There is no let up in the demand for Data Scientists, nor in the interest shown in this emerging field. Enrollment has just opened for an online program that can serve as an accelerated route to gaining a Master's degree at seven universities and also provides a valuable professional credential in its own right.
From probability and statistics to data analysis and machine learning, master the skills needed to solve complex challenges with data.
According to Professor Devavrat Shah, faculty director of the program and MIT professor in the Department of Electrical Engineering and Computer Science (EECS):
“This new MicroMasters program in Statistics and Data Science is bringing the quality, rigor, and structure of a master’s-level, residential program in data science at MIT to a wider audience around the world, and at a very accessible price, so people can learn anywhere they are while keeping their day jobs.”
The SDS program consists of four 13-16 week courses requiring 10-14 hours effort per week plus a final 2-week Capstone exam that pulls everything you have learned together.
Probability - The Science of Uncertainty and Data Starts September 3, 2018; May 20, 2019; Jan 27, 2020 An introduction to probabilistic models, including random processes and the basic elements of statistical inference. This course content is essentially the same as the corresponding MIT class Introduction to Probability, a course that has been offered and continuously refined over more than 50 years. Taught by John Tsitsiklis, the Clarence J. Lebel Professor in EECS.
Data Analysis in Social Science Starts September 25, 2018, November 8, 2019, Introduces learners to the essential notions of probability and statistics, covering techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. This class will illustrate these concepts with applications drawn from real world examples and frontier research. Taught by Esther Duflo, the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the MIT Department of Economics, and Sara Fisher Ellison, senior lecturer in the MIT Department of Economics.
Fundamentals of Statistics Starts February 12, 2019, September 2, 2019, May 11, 2020 Statistics is the science of turning the proliferating amount of available “raw” data into insights that support better decisions. Fundamental statistical principles are at the core of recent advances in machine learning, data science, and artificial intelligence. Taught by Philippe Rigollet, associate professor in the MIT Department of Mathematics and member of the Statistics and Data Science Center.
Machine Learning with Python: from Linear Models to Deep Learning Starts June 11, 2019, February 3, 2020 Machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. ML algorithms are deployed by search engines, recommender systems, advertisers, and financial institutions for content recommendation, predicting customer behavior, compliance, or risk. Taught by Regina Barzilay, the Delta Electronics Professor in EECS, and Tommi S. Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society.
Learners who successfully complete this MITx MicroMasters credential will have the opportunity to apply to the MIT Doctoral Program in Social and Engineering Systems (SES) offered through the MIT Institute for Data, Systems, and Society (IDSS) and have MITx MicroMasters couirsework recognized for credit.
In addition seven universities, including the Rochester Institute of Technology (United States), Doane University (United States), Galileo University (Guatemala), Reykjavik University (Iceland), Curtin University (Australia), Deakin University (Australia), and RMIT University (Australia), will be accepting the credential towards a master’s degree.
Of course, for many participants the SDS credential will be sufficient to help them into a new job or advance their career. Enrollment is now open and signing up to the entire program reduces the cost from $1500 to $1350.
Amazon SageMaker Studio, announced by CEO Andy Jassy on the second day of the AWS re:Invent conference, is envisaged as unifying all the tools needed for machine learning. Several other SageMaker [ ... ]
Julia has a new release with support for multi-threading and the ability to add methods to an abstract type. Julia recently made it into the top six languages for machine learning projects on&nbs [ ... ]