Ninety percent of the world’s data was created in the past few years. Faced with overwhelming amounts of data, organizations are struggling to extract actionable insights.
To address this challenge, MIT Professional Education has partnered with the MIT Institute for Data, Systems, and Society (IDSS) to offer Data Science: Data to Insights, a new, six-week online course focusing on analytics. Designed for data scientists, business analysts, engineers, and technical managers this focused course provides an introduction to fundamental principles and addresses a broad range of questions, for example:
- What should I know about the latest trends in machine learning?
- How do I conduct hypothesis testing using my data?
- How can I extract preferences from customer data?
- How can I understand interactions networks using graphical models?
- How do I assess performance of my prediction algorithms?
By completing this course, you’ll be well prepared to address your company’s most pressing data analytics challenges.
What You'll Learn
- Learn how to apply data science techniques through case studies to more effectively address your organization’s challenges.
- Discover common pitfalls in big data analytics and how to avoid them.
- Develop a better understanding of machine learning and see how it works in practice with hands-on exercises.
- Interpret models, and learn the right questions to ask to make better business decisions.
Devavrat Shah, Co-Director Professor, Laboratory for Information and Decision Systems (LIDS), Computer Science and Artificial Intelligence Laboratory (CSAIL) and Operations Research Center (ORC)
Devavrat Shah, Co-Director Professor, Laboratory for Information and Decision Systems (LIDS), Computer Science and Artificial Intelligence Laboratory (CSAIL) and Operations Research Center (ORC) at MIT
Dr. Shah received his Bachelor of Technology in Computer Science and Engineering from the Indian Institute of Technology, Bombay, in 1999. He received the Presidents of India Gold Medal, awarded to the best graduating student across all engineering disciplines. He received his Ph.D. in Computer Science from Stanford University. His doctoral thesis won the George B. Dantzig award from INFORMS for best dissertation in 2005. After spending a year between Stanford, Berkeley and MSRI, he started teaching at MIT in 2005. In 2013, he co-founded Celect, Inc. to commercialize his research at MIT.
Philippe Rigollet, Co-Director Associate Professor, Mathematics department and Center for Statistics
Philippe Rigollet, Co-Director Associate Professor, Mathematics department and Center for Statistics at MIT
At the University of Paris VI, Dr. Rigollet earned a B.S. in statistics in 2001, a B.S. in applied mathematics in 2002, and a Ph.D. in mathematical statistics in 2006. He has held positions as a visiting assistant professor at the Georgia Institute of Technology, and as an assistant professor at Princeton University.
Guy Bresler Assistant Professor, Electrical Engineering and Computer Science, LIDS and IDSS
Guy Bresler Assistant Professor, Electrical Engineering and Computer Science, LIDS and IDSS at MIT
He received his Ph.D. from the Department of Electric Engineering and Computer Science at UC Berkeley, and was a postdoc at MIT.
Tamara Broderick Assistant Professor, Institute for Data, Systems, and Society (IDSS), Electrical Engineering and Computer Science (EECS) Department
Tamara Broderick Assistant Professor, Institute for Data, Systems, and Society (IDSS), Electrical Engineering and Computer Science (EECS) Department at MIT
Prior to joining MIT, she earned her Ph.D. in Statistics at UC Berkeley, an AB in Mathematics from Princeton University, a Master of Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge, an MPhil by research in Physics from the University of Cambridge, and an MS in Computer Science from UC Berkeley. Dr. Broderick was awarded the Evelyn Fix Memorial Medal and Citation, the Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize.
Victor Chernozhukov Professor, Department of Economics; Center for Statistics
Victor Chernozhukov Professor, Department of Economics; Center for Statistics at MIT
David Gamarnik Professor, Sloan School of Management
David Gamarnik Professor, Sloan School of Management at MIT
Dr. Gamarnik is a member of the Institute of Mathematical Statistics, Bernoulli Society, INFORMS, and the American Mathematical Society. He serves on the editorial board of both “Operations Research” and the “Annals of Applied Probability,” Notably, he was the recipient of the 2004 Erlang Prize from the INFORMS Applied Probability Society, as well as two National Science Foundation grants in 2007. He holds a B.A. in mathematics from New York University and a Ph.D. in operations research from MIT.
