5 Self-paced Courses
Designed to ensure you learn the fundamental skills to embark on a career as a Data Scientist.
Learn From the Best
A stellar team of academics at Harvard and Columbia, and industry practitioners at Cisco, Nokia Labs and Pitney Bowes.
Certification
Industry recognized certification enables you to add this credential to your resume upon completion of all courses.
Data Science Training Track
Learn data science from industry experts at Harvard, Columbia, Cisco, Apple and Google. Experfy instructors are industry thought leaders who provide you with in-depth training in introductory topics like statistics to advanced ones like machine learning. This Data Science Certification Program covers the concepts and tools you will need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. As you work your way through different courses in the data science training track, you will develop a portfolio of projects that you can showcase during interviews. Employers want to see students who have been trained by real experts and not by training departments. Experfy courses give you the practical hand-on training that will prepare you for the real world skills that will be necessary as you begin working as a data scientist.
Probability and Statistics for Data Science with R
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Kaitlin Hagan,
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Michael Parzen
Self-paced
About the Course
- Harvard faculty teaches you how to apply statistical methods to explore, summarize, make inferences from complex data and develop quantitative models to assist business decision making.
- Course includes instructional component, R tutorial videos, and exercises to reinforce concepts and give you an opportunity to see statistics in action.
- Michael Parzen is an award-winning faculty member at Harvard and teaches one of the most popular classes. Kaitlin Hagan is a post-doctoral fellow at Brigham and Women's Hospital and has won numerous teaching awards and citations for her work.
Data Wrangling in R
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Dr. Connie Brett
Self-paced
About the Course
Real-world data preparation for further analysis using R
- Learn from start to finish how to get your data into R efficiently and polish it up so that it is as good as it can be.
- Instructor is the founder of Analytics Incubation Center at Cisco and has 15 years of analytics development experience.
- Capstone project reviewed by the instructor.
Econometric Analysis: Methods and Applications
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Alan Yang
Self-paced
About the Course
- Quantitative and econometric analysis focused on practical applications that are relevant in fields such as economics, finance, public policy, business, and marketing.
- The Instructor, Alan Yang, is a faculty member at the Department of International and Public Affairs at Columbia University where he teaches courses in Introductory Statistics, Econometrics, and Quantitative Analysis in Program Evaluation and Causal Inference.
Classification Models
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Saed Sayad
Self-paced
About the Course
How to use classification algorithms to solve real world problems
- Online self-paced course with cap-stone project
- Instructor is lead data scientist at one of the largest software companies in the world, author of a best-seller and an adunct professor at University of Toronto
Clustering and Association Rule Mining
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Anirban Ghosh
Self-paced
About the Course
Learn Clustering methods and Association Rule Mining Techniques
- Learn concepts of Cluster Analysis and study most popular set of Clustering algorithms with end-to-end examples in R
- Supported by office hours and hands-on practice exercises to be submitted at the end of the course
- Instructor is a Machine Learning Scientist with 10+ years of hands-on experience in predictive analytics and data science research at leading consulting, captive and R&D organizations.
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