Statistics and Data Science (Methods Track)

Massachusetts Institute of Technology via edX MicroMasters

Overview

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 54% 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 separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.

General Track
This track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.

Explore the General track here

Methods Track
This track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.

You are currently exploring the Methods track

Social Sciences Track
This track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.

Explore the Social Sciences track here

Time Series and Social Sciences Track
This track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.

Explore the Time Series and Social Sciences track here

All tracks are taught by MIT faculty and administered by IDSS at a similar pace and level of rigor as an on-campu

To earn the MicroMasters program certificate in Statistics and Data Science, learners must complete and successfully earn a certificate in the four required courses and pass a virtually-proctored capstone exam.

MicroMasters programs are designed to offer learners a pathway to an advanced degree and can count as credit toward completing a Master’s degree program. Learners who successfully earn this MicroMasters program certificate may apply for admission to several Master’s programs, and if accepted, the MicroMasters program certificate will count towards the degree.

Learners who successfully complete this MicroMasters program certificate 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).

Learners can use their MicroMasters program certificate to demonstrate their preparation in Statistics and Data Science fundamentals to the SES Admissions Committee. Learners admitted to SES can expect that their MicroMasters coursework will be recognized with credit for corresponding SES core classes, and for satisfying the SES Information, Systems, and Decision Science requirements. More information on the MIT SES Doctoral Program can be found here.

In addition, learners who successfully earn the MicroMasters program certificate in Statistics and Data Science are now eligible to earn credit at a number of universities across the globe to fast track their pursuit of a full Master’s degree. A list of pathways to graduate programs can be found here.

s course at MIT. The program 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.

Syllabus

Courses under this program:
Course 1: Probability - The Science of Uncertainty and Data

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference -- Part of the MITx MicroMasters program in Statistics and Data Science.

Course 2: Machine Learning with Python: from Linear Models to Deep Learning.

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

Course 3: Fundamentals of Statistics

Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Part of the MITx MicroMasters program in Statistics and Data Science.

Course 4: Learning Time Series with Interventions

An in-depth introduction to time series analysis, from learning structured models to predictions and reinforcement learning, with hands-on projects - Part of the MITx MicroMasters program in Statistics and Data Science.

Course 5: Capstone Exam in Statistics and Data Science

Solidify and demonstrate your knowledge and abilities in probability, data analysis, statistics, and machine learning in this culminating assessment. -- Final Requirement of the MITx MicroMasters Program in Statistics and Data Science.

Courses

34 reviews
16 weeks, 10-14 hours a week, 10-14 hours a week
View details
The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.

Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem-proof" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.

The course covers all of the basic probability concepts, including:
- multiple discrete or continuous random variables, expectations, and conditional distributions
- laws of large numbers
- the main tools of Bayesian inference methods
- an introduction to random processes (Poisson processes and Markov chains)
The contents of this courseare heavily based upon the corresponding MIT class -- Introduction to Probability -- a course that has been offered and continuously refined over more than 50 years. It is a challenging class but will enable you to apply the tools of probability theory to real-world applications or to your research.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.
10 reviews
17 weeks, 10-14 hours a week, 10-14 hours a week
View details
Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is to develop and understand these core ideas on firm mathematical grounds starting from the construction of estimators and tests, as well as an analysis of their asymptotic performance.

After developing basic tools to handle parametric models, we will explore how to answer more advanced questions, such as the following:
- How suitable is a given model for a particular dataset?
- How to select variables in linear regression?
- How to model nonlinear phenomena?
- How to visualize high-dimensional data?
Taking this class will allow you to expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link them together, equipping you with the tools you need to develop new ones.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.
30 reviews
15 weeks, 10-14 hours a week, 10-14 hours a week
View details
If you have specific questions about this course, please contact us at sds-mm@mit.edu.

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:
- Representation, over-fitting, regularization, generalization, VC dimension;
- Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
- On-line algorithms, support vector machines, and neural networks/deep learning.
Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

This course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.
0 reviews
14 weeks, 10-14 hours a week, 10-14 hours a week
View details

If you have specific questions about this course, please contact us atsds-mm@mit.edu.

A time series is a time-stamped set of noisy observations from an underlying process that evolves over time. These observations are dependent on each other in a particular, unknown, fashion. Examples of such series include stock values, value of a currency with respect to the dollar, mean housing prices, the number of Covid-19 infections, or the pitch angle of an airplane during flights. Modeling such processes for the purpose of prediction or intervention is a fundamental problem in statistical learning.

This graduate-level course that will address three lines of development:

Learning Structured Models: In this module, we focus on learning the underlying stochastic dynamic model that generates the data. We discuss how algorithms depend on the underlying class of models adopted for this learning. We address the accuracy and reliability of our learned models.

Prediction: In this module, we make no assumptions on how the data is generated and focus on predicting the next outcome of the process based on past observations. In this context, we analyze Matrix and Tensor Completion Methods in providing such predictions and we analyze the accuracy of these prediction in the presence of noise, missing data.

Optimal Intervention and Reinforcement Learning (RL): A key ingredient of RL is a simulator that can estimate the value of a reward for a given intervention. In this module course, we build on techniques from RL as well as the first two parts to show how new intervention/control can be derived with better outcomes.

This course will consist of three hands-on projects, in which learners will apply knowledge gained in lectures, build models and implement algorithms to solve problems posed on real time series data sets.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visithttps://micromasters.mit.edu/ds/.

Taught by

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