Stefanie Jegelka Assistant Professor, Institute for Data, Systems, and Society (IDSS), Electrical Engineering and Computer Science (EECS) Department
Stefanie Jegelka Assistant Professor, Institute for Data, Systems, and Society (IDSS), Electrical Engineering and Computer Science (EECS) Department at MIT
Prior to joining MIT, she was a postdoc in the AMPlab and computer vision group at UC Berkeley, and a Ph.D. student at the Max Planck Institutes in Tuebingen and at ETH Zurich.
Jonathan Kelner Associate Professor, Department of Mathematics and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
Jonathan Kelner Associate Professor, Department of Mathematics and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT
Dr. Kelner received a B.A. in mathematics from Harvard in 2002 and the David Mumford Award as the top Harvard graduate in mathematics. He received his M.S. and Ph.D. degrees from MIT in Electrical Engineering and Computer Science in 2005 and 2006. Dr. Kelner was a Member of IAS 2006-2007 before joining the MIT faculty in applied mathematics as an assistant professor in 2007. He was named associate professor in 2012. He is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
Ankur Moitra Assistant Professor, Department of Mathematics and member of the Computer Science and Artificial Intelligence Lab (CSAIL)
Ankur Moitra Assistant Professor, Department of Mathematics and member of the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT
Dr. Moitra received his B.S. in electrical and computer engineering from Cornell in 2007. He completed his M.S. in 2009 and his Ph.D. in 2011 in computer science at MIT. Notably, he received a George M. Sprowls Award and a William A. Martin Award for best thesis for his doctoral and master’s dissertations. He then spent two years as an NSF CI Fellow at the Institute for Advanced Study while he was a senior postdoc in the computer science department at Princeton University.
Caroline Uhler Assistant Professor, Institute for Data, Systems, and Society (IDSS), Electrical Engineering and Computer Science (EECS) Department
Caroline Uhler Assistant Professor, Institute for Data, Systems, and Society (IDSS), Electrical Engineering and Computer Science (EECS) Department at MIT
By engaging in comprehensive lectures from our MIT IDSS faculty members, you’ll acquire the theories, strategies, and tools you need to convert gigabytes of data into meaningful insights.
Over the course of six weeks, you will review a broad spectrum of topics including recommendation engines, regressions, network and graphical modeling, anomaly detection, hypothesis testing, and machine learning. Using case studies and hand-on exercises, participants will have the opportunity to practice and increase their data analysis skills. After completing this course, you will be well prepared to:
- Uncover unexpected patterns and anomalies in your data
- Determine what data you need and how to design experiments
- Use foundational and emerging analytics techniques
- Understand common pitfalls in big data analytics and how to avoid them
- Comprehend how machine learning works in practice
- Interpret model results and make more effective decisions
- Overcome the challenges and constraints associated with scaling big data algorithms
You will also receive:
- 90-day access to archived course materials: Videos, discussion boards, and content
- Complete course transcript: Synchronized video transcripts and a compiled transcript of all course lectures
MIT Professional Education Digital Programs are designed to fit the schedules of busy professionals. That’s why this course is self-paced and available online 24 hours a day, 7 days a week.
Each video module is pre-recorded, enabling you to watch it anytime. While you may complete the program as quickly as you wish, most participants find it beneficial to adhere to the weekly schedule and participate in online discussion forums along the way.
The course requires a time commitment of three-to-four hours a week comprised of videos, assigned reading, and assignments.
Access to our courses requires an Internet connection, as videos are only available via online streaming, and cannot be downloaded for offline viewing. Please take note of your company's restrictions for viewing content and/or firewall settings.
MIT Institute for Data, Systems, and Society (IDSS) is committed to addressing complex societal challenges by advancing education and research at the intersection of statistics, data science, information and decision systems, and social sciences. Spanning all five schools at MIT, IDSS embraces approaches and methods from disciplines including statistics, data science, information theory and inference, systems and control theory, optimization, economics, human and social behavior, and network science. These disciplines are relevant both for understanding complex systems, and for presenting design principles and architectures that allow for the systems’ quantification and management. IDSS seeks to integrate these areas—fostering new collaborations, introducing new paradigms and abstractions, and utilizing the power of data to address societal challenges. Read more about IDSS at idss.mit.edu.
