Statistics for Data Science Essentials
Master probability and statistics essentials for data science, from central limit theorem to confidence intervals. Perfect for ML and AI preparation.
Master probability and statistics essentials for data science, from central limit theorem to confidence intervals. Perfect for ML and AI preparation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI and Machine Learning Essentials with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Master probability fundamentals and their applications in data science
Apply the Central Limit Theorem to real-world problems
Calculate and interpret confidence intervals
Understand point estimation and maximum likelihood methods
Develop skills in statistical sampling and analysis
Skills you'll gain
This course includes:
3.3 Hours PreRecorded video
16 assignments
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FullTime access
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There are 4 modules in this course
This comprehensive course covers essential statistical concepts for data science practitioners. Students learn fundamental probability theory, statistical estimation methods, and practical applications of concepts like the Central Limit Theorem and confidence intervals. The curriculum combines theoretical foundations with hands-on mathematical assignments, preparing learners for advanced machine learning and AI applications. Through weekly modules, students progress from basic probability to complex statistical estimation techniques.
Getting Started with Statistics for Data Science
Module 1 · 4 Hours to complete
Probability
Module 2 · 4 Hours to complete
Statistical Estimation
Module 3 · 4 Hours to complete
Confidence Intervals & Point Estimation
Module 4 · 4 Hours to complete
Fee Structure
Instructors
Leading Scholar in Natural Language Processing and Crowdsourcing
Chris Callison-Burch is an associate professor of Computer and Information Science at the University of Pennsylvania, where his work has positioned him as a thought leader in natural language processing (NLP) and crowdsourcing. Before joining Penn, he was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University, contributing significantly to advancements in the field during his six-year tenure.Chris has held prominent roles in major conferences and organizations, including serving as the General Chair of the ACL 2017 conference, Program Co-Chair for EMNLP 2015, Chair of the Executive Board of NAACL (2011–2013), and Secretary-Treasurer for SIGDAT (2015–2017). His editorial contributions span leading journals such as Transactions of the ACL (TACL) and Computational Linguistics.With over 100 publications that have been cited more than 10,000 times, Chris's research has had a profound impact on computational linguistics. Recognized as a Sloan Research Fellow, he has received prestigious faculty research awards from Google, Microsoft, Amazon, and Facebook. His work has also been supported by DARPA and the National Science Foundation (NSF).Chris's research interests focus on the intersections of natural language processing, machine translation, and the innovative use of crowdsourcing to tackle complex computational challenges. His contributions continue to shape the future of AI-driven language technologies and their practical applications.
Associate Professor
Hamed Hassani is an Associate Professor in the Department of Electrical and Systems Engineering, as well as in the Departments of Computer and Information Sciences and Statistics at the University of Pennsylvania. He earned his PhD in Computer and Communication Sciences from EPFL, Lausanne. Prior to his current role, he was a research fellow at the Simons Institute for the Theory of Computing at UC Berkeley and a post-doctoral researcher at ETH Zurich. His research interests encompass machine learning, coding theory, and information theory, with a focus on developing robust algorithms for data science applications.On Coursera, Hamed teaches the course Statistics for Data Science Essentials, which is designed to provide learners with foundational statistical concepts essential for data science. His expertise and innovative teaching methods aim to equip students with the necessary skills to analyze data effectively and make informed decisions based on statistical analysis. Hamed's contributions to the field have been recognized through various awards, including the IEEE Information Theory Society's Thomas M. Cover Dissertation Award and the NSF CAREER Award.
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