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Is a PhD In Statistics Worth It?

At face value, a statistics PhD seems like a sound career investment, the ticket to higher paying jobs and career growth.

It’s no surprise, then, that one of the most common questions we hear is: Are statistics PhD programs worth it for data science jobs?

If we’re just looking at PhD in statistics salaries, sure, from a purely financial perspective, a PhD might be a good investment in your data science career. There’s a strong financial case you can make for earning one.

But beyond the great statistics PhD salary data, there are many other variables that make the answer a little less clear. When you think about the time commitment - almost 8 years - and the average salaries between master’s and PhD students in statistics, you’ll see that there are a number of trade-offs and that the bump in earnings isn’t so significant as to be a no-brainer.

That’s not to say there aren’t tons of great benefits of a PhD, because there are. For one, a PhD provides much more specialized knowledge, which can help you land competitive, more senior-level jobs. (It’s a preferred qualification for many Google jobs, in fact.) And of course, the average starting salaries for statistics PhDs are very enticing.

To help answer the question, “Is a PhD worth it?” we took a closer look at salaries for data scientists and statistics PhDs, as well as some of the pros and cons of pursuing a PhD for your data science career.

PhD In Stats: Salary Comparison

It’s probably not all that surprising that a PhD can increase your earnings, often by 2X or 3X. That’s really across the board, in all industries. For example, according to the Bureau of Labor Statistics, median weekly pay for a PhD ($1,885) was 45% higher than bachelor’s ($1,305) in 2020.

When you take a closer look at PhDs by field, though, PhDs in math and statistics have some of the best starting salaries in any industry. According to 2019 Survey of Doctorate Recipients data , recipients of a PhD in statistics have an average median starting salary of $140,000 (when pursuing a job in industry). That’s better than business administration, economics, and engineering:

A PhD also results in a pretty big bump in salary compared to just earning a bachelor’s or master’s degree. For instance, median salaries for statistics PhD are two times that of bachelor’s recipients and 1.5 times that of master’s of statistics recipients:

In other words, if you’re looking at the question through a purely financial lens, yes, a PhD in statistics is worth it.

But there’s one caveat. The lifetime earnings of a PhD vs a master’s recipient in statistics isn’t all too significant (on average about $3.6 million vs $3.45 million).

PhD in Stats: The Skills Bump

A big reason why starting salaries are so good for statistics PhDs is that your knowledge will be much more specialized.

Master’s in statistics programs tend to provide broad knowledge in the field. You’ll get a strong foundation of the fundamentals, and become well-versed in many different statistical concepts and methodologies. But you likely won’t get the depth of knowledge that you would from a PhD program.

A PhD differs quite a bit, and these programs are built around research. Here’s how it usually works: After completing initial coursework (usually 2 years), you’ll choose an area to focus your research. And then, you’ll spend 3-5 years researching that topic and preparing a dissertation on it.

The difference in focus, therefore, provides you with very specialized knowledge, and that’s a big reason why starting PhD salaries tend to be so high.

Is It Worth It? Delayed Earnings and Career Goals

Of course, the biggest trade-off in getting all this knowledge is the time commitment. PhD candidates in statistics spend nearly a decade – 7.75 years on average – earning the credential.

And that commitment is something you have to consider to really know if it’s worth it to you. Do you want to make this time commitment and spend the next 8 years researching a topic?

As a master’s recipient, you’ll gain a lot of useful professional skills and can jump right into a career. Sure, you might fully understand advanced statistical methodologies, but you will have a strong grasp of the fundamentals. And you can learn a lot to advance your career with professional development and on-the-job training.

Although they spend a lot of time researching a topic, PhDs do have one advantage: They’re often qualified for more senior-level data science jobs. At Google, for example, a PhD is a preferred qualification for many of their data science jobs, and that’s increasingly true for many FAANG companies.

A PhD Is a Good Investment, But With One Caveat

There’s a lot of reasons why you might consider a PhD in statistics. Salaries, for one, are some of the highest in data science , and job growth for statisticians is about 30% year-over-year. You’ll also have a lot of specialized knowledge that will increase your worth and prepare you for senior-level positions.

But here’s the caveat:

Even if you earn a PhD, you’ll still have a skills gaps that you need to fill, especially if you’re interested in a career in data science. There will skills - like coding or machine learning - that you might need to brush up on.