EARN A CERTIFICATE OF COMPLETION AND CEUS
CERTIFICATE OF COMPLETION
To earn a Certificate of Completion in this course, participants should watch all the videos, and complete all assessments by the due date, with an overall average of 80 percent success rate. Keep in mind that the 80-percent pass rate is across all assessments, and is your overall average “grade” for the course.
Upon successful completion of the course and all assessments, a Certificate of Completion will be awarded by MIT Professional Education after the course has ended.
Continuing Education Units (CEUs)
Participants of this course who successfully complete all course requirements in order to earn a Certificate of Completion are eligible to receive 1.3 Continuing Education Units (1.3 CEUs).
CEUs are a nationally recognized means of recording noncredit/non-degree study and are accepted by many employers, licensing agencies, and professional associations as evidence of a participant’s serious commitment to the development of a professional competence.
Acceptance of CEUs depends on the organization to which one is submitting them. If your employer requires any additional information, MIT Professional Education can answer questions and provide information, but we cannot guarantee that any particular organization will accept our CEUs.
CEUs are based on hours of instruction. For example: One CEU = 10 hours of instruction. CEUs may not be applied toward any MIT undergraduate or graduate level course.
WHO SHOULD PARTICIPATE
This course is designed for data scientists and data analysts, as well as professionals who wish to turn large volumes of data into actionable insights. Because of the broad nature of the information, the course is well suited for both early career professionals and senior managers.
Participants may include:
- Technical managers
- Business intelligence analysts
- Management consultants
- IT practitioners
- Business managers
- Data science managers
- Data science enthusiasts
The course features five modules:
Module 1: Making sense of unstructured data
Modern businesses, scientific and engineering laboratories, and Web 2.0 generate vast quantities of data, often without existing labels. To make sense of this data, a principal challenge becomes to discover patterns or latent structure where none is known beforehand. For instance, we might want to discover an organic organization of documents, such as articles collected from the New York Times or Wikipedia, into distinct groups representing topics or themes. We might want to discover latent communities in social networks, such as Facebook or Twitter. We might to figure out which aspects of text or images, such as those on Imgur or Google images, capture the important information encapsulated in these data formats. In this module, we offer an overview of modern techniques for addressing these problems across a variety of different types of data. We demonstrate the usefulness of these methods in a number of case studies.
- Spectral Clustering, Components and Embeddings
- Case Studies
Module 2: Regression and Prediction
The module provides an introduction to regression, combining both classical and modern views. We will begin with bivariate and multivariate regression for purposes of prediction and causal inference, followed by logistic and nonlinear regression. We then go over a menu of modern prediction methods that aim to solve prediction problems well using high-dimensional data, namely lasso, ridge and various modifications. We shall discuss regression trees, boosted trees, and random forests, followed by a basic view of neural networks, all for prediction purposes. We will discuss the assessment of prediction performance using validation samples and cross-validation. We will conclude with a brief discussion of how to use these methods for inferring causal effects of a treatment in randomized control trials and in the presence of confounding.
- Classical Linear & nonlinear regression & extension
- Modern Regression with High-Dimensional Data
- The use of modern Regression for causal inference
- Case Studies
Module 3: Classification, Hypothesis Testing and Anomaly Detection
This module provides a basic introduction to statistical methods of classification, testing hypothesis and its applications, including detection of statistical anomalies, detection of frauds, spams, and other malicious behaviors. The course will begin by describing informally the range of applications of these techniques and then move on to methods, mostly evolving around the methods of classifications. Those include binary classification, logistic and probit regression, perceptron method and neural networks method, support vector machines, and others. Several examples will be introduced to illustrate the application of the discussed methods. Finally, the course will discuss the limitations of the methods, the importance of careful usage and the dangers of misuse of the discussed methods.
- Hypothesis Testing and Classification
- Deep Learning
- Case Studies
Module 4: Recommendation Systems
Recommendation systems have become primary way to discover relevant information from vast amounts of data. Examples include media recommendations by Netflix, YouTube and Spotify; online dating suggestions by Tinder; news feeds by Facebook; and product recommendations by Amazon and more. This module provides a systematic overview of principles and algorithms for designing and developing recommendation systems. The content is exemplified using concrete case studies.
