Anyone is welcome to enroll in the MITx MicroMasters Program in Statistics and Data Science (SDS). The program does not require learners to possess any specific academic background (such as an undergraduate degree) or to take any specific courses beforehand. However, SDS MicroMasters courses are graduate-level and they assume learners will possess proficiency in several topic areas.
Learners will excel in the SDS MicroMasters courses only if they are comfortable with mathematical reasoning, single- and multi-variable calculus, and Python programming before beginning the program. Later courses may require additional pre-existing proficiencies, like linear algebra. Learners attempting to take SDS MicroMasters courses without adequate proficiency in the required topic areas are likely to find the courses extremely challenging to complete successfully.
Each SDS MicroMasters course lists specific pre-requisites—the knowledge and skills that learners are assumed to be proficient with before starting the course. Because each Program Track includes a different subset of SDS courses, the overall pre-requisites for each Program Track will vary.
A list of each SDS course's pre-requisites is presented below, along with a collection of resources suitable for preparing to take the SDS courses, for learners lacking the necessary knowledge and skills—or wishing to refresh them.
SDS Courses: Pre-Requisite Knowledge and Skills
Below is a list of the academic topic areas required for each SDS MicroMasters course—topics which the courses will assume learners are proficient with before beginning them. The courses are listed in the order that most learners would generally be expected to take them—though no learner is required to take all of the courses to complete the program. The first three courses all function as excellent entry points for the program. See this section below for more information about Recommended Course Order.
As the Assessment Course 14.310Fx Data Analysis in Social Science — Assessing Your Knowledge consists solely of a Final Exam testing the knowledge and skills learned in its Content Course, 14.310x Data Analysis for Social Scientists, only requirements for 14.310x are listed here. Likewise, the Capstone Exam course requires the completion of and tests only knowledge and skills learned from the courses in each SDS MicroMasters Program Track, so it is not listed here.
Course Pre-Requisites
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6.431x Probability - The Science of Uncertainty and Data
- Comfort with mathematical reasoning
- Undergraduate single- and multi-variable calculus
- Including familiarity with: sequences, limits, infinite series, the chain rule, ordinary and multiple integrals
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6.86x Machine Learning with Python: From Linear Models to Deep Learning
- Familiarity with vectors and matrix mathematics
- Undergraduate single- and multi-variable calculus
- Undergraduate Python programming (ex: 6.00.1x Introduction to Computer Science and Programming with Python)
- Undergraduate probability theory (ex. 6.431x Probability)
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14.310x Data Analysis for Social Scientists (Content Course)
- No prior preparation in probability and statistics required
- Undergraduate algebra
- Undergraduate single- and multi-variable calculus
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18.6501x Fundamentals of Statistics
- Familiarity with vectors and matrix mathematics
- Undergraduate single- and multi-variable calculus
- Undergraduate probability theory (ex. 6.431x Probability)
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6.419x Data Analysis: Statistical Modeling and Computation in Applications
- Undergraduate single- and multi-variable calculus
- Undergraduate linear algebra
- Undergraduate Python programming (ex: 6.00.1x Introduction to Computer Science and Programming with Python)
- Undergraduate probability theory and statistics (ex. 6.431x Probability, 18.6501x Fundamentals of Statistics)
- Undergraduate machine learning (ex. 6.86x Machine Learning with Python)
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IDS.S24x Learning Time Series with Interventions
- Familiarity with complex numbers
- Undergraduate multi-variable calculus
- Undergraduate linear algebra
- Undergraduate Python programming (ex: 6.00.1x Introduction to Computer Science and Programming with Python)
- Undergraduate probability theory and statistics (ex. 6.431x Probability, 18.6501x Fundamentals of Statistics)
Important Details
No Pre-requisite Courses
Some pre-requisites in the list above refer to other SDS courses as examples. This does not mean learners must take the example course first. Rather, that SDS course is provided as an example of a course sufficient to fulfill the knowledge and skills requirement. Other courses covering the same topic area at the indicated level would also be sufficient.
- For instance, 6.86x Machine Learning with Python requires “undergraduate-level probability theory” and provides as an example the SDS course 6.431x Probability. Learners who wish to start the SDS MicroMasters Program with 6.86x but lack a pre-existing background in probability theory could either choose to wait to start the program with 6.431x, complete one of the probability courses listed below, or acquire the appropriate knowledge and skills through any other means.