So if you’re expecting that a PhD is a ticket to a FAANG job, it’s not. But the specialized knowledge that it brings is, increasingly, a preferred qualification.

If you want to read more topics that are similar to this one, consider reading more through our blog where we dive into topics such as our PostgreSQL Interview Questions Guide , Machine Learning Case Studies , and even this article on ‘ Is Data Science a Good Career? ’

Learn and grow more by using resources here at Interview Query !

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Mathematics and Statistics

Choosing Statistics PhD: Harvard vs Berkeley?

By Ryuk February 17, 2021 in Mathematics and Statistics

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how is harvard a good fit given your research interests? I feel like they're more into biostat/applied stuff..although Cynthia Dwork is there...

If you're into probability, deep learning etc, then berkeley and potentially other schools out of those 24 would be better fits.

icantdoalgebra , bayessays , Ryuk and 1 other

Thanks for the reply!

I know that Berkeley is a better fit for my research interests and I know that research fit is one of the most important parts of choosing a PhD.

However, I also hear people say that you should choose a place that you would enjoy and fit in for the next 5 years. I think I would enjoy my time at Harvard more. In addition, Harvard grads seem to do extremely well on the job market.

There are young faculty at Harvard with similar research interests as me, but obviously there are many more options at Berkeley. I am having difficulty comparing my desire to live in Boston and be a part of the Harvard community with the exact research fit at Berkeley.

Are Harvard grads getting jobs at Google, Microsoft, Facebook, etc Research? I have not been stalking recently lol, but I cannot recall any examples (I wouldn't mind seeing some if you know any!). And the ones who get the top academic placements (aka berkeley and stanford TT professorships) seem to have worked in causal inference. If that is a strong area of your interests, then sure, go ahead.

I turned down Harvard (and had similar interests to yours and even a similar situation, haha), cause I thought there were only 1-2 people with similar interests as mine.

another bit of advice when you have to decide between almost all the top programs is to not get hung up on the top 2 ones that are ranked just after stanford. I think other ones you should also consider carefully given your interests are Columbia, UPenn Wharton, Duke, Yale (only if you wanna do pure math stat; imo they're some of the best at that). Make sure you review these carefully as well.

@MathStat

You're right that only a few people at Harvard do what I'm interested in. I guess I'm wondering how much I should force it if it's a place I really want to be.

A quick glance at your profile seems to tell me that you are currently at U Chicago. I also have an offer from them. If that's true and you really were in a similar situation as me, why did you pick Chicago over Berkeley and Harvard?

Chicago over Harvard was a no-brainer given my research interests. Of course that Harvard is still an outstanding program, and turning it down is not easy either way.

Chicago over Berkeley was a hard and excruciating decision, but I reasoned that there were only a handful of Berkeley professors I truly wanted to work with all of which are absolute superstars and who have millions of students and postdocs...Chicago was an equally good match for my interests, they have TTIC which is at least top 5 for theoretical machine learning, as well as a handful of superstars or rising stars in the Statistics department and the Booth school of business. Also, besides purely academic reasons, between very long winters and very high living costs, I decided I'd prefer the former, haha. But this is a purely personal preference, and I totally understand people who think otherwise.

You should also consider Chicago and should come at the visit! We do have the notorious quals, as well as the coursework which take up all of the first year plus summer. This is not for everyone. Research-wise, I am still recovering from that, yet I do have two exciting lines of research going on...I guess I just need a few more months to be able to tell you exactly how it's gonna turn out.

Stat Assistant Professor

Yeah, Harvard is really, really strong in the areas of causal inference and MCMC. For deep/maching learning and probability theory, I would say that Columbia, UC Berkeley, and UPenn Wharton have an edge over Harvard (e.g. you've got David Blei at Columbia, Michael Jordan and Martin Wainwright at Berkeley, Edgar Dobriban and Weijie Su at UPenn, etc.). There is also a large group of probability theory researchers in the Statistics Department at UCB, which is somewhat unusual nowadays (typically there is only one or two faculty in a Stats department working on pure probability theory topics).