- Recommendations and ranking
- Collaborative filtering
- Personalized recommendations
- Case Studies
- Wrap-up: Parting remarks and challenges
Module 5: Networks and Graphical Models
From social networks to gene regulatory networks, networks form the backbone for many of the processes we care about. Local interactions between basic entities in a network give rise to large-scale network effects such as the spread of information or ideas. How do we make use of network data to understand the behavior or functionality of the network? This module provides a systematic overview of methods for analyzing large networks, determining important structure in such networks, and for inferring missing data. An emphasis is placed on graphical models both as a powerful way to model network processes and to facilitate efficient statistical computation. The course content is illustrated via case studies.
- Graphical Models
- Case Studies
Who can register for this course?
Unfortunately, US sanctions do not permit us to offer this course to learners in or ordinarily residing in Iran, Cuba, Sudan, and the Crimean region of Ukraine. MIT Professional Education truly regrets that US sanctions prevent us from offering all of our courses to everyone, no matter where they live.
What do I need to do to register for the course?
Go to mitprofessionalx.mit.edu, click on the course you would like to register for, and click “Add to Cart.” You may be prompted to first register for a mitprofessionalx account if you do not have one already. Complete this process, then continue with checkout and pay for the course. Once you are given access to the course, the first assignment will be to complete the mandatory entrance survey before you can gain access to the videos and other course materials.
How do I register a group of participants?
There are two ways to register multiple individuals at once.
- Once the course is added to your cart, you can select the number of enrollments you would like to purchase. You can then pay using a valid credit card.
- For a group of 5 or more individuals, you can pay via invoice. To be invoiced, please email email@example.com with the number of individuals in your group, and instructions to register will be provided. Please note that our payment terms are net zero, and all invoices must be paid prior to the course start date. Failure to remit payment before the course begins will result in removal from the course. No extensions or exceptions will be granted.
What is the registration deadline?
Individual registrations must be completed by October 4, 2016. For group sales, purchases can take place up until September 27, 2016. Please note that once registration has closed, no late registrations or cancellations will be granted.
**The registration deadline for individual and group orders has been extended through end of day October 11, 2016. In order to earn a Certificate of Completion, you must still submit all assessments by November 14, 2016.
How should I pay?
Individual registrants must complete registrations and pay online with a valid credit card at the time of registration. MIT Professional Education accepts globally recognized major credit or debit cards that have a Visa, MasterCard, Discover, American Express or Diner's Club logo. Invoices will not be generated for individuals, or for groups of less than 5 people. However, all participants will receive a payment receipt. Payment must be received in full; payment plans are not available.
When will I get access to the course site?
Instructions for accessing the course site will be sent to all paid registrants via email prior to the course launch date. In order to receive these instructions, please add firstname.lastname@example.org to your “trusted senders” list. If you have not received these instructions by the course start date, visit your account dashboard to login and start the course on the advertised course start date.
Participants are required to provide some personal information via a short mandatory course entrance survey. You will be able to access the survey on the course start date, October 4, 2016. Please be advised that a failure to provide said information will mean that participants will be unable to access course material.
Please see our Terms of Service page for our detailed policies, including terms and conditions of use.
I need to cancel my registration. Are there any fees?
Cancellation requests must be submitted to email@example.com. Cancellation requests received after September 20, 2016 will not be eligible for a refund. To submit your request, please include your full name and order number in your email request. Refunds will be credited to the credit card used when you registered and may take up to two billing cycles to process. MIT Professional Education Digital Programs and edX have no obligation to issue a refund after September 20, 2016, but if you believe a refund is warranted, please email us at firstname.lastname@example.org.
Can I transfer/defer my registration for another session or course?
Admission and fees paid cannot be deferred to a subsequent session; however, you may cancel your registration and reapply at a later date.
Can someone else attend in my place?
We cannot accommodate any substitution requests at this time. Please review the time commitment section and course schedule
How do I know if this course is right for me?
Carefully review the course description page, which includes a description of course content, objectives, and target audience, and any required prerequisites.
Are there prerequisites or advance reading materials?
