MASSACHUSETTS INSTITUTE OF TECHNOLOGY · UNITED STATES
Statistics for Applications
Bachelor's · Taught in en
At a glance
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Duration
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IELTS
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Tuition / yr
en
Language
Bachelor's
Level
- Fall 2016Term Offered
- Professor Philippe RigolletInstructor
- Mathematics DepartmentDepartment
- Probability and StatisticsTopic
- Lecture NotesResource Type
- Lecture VideosResource Type
This undergraduate course, "Statistics for Applications," is offered by the Mathematics department at MIT. It covers fundamental concepts in statistics relevant for various applications. The course materials are available online, including lecture notes, lecture videos, and problem sets from the Fall 2016 offering, taught by Professor Philippe Rigollet. It delves into topics such as parametric inference, maximum likelihood estimation, hypothesis testing, regression, principal component analysis, and generalized linear models.
Curriculum
The course is structured into lectures covering a range of statistical topics. The specific breakdown of lectures and their associated topics are detailed below.
- Fall 2016
- IntroductionLectures 1-2
- Parametric InferenceLecture 3
- Maximum Likelihood EstimationLectures 4-5
- The Method of MomentsOld Lecture 6
- Parametric Hypothesis TestingLectures 7-10
- Testing Goodness of FitLectures 11-12
- RegressionLectures 13-16
- Bayesian StatisticsOld Lectures 17-18
- Principal Component Analysis (PCA)Lectures 19-20
- Generalized Linear ModelsLectures 21-24
Careers
While specific career outcomes for this individual course are not detailed, a strong foundation in statistics from MIT can open doors to a variety of analytical and data-focused roles across numerous industries.
- Data Scientist
- Statistician
- Data Analyst
- Machine Learning Engineer
- Quantitative Analyst
- Researcher
Frequently asked questions
- Is there a tuition fee for the "Statistics for Applications" course?
- The "Statistics for Applications" course materials are provided free of charge via MIT OpenCourseWare. Tuition fees apply to degree programs at MIT, not for accessing OCW content.
- What topics are covered in "Statistics for Applications"?
- The course covers fundamental statistical concepts including parametric inference, maximum likelihood estimation, hypothesis testing, regression, principal component analysis, and generalized linear models.
- Who taught the "Statistics for Applications" course?
- The "Statistics for Applications" course offering from Fall 2016 was taught by Professor Philippe Rigollet.
- What are the potential career paths after studying statistics at MIT?
- A strong foundation in statistics from MIT can lead to roles such as Data Scientist, Statistician, Data Analyst, Machine Learning Engineer, Quantitative Analyst, or Researcher.
- How can I apply to degree programs at MIT?
- Applications for graduate programs are submitted online via the MIT admissions portal, typically between October 1st and December 1st, and require transcripts, English proficiency scores, letters of recommendation, and a statement of purpose.
Entry requirements
Undergraduate DegreeAdmission to an undergraduate program at MIT.
Specific entry requirements for this particular course are not detailed, but admission to MIT as an undergraduate is a prerequisite. For graduate programs, a separate application process with specific academic and English requirements applies.
Fees
Tuition (international)This course is offered through MIT OpenCourseWare, which provides free access to course materials. Tuition fees are applicable for degree programs at MIT, but not for accessing OCW content.
Tuition (home)This course is offered through MIT OpenCourseWare, which provides free access to course materials. Tuition fees are applicable for degree programs at MIT, but not for accessing OCW content.
LivingNot applicable for this course content.
The materials for this course are provided free of charge via MIT OpenCourseWare. Information on tuition and living costs applies to degree-seeking students and varies by program.
How to apply
1. Create an Application AccountBegin the application process by creating an account on the MIT graduate admissions portal.
2. Complete the Application FormFill out all required sections of the online application, ensuring accuracy and selecting options from provided dropdown menus where available to avoid data mismatches.
3. Submit TranscriptsUpload a scanned PDF copy of your academic transcript(s) from each institution attended. Official transcripts will be required upon admission.
4. Provide English Proficiency ScoresFor international applicants, upload scanned copies of your IELTS or TOEFL exam scores.
5. Request Letters of RecommendationArrange for three letters of recommendation (at least two academic if still in school) to be submitted electronically through the application system. Ensure recommenders receive instructions.
6. Write a Statement of PurposeCraft a statement detailing your scientific goals, how they align with the program at MIT (mentioning specific faculty), and your preparedness through experiences. Discuss any challenges overcome and potential contributions to the MIT community.
7. Pay Application FeeSubmit the required application fee by the deadline to ensure your application is considered complete.
Scholarships
Departmental Fellowships$3,643.76 per month (effective June 1, 2023)
Research Assistantship (RA)Varies
Teaching Assistantship (TA)Varies
Outside FellowshipsVaries
Accreditation
MIT is accredited by the New England Commission of Higher Education (NECHE).
Frequently asked questions
Is there a tuition fee for the "Statistics for Applications" course?
+
The "Statistics for Applications" course materials are provided free of charge via MIT OpenCourseWare. Tuition fees apply to degree programs at MIT, not for accessing OCW content.
What topics are covered in "Statistics for Applications"?
+
The course covers fundamental statistical concepts including parametric inference, maximum likelihood estimation, hypothesis testing, regression, principal component analysis, and generalized linear models.
Who taught the "Statistics for Applications" course?
+
The "Statistics for Applications" course offering from Fall 2016 was taught by Professor Philippe Rigollet.
What are the potential career paths after studying statistics at MIT?
+
A strong foundation in statistics from MIT can lead to roles such as Data Scientist, Statistician, Data Analyst, Machine Learning Engineer, Quantitative Analyst, or Researcher.
How can I apply to degree programs at MIT?
+
Applications for graduate programs are submitted online via the MIT admissions portal, typically between October 1st and December 1st, and require transcripts, English proficiency scores, letters of recommendation, and a statement of purpose.