How sure are you of those research interests and how passionate about them are you? Some people can be truly fulfilled by their research and if that'll make you happy, go to Berkeley. But you're not even going to be able to do good research if you're unhappy and wishing you were on the other side of the country. Are you sure that you are that much interested in probability than say, MCMC, where you could work with Xiao Li Meng at Harvard who to me is one of the most interesting people in statistics - just read some of his paper titles and listen to his talks. Are you sure that theoretical machine learning at Berkeley is that much more interesting to you than the reinforcement learning that Susan Murphy is doing? There's plenty of theoretical stuff going on at Harvard that might satisfy you intellectually, and I definitely think that location is extremely important. The facts are that you will be qualified for top stats jobs after working with someone good at Harvard. Maybe Berkeley will offer you a slightly better chance at doing the type of ML that gets a FB research job, but is that extra slight chance worth 5 years?

My recommendation would be to download some papers from profs you like at both school. Read the papers from Berkeley and ask yourself if you love reading about that subject so much that you would move across the country to Berkeley to be able to ask the person who wrote it a couple questions every week.

insert_name_here , Euler17 and Ryuk

DanielWarlock

I'm a current student at Harvard and was also admitted to Berkeley last year. I know exactly what you are talking about regarding "hot areas". Our department has been recruiting "superstars" in the "hot areas" such as machine learning, high-dim, network, deep learning, non-convex optimization etc. There is one more assistant prof coming in next year working in one of these areas. I think Harvard is becoming less Bayesian/MCMC with the new recruits. A thing to note is that most people in our department working in those areas are "younger generation" who are generally students of well-established people found at, say, Stanford.

That said, a factor you should not ignore is access to MIT. It's possible to find advisors at MIT who are working in the "hot areas", Rakhlin, Poggio, Rigollet, Moitra, Mossell, Jaakkola, to name a few. Similarly, you can find some very famous people in random matrix, statistical physics (e.g. Yau) in harvard math department as well, who actually supervises student doing statistics type of work. In short, I don't think you are losing much academically by choosing Harvard over Berkeley.

I don't think Berkeley's job prospect is any dimmer than Harvard (if not brighter). The only exception is if you are going to a foreign country working in something unrelated to statistics/CS (e.g. banking), then Berkeley probably sounds less impressive. Some HR in my country even thinks of Berkeley as some sort of cash-cow spin-off of "University of California". So you could even get questioned on this.

Is MIT access really feasible as a harvard stats student? If so then yup, huge point taken.

@DanielWarlock Thanks for the input! My main concern is it seems that Berkeley has a strong reputation in ML and a close relationship with the CS department, while attending Harvard would require me to take a bit of a gamble. If I want to do anything with deep learning or theoretical machine learning, I will have to reach out to the CS department, which doesn't seem to have a close relationship with the stats department. I will also be counting on some of the new hires to be good research fits. I am definitely attracted to Harvard, but the reasons I just listed make me nervous about research there. To what extent do you agree with my analysis?

@MathStat I spoke with a few current students who said that there is one Harvard PhD student with a co-advisor at MIT, but he also got his masters from MIT. I'm not sure how feasible it is for the average student.

Some professors at Berkeley also have ties to MIT through FODSI which has been very active this year. I imagine these ties will get stronger over time with FODSI postdocs at Berkeley. This tie is probably not as strong as Harvard, but it's worth noting that it is there.

10 hours ago, Ryuk said: @DanielWarlock Thanks for the input! My main concern is it seems that Berkeley has a strong reputation in ML and a close relationship with the CS department, while attending Harvard would require me to take a bit of a gamble. If I want to do anything with deep learning or theoretical machine learning, I will have to reach out to the CS department, which doesn't seem to have a close relationship with the stats department. I will also be counting on some of the new hires to be good research fits. I am definitely attracted to Harvard, but the reasons I just listed make me nervous about research there. To what extent do you agree with my analysis? @MathStat I spoke with a few current students who said that there is one Harvard PhD student with a co-advisor at MIT, but he also got his masters from MIT. I'm not sure how feasible it is for the average student.

I can't say any professor (MIT or Harvard) will agree to take you as student. Same thing with Prof Jordan or Wainwright at Berkeley. I talked to one of their students on admit day who told me they are extremely busy. But it is not as hard as you think to approach CS or MIT professor at Harvard. Why is there no close relationship to stats department? Jansan, Murphy, Ba have cross appointment. Yue Lu held a reading group last semester with two stats faculties last semester. I found a paid research with CS prof last summer on graphical neural networks without knowing much about the subject (although I ended up not taking it due to COVID). I have not yet approached MIT professors to ask them to advise me on an official capacity but I think it is definitely within reach especially if you know their stuff. It is very easy to initiate a conversation and get to know people on personal level. For example, Moitra held a grad seminar (joint with Boaz from harvard) this semester which essentially requires you to talk to him about research on deep nn. I'm taking a class with Rakhlin and Rigollet on high-dim stats and can go to talk to these guys on office hour every week. I feel that most people are very approachable in the first place. The issue is how well you can convince these professors to advise you on official capacity. There is no guarantee with this kind of thing even if you go to Berkeley.