The course is open to any interested participant. No advance reading is required. Ability to write code/programming experience not a requirement.
Who will be participating in this course?
Professionals with diverse personal, business, and academic backgrounds from the U.S. and around the world will participate. They include scientists, engineers, technicians, managers, consultants, and others, and they come from industry, government, military, non-profit, and academia.
How long is the course?
The course is held over six weeks, and is entirely asynchronous. Lectures are pre-taped and you can follow along when you find it convenient, as long as you finish all required assignments by November 14, 2016.. You may complete all assignments before the due date, however, you may find it more beneficial to adhere to a weekly schedule so you can stay up-to-date with the discussion forums.
What is the time commitment of this course?
MIT Professional Education Digital Programs are designed to fit the schedules of busy professionals. That’s why each course is self-paced and available online 24 hours a day, 7 days a week. Each video module is pre-taped, enabling you to watch it at any time. While you may complete all the assignments in rapid succession, most participants find it beneficial to adhere to the weekly schedule and participate in online discussion forums along the way. There are approximately two hours of video every week. You will spend additional time on multiple choice assessments, readings, and discussion forums. Most participants will spend about 3 - 4 hours a week on course-related activities.
How long will the course material be available online?
The materials will be available to registered and paid participants for 90 days after the course end date, February 12, 2017. No extensions may be granted.
What reference materials will be available at the end of the course?
Participants will have 90-day access to the archived course (includes videos, discussion boards, content, and Wiki).
What materials will participants keep at the end of the course?
Participants will take away program materials, and resources presented in the course Wiki.
Will I receive an MIT Professional Education Certificate?
Participants who successfully complete the course and all assessments will receive a Certificate of Completion. This course does not carry MIT credits or grades, however, an 80% pass rate is required in order to receive a Certificate of Completion.
Will I receive MIT credits?
This course does not carry MIT credits. MIT Professional Education offers non-credit/non-degree professional programs for a global audience. Participants may not imply or state in any manner, written or oral, that MIT or MIT Professional Education is granting academic credit for enrollment in this professional course. None of our Digital courses or programs award academic credit or degrees. Letter grades are not awarded for this course.
Will I earn Continuing Education Units (CEUs)?
Course participants who successfully complete all course requirements are eligible to receive 1.3 Continuing Education Units (CEUs) from MIT Professional Education.CEUs are a nationally recognized means of recording non-credit/non-degree study. They are accepted by many employers, licensing agencies, and professional associations as evidence of a participant’s serious commitment to the development of a professional competence. CEUs are based on hours of instruction. For example: One CEU = 10 hours of instruction. CEUs may not be applied toward any MIT undergraduate or graduate level course.
After I complete this course, will I be an MIT alum?
Participants who successfully complete a Digital Programs course are considered MIT Professional Education Alumni. Only those who complete an undergraduate or graduate degree are considered MIT alumni.
Are video captions available?
Each video for this course has been transcribed and the text can be found on the right side of the video when the captions function is turned on. Synchronized transcripts allow students to follow along with the video and navigate to a specific section of the video by clicking the transcript text. Students can use transcripts of media-based learning materials for study and review.
Access our courses requires an Internet connection, as videos are only available via online streaming, and cannot be downloaded for offline viewing. Please take note of your company's restrictions for viewing content and/or firewall settings. Our courseware works best with current versions of Google Chrome, Firefox, or Safari, or with Internet Explorer version 10 and above. For the best possible experience, we recommend switching to an up-to-date version of Chrome. If you do not have Chrome installed, you can get it for free here: http://www.google.com/chrome/browser/
We are unable to fully support access with mobile devices at this time. While many components of your courses will function on a mobile device, some may not.
I have never taken a course on the edX platform before. What can I do to prepare?
Prior to the first day of class, participants can take a demonstration course on edx.org that was built specifically to help students become more familiar with taking a course on the edX platform.
What are the technical requirements to participate in this course?
Our courseware works best with current versions of Google Chrome, Firefox, or Safari, or with Internet Explorer version 10 and above. For the best possible experience, we recommend switching to an up-to-date version of Chrome. If you do not have Chrome installed, you can get it for free here: http://www.google.com/chrome/browser/