As an open access platform of the Harvard Data Science Initiative, Harvard Data Science Review [HDSR] features foundational thinking, research milestones, educational innovations, and major applications, with a primary emphasis on reproducibility, replicability, and readability. We aim to publish content that help define and shape data science as a scientifically rigorous and globally impactful multidisciplinary field based on the principled and purposed production, processing, parsing, and analysis of data. By uniting the strengths of a premier research journal, a cutting-edge educational publication, and a popular magazine, HDSR provides a crossroads at which fundamental data science research and education intersect directly with societally-important applications from industry, governments, NGOs, and others. By disseminating inspiring, informative, and intriguing articles and media materials, HDSR aspires to be a global forum on everything data science and data science for everyone.
Panorama
Overviews, Visions, and Debates
Cornucopia
Impact, Innovation, and Knowledge Transfer
Diving into Data
Mini Tutorials on Concepts, Methods, and Tools
Column Editor: Sach Mukherjee
Mining the Past
Brief Histories of Data Science
Column Co-Editors: Stephanie Dick and Christopher J. Phillips
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MicroMasters Statistics and Data Science Program [SDS] team would like to share these 7 credential holders stories that were included as part of the Completion Celebration that was held June 18, 2020.
This group of learners either just earned a MicroMasters program credential in Statistics and Data Science or are about to take the final comprehensive exam. Learn more about the program and their experiences in this video.
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Demand for professionals skilled in data, analytics, and machine learning is exploding. The U.S. Bureau of Labor Statistics reports that demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026. Data scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 39% of the most rigorous data science positions requiring a degree higher than a bachelor’s.
This MicroMasters® program in Statistics and Data Science [SDS] was developed by MITx and the MIT Institute for Data, Systems, and Society [IDSS]. It is a multidisciplinary approach 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.
To complete the SDS MicroMasters program, learners will need to take the three core courses and one out of two electives. Once learners have passed their four courses, they will then take the virtually-proctored Capstone exam to earn the MicroMasters program credential in SDS. The credential can be applied, for admitted students, towards a Ph.D. 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 who 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 of this program are taught by MIT faculty and administered by Institute for Data, Systems, and Society [IDSS], 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.
Is data science at MIT good?
MIT is a highly prestigious institution that offers undergraduate and graduate-level courses in data science, including a Master's in Business Analytics, a PhD in Operations Research, and a range of certificate programs.
Is MIT MicroMaster data science worth it?
Is the MITx MicroMaster in Statistics and Data Science worth it? Yes, if you want to learn fundamentals; it is a lot of theory and hand on experience. You will develop deep understanding of skills needed to start data science career or an advanced degree.
Is data science and statistics hard?
Conclusion. Data science is a complicated and rapidly developing discipline that calls for a mix of technical know-how, subject-matter expertise, and problem-solving skills.
Is MIT MicroMasters hard?
Academic rigor. Make no mistake, these are graduate-level MIT courses. They are hard. Each one took me about 15 hours per week. I expected that I could go to a coffee shop and watch lectures.