings

Highlight the key learnings from each course and revisit these learnings and even labs repeatedly (because it is a long course and the Capstone project requires that you implement everything you learned from the beginning)

Assessment

The courses are split by week, and every week is followed by assignments or quizzes or hands-on lab work. There is a final peer-reviewed assignment at the end of each course.

Capstone Project

The final Capstone project was to assess the viability of a new company to compete with SpaceX in launching satellites at a cheaper price. It required using all the skills learned throughout the course

Final Take

Taking this course has enhanced my ability to leverage different tools and skills like SQL, web scraping, data visualization, and ML. This has helped me in my current job and has aided by professional development. Overall, it has provided a good foundation in the subject on which I can continue to build.

This is a comprehensive course for beginners and it is good to build a foundation in the data science domain with the help of this course. If you consider R as your primary language - take Google's data science course instead of IBM.