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About Data Science Certification Course

About the Data Scientist course developed in collaboration with IBM

This Data Scientist Master’s Program, in collaboration with IBM, accelerates your career in Data Science and provides you with world-class training and skills required to become successful in this field. The Data Scientist course offers extensive training on the most in-demand Data Science and Machine Learning skills with hands-on exposure to key tools and technologies including Python, R, Tableau, and concepts of Machine Learning. Become an expert in Data Science by diving deep into the nuances of data interpretation, mastering technologies like Machine Learning, and mastering powerful programming skills to take your career in Data Science to the next level.

This joint partnership between Simplilearn and IBM introduces students to an integrated Blended

Learning approach, making them experts in data science. This Data Science course, in collaboration with IBM, will help students become industry-ready for top data scientist job roles.

What can I expect from these Data Science courses developed in collaboration with IBM?

Upon completion of this Data Scientist course, you will receive IBM certificates for the IBM courses and Simplilearn certificates for all the courses in the learning path. These certificates will testify for your skills and assert your Data Science expertise. You can also avail the following benefits as part of this Data Science Course Online program

Masterclasses by IBM experts

Ask Me Anything sessions with IBM leadership

Exclusive Hackathons conducted by IBM

Industry-recognized Data Scientist Master's certificate from Simplileari


Scientist is one of the hottest professions of this year. The United States Bureau of Labor Statistics sees unprecedented growth in the field of data science and predicts 11.5 million job openings in 2026 rising at an annual growth rate of 28%. Simplilearn's Data Science certification course co-developed with IBM encourages you to master job-critical skills including statistics, hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, recommendation engine, supervised and unsupervised learning and much more

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