It depends on your background and interests. If in doubt, please discuss this with your academic advisors and the Statistics advisors. By far the most common places to start are Stat 100, 101, 102, or 104 (which are introductions to applied statistics, each with its own flavor), or Stat 110 (which is an introduction to probability and is a necessary foundation for most of the upper-level Statistics courses).

Statistics is guided by a few fundamental principles but has applications in a great variety of different fields. To best serve the needs of students with a wide range of backgrounds and interests, the department has several versions of its introductory course: Stat 100, 101, 102, and 104. These courses introduce common core material in statistical inference at an introductory level, but with different areas of application emphasized. A student can take only one of these courses for credit.

Stat 100 is a general introduction to applied statistics, exploring applications in a wide range of fields. Stat 101 emphasizes applications to psychology and other behavioral sciences. Stat 102 emphasizes applications to biology and medicine. Stat 104 emphasizes applications to economics, and is faster-paced than Stat 100.

They are complementary, with very little overlap. Stat 100 (and its other flavors) is an introduction to statistical principles and concepts, as described above. Stat 110 is an introduction to probability. The emphasis of Stat 110 is on random variables and their distributions, and on how to use probability to quantify uncertainty and understand randomness. Aside from the very different emphasis, Stat 110 is taught at a much higher mathematical level than Stat 100/101/102/104. In particular, Stat 110 requires multivariable calculus at the level of Math 18 or above (the multivariable calculus course can be taken concurrently).

Stat 107 and 139 are the two main examples. Stat 107 explores business applications, while Stat 139 explores statistical methods for data analysis at a higher level than Stat 100. There are also various courses in other departments, such as Economics and Psychology, that rely heavily on the ideas from Stat 100/101/102/104.

Stat 111, 123, 170, and 171 have Stat 110 as their main or only prerequisite. There are also various courses in other departments, especially Applied Mathematics, Computer Science, and Economics, that rely heavily on the ideas from Stat 110.

They are complementary courses on the science of learning from data, with very different emphases: Stat 111 focuses the theory of statistical inference, while Stat 139 focuses on applications. Stat 111 is an introduction to both Bayesian and frequentist perspectives on inference. Stat 139 is an introduction to data analysis using linear regression models and R.

They are complementary courses on mathematical finance. Stat 123 focuses on derivatives, with an emphasis on interest rate derivatives and issues about trading them in real markets. Stat 170 focuses on the pricing of assets, portfolio analysis, and derivative pricing. The mathematical level of Stat 170 is somewhat higher than that of Stat 123, e.g., the course delves into topics such as Brownian motion, stochastic differential equations, and Monte Carlo methods.

Students contemplating a career in investment management, sales and trading, investment banking, hedge funds, or policy-making should consider taking Stat 123 and Econ 1723. Students aiming for a research-oriented (“quant”) position in finance, considering graduate school in finance or economics, or who want further mathematical depth in the subject, should consider taking Stat 170.

Yes.

Yes, since it is the same course as Stat 121.

Yes, one (and only one) course may be counted toward both a concentration and a secondary field. For example, a Computer Science concentrator can count Stat 110 both as a technical elective for the concentration and as a requirement for the Statistics secondary field.

Stat 210a can be substituted for Stat 110, and Stat 211a can be substituted for Stat 111 (these are the PhD-level analogs, and are taught at a much higher mathematical level than Stat 110 and 111). Aside from these, no substitutions for Stat 110 or 111 are allowed.

http://statistics.fas.harvard.edu/pages/undergraduate-statistics-general-information

has links to information and resources about the concentration. For further information, please contact the Statistics Student Coordinator, Kathleen Cloutier (cloutier@fas.harvard.edu), and/or the Directors of Undergraduate Study, Joe Blitzstein (blitz@fas.harvard.edu), Mike Parzen (mparzen@stat.harvard.edu), and Kevin Rader (rader@stat.harvard.edu).

The concentration requirements can be fulfilled via any of four tracks: a **General** track in core statistical principles and methods, a track in **Data Science**, a track in **Quantitative Finance**, and a track in **Bioinformatics and Computational Biology (BCB)**. The diploma and transcript do not list the student’s track; all four tracks lead to the same degree: Bachelor of Arts in Statistics.

The general track provides students with a set of methods for making decisions in the face of risk and uncertainty, and for making sense of complex data arising from many applications. The data science track explores the interface of statistics, computer science, and application areas, emphasizing topics such as prediction, machine learning, and analysis of massive data sets. The quantitative finance track gives strong preparation for many careers in finance and actuarial work, and involves a mixture of Statistics and Economics coursework. The BCB track mixes together biology, statistics, and computation, giving models and tools for studying biological data such as gene and protein sequences.

Multivariable calculus and linear algebra, to at least the level of Math 19a and 19b, are required. The most common way for concentrators to fulfill the math requirement is to take Math 21a and 21b, but there are a variety of other fairly common choices. Students considering graduate school in Statistics are strongly recommended to take additional mathematics courses, especially real analysis (e.g., Math 112).

Summer school courses do not count for concentration credit unless specifically approved by one of the Co-Directors of Undergraduate Studies in Statistics.

As of December 2015, there were 201 Statistics concentrators: 67 seniors, 71 juniors, and 63 sophomores. The concentration has grown dramatically in recent years; by way of contrast, as of December 2005 there were only 8 Statistics concentrators.

It varies from year to year, but typically about half of the graduating Statistics concentrators each year write senior theses. Note that writing a senior thesis is required for honors-eligibility and for joint concentrations.

Senior theses in Statistics cover a wide range of topics, across the spectrum from applied to theoretical. Typically, senior theses are expected to have one of the following three flavors:

1. Novel statistical theory or methodology, supported by extensive mathematical and/or simulation results, along with a clear account of how the research extends or relates to previous related work.

2. An analysis of a complex data set that advances understanding in a related field, such as public health, economics, government, or genetics. Such a thesis may rely entirely on existing methods, but should give useful results and insights into an interesting applied problem.

3. An analysis of a complex data set in which new methods or modifications of published methods are required. While the thesis does not necessarily contain an extensive mathematical study of the new methods, it should contain strong plausibility arguments or simulations supporting the use of the new methods.

A good thesis is clear, readable, and well-motivated, justifying the applicability of the methods used rather than, for example, mechanically running regressions without discussing the assumptions (and whether they are plausible), performing diagnostics, and checking whether the conclusions make sense. See http://www.stat.harvard.edu/Academics/senior_thesis_guidelines.pdf for further information.

Recent alumni have been working in a very wide variety of companies, such as data science positions in tech companies and analyst positions in banks, hedge funds, and consulting firms. Additionally, some alumni are pursuing graduate degrees in Statistics or Biostatistics. See http://www.stat.harvard.edu/alumni/AB.html for further information.

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