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Microcredential

Statistics and Data Science

Massachusetts Institute of Technology via edX Micromasters

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Overview

Demand for professionals skilled in data, analytics, and machine learning is exploding. A recent report by IBM and Burning Glass states that there will be 364K new job openings in data-driven professions by 2020 in the US. Data scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. 39% of the most rigorous data science positions require a degree higher than a bachelor’s.

This MicroMasters program in Statistics and Data Science is comprised of four online courses and a virtually proctored exam that will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning. You will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. This program will prepare you to become an informed and effective practitioner of data science who adds value to an organization. The program certificate can be applied, for admitted students, towards a PhD in Social and Engineering Systems (SES) through the MIT Institute for Data, Systems, and Society (IDSS) or may accelerate your path towards a Master’s degree at other universities around the world.

Anyone can enroll in this MicroMasters program. It is designed for learners that want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality. There is no application process but college-level calculus and comfort with mathematical reasoning and Python programming are highly recommended if you want to excel. All the courses are taught by MIT faculty at a similar pace and level of rigor as an on-campus course at MIT. This program brings MIT’s rigorous, high-quality curricula and hands-on learning approach to learners around the world – at scale.

For more detail on this program and credit pathways, please visit https://micromasters.mit.edu/ds/

Courses

Taught by

Jimmy Li, Jagdish Ramakrishnan, Katie Szeto, Kuang Xu, Regina Barzilay, Zied Ben Chaouch, Dimitri Bertsekas, Sara Fisher Ellison, Esther Duflo, Tommi Jaakkola, Philippe Rigollet, Jan-Christian Hütter, John Tsitsiklis, Patrick Jaillet, Karene Chu, Eren Can Kizildag and Qing He

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