Social Sciences Courses
The Social Sciences Track and the Time Series and Social Sciences Track both require learners to take the Content Course 14.310x Data Analysis for Social Scientists before taking the required Assessment Course, 14.310Fx Data Analysis in Social Science: Assessing Your Knowledge. The logistics of these courses are more complex than the other SDS MicroMasters courses.
- Learners pursuing either of these Program Tracks read this FAQ article for details.
Python Programming Requirement
Python programming is not taught or reviewed in any SDS course, including 6.86x, IDS.S24x, or 6.419x—but proficiency is expected as a pre-requisite for these courses. For learners without a strong knowledge of Python, we recommend completing Introduction to Computer Science and Programming with Python for free on edX—or another free introductory Python course of their preference
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- Learners should be proficient with at least the following in Python: functions, tuples and lists, mutability, recursion, dictionaries, and object-oriented programming. 6.86x and 6.419x make use of various Python packages, including pytorch, scikit-learn, and others.
Courses are Self-Contained
The SDS MicroMasters courses are self-contained and do not require learners to purchase any textbooks or other outside materials. However, the Probability course does closely follow the text of Introduction to Probability, 2nd edition, by Bertsekas and Tsitsiklis, Athena Scientific, 2008. While the course provides all of the materials that learners will need, and the textbook is not required, the SDS team does recommend it. In the past, learners have found the textbook to be a useful complement to the course material.
Recommended Course Order
Pre-requisite knowledge and skills for SDS MicroMasters courses are not the only factors useful to determining what order a learner should take the program's courses in. While the program does not mandate learners take courses in a specific order, several SDS courses do build upon content and concepts introduced in other courses, and the overall challenge level of courses varies. See this FAQ article for more information about the SDS Team's Recommended Course Order Considerations
Recommended Preparation Resources
While the SDS MicroMasters team has not officially prepared or endorsed any courses or materials for learners to prepare for SDS MicroMasters courses, many freely available online courses are suitable for learning the required knowledge and skills. The MicroMasters Support Team has gathered a short list of online courses and collections of course material that we can recommend to learners.
Remember: Learners are not required to take any of the following courses! No specific courses are pre-requisites for any SDS MicroMasters course or the program overall. Rather, certain knowledge and skills are pre-requisites for the SDS MicroMasters program. These resources are offered as options to acquire or refresh the knowledge and skills required by the SDS MicroMasters Program’s courses. None of the resources listed below need to be paid for—all are either free or have a free option.
Presented below are three different types of resources:
- Courses offered on edX.org (edX) or MITx Online: these are live, with live discussion forums and TA’s to answer questions. However, they may be instructor-led and only available during specific time periods. These courses may not always be available when desired
- Courses offered on MIT Open Courseware (OCW): these are collections of course materials from residential MIT courses, so they are not formatted as interactive online courses in the same way that the edX courses are, and do not offer live support such as discussion forums or support from TAs. However, the material is archived, so these courses are self-paced and available at any time
- Courses offered on the MIT Open Learning Library (OLL): these are interactive online courses created from select OCW course collections, providing opportunities to learn at your own pace, while engaging with problems and providing instant feedback on coursework. OLL courses do not offer live support such as discussion forums or support from TAs, but the courses are self-paced and available at any time
For all learners:
- edX: Introduction to Computational Thinking and Data Science
- OCW: Introduction to Computational Thinking and Data Science
For learners without a strong knowledge of Python:
- edX: Introduction to Computer Science and Programming with Python
- OCW: Introduction to Computer Science and Programming with Python
For learners without a strong knowledge of calculus:
- OCW: Single Variable Calculus
- OLL: Calculus 1A: Differentiation
- OLL: Calculus 1B: Integration
- OLL: Calculus 1C: Coordinate Systems & Infinite Series
- OCW: Multivariable Calculus on MIT Open Courseware
For learners without a strong knowledge of linear algebra:
- OCW: Linear Algebra (In-Depth)
- OLL: Linear Algebra
For learners without a strong knowledge of Probability:
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