Statistics and Data Science

Massachusetts Institute of Technology via edX MicroMasters

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Details

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Provider

edX MicroMasters
Pricing

$1,500.00
Languages

English
Certificate

Certificate Available
Duration & workload

56 weeks, 10-14 hours a week

Found in

Overview

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 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/

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: Data Analysis in Social Science—Assessing Your Knowledge

Learn the methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest, and then assess that knowledge-- 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: 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 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

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/.
27 reviews
15 weeks, 10-14 hours a week, 10-14 hours a week
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If you have specific questions about this course, please contact us at [email protected].

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/.

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