2 weeks later...

I would say Berkeley is far way superior than Harvard, this would be a no brainer to me unless you have strong location preferences.

statsnow and Ryuk

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How much of machine learning is computer science vs. statistics.

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How much of machine learning is computer science vs. statistics? originally appeared on Quora : the knowledge sharing network where compelling questions are answered by people with unique insights .

Answer by Michael Hochster , PhD in Statistics from Stanford; Director of Research at Pandora, on Quora :

I don't think it makes sense to partition machine learning into computer science and statistics. Computer scientists invented the name machine learning, and it's part of computer science, so in that sense it's 100% computer science. But the content of machine learning is making predictions from data. People in other fields, including statisticians, do that too. It is more that computer scientists and statisticians view "making predictions from data" through different lenses. Here are some stereotypes, which I am adding as a header so I don't have to say "tend to" and "mostly" everywhere.

Computer scientists view machine learning as "algorithms for making good predictions." Unlike statisticians, computer scientists are interested in the efficiency of the algorithms and often blur the distinction between the model and how the model is fit. Computer scientists are not too interested in how we got the data or in models as representations of some underlying truth. For them, machine learning is black boxes making predictions. And computer science has for the most part dominated statistics when it comes to making good predictions.

Statisticians are concerned with abstract probability models and don't like to think about how they are fit (ummm, is it iteratively reweighted least squares?). Statisticians pay more attention to interpreting models (e.g. looking at coefficients) and attach meaning to statistical tests about the model structure. Computer scientists might reasonably ask if statisticians understand things so well, why are their predictions so bad? But I digress. Unlike computer scientists, statisticians understand that it matters how data is collected, that samples can be biased, that rows of data need not be independent, that measurements can be censored or truncated. These issues, which are sometimes very important, can be addressed with the probability-model approach statisticians favor.

Computer scientists and statisticians both ignore questions of causality when they build models. Right now causation doesn't play much of a role in "machine learning," even though it obviously matters for making predictions. Economists are better about acknowledging this. Maybe someday there will be a future version of this question that will mention causal modeling as a third aspect of machine learning.

This question originally appeared on Quora. Ask a question, get a great answer. Learn from experts and access insider knowledge. You can follow Quora on Twitter , Facebook , and Google+ . More questions:

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Machine Learning : What skills are needed for machine learning jobs?
Computer Science Education : What are some famous bugs in the computer science world?

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$phd statistics quora$

Objective Of The Program

The Department offers PhD programs in Mathematics and Statistics with the objective of preparing motivated researchers, who can work in frontline areas as well as to prepare well-trained teaching faculty.

The vibrant academic environment of the department is nurtured by strongly motivated faculty. The Department faculty takes research initiatives to work in recent and emerging areas. The department encourages interdisciplinary trends. Around 290 research scholars have graduated from the department and are serving in the faculty of various institutes and universities in India and abroad. Some of them have gone for industry also.

Structure Of The Programme

The department offers separate PhD Programmes in Mathematics and Statistics. Following are the requirements for the degrees.

Course work:

During the first two semesters, students are required to complete the minimum credit requirements (24) from course work. The minimum number of courses to be done is 6, out of which 3 are compulsory: Algebra, Mathematical Methods and Analysis for Ph.D. Mathematics, and Analysis, Probability Theory and Statistical Inference for Ph.D. Statistics. Minimum research credit requirement for the programme is 80 and students are required to register for 16 research credit per semester after finishing the course-work.

Comprehensive examination:

Students registered in the Ph.D. programme must pass a Comprehensive examination designed to test the overall comprehension of the student in Mathematics/Statistics. A student can appear in the comprehensive examination only after he/she has completed the course requirements and satisfied the minimum specified CPI requirement, i.e., 7.0 (out of 10.0).The comprehensive examination board consists of at least three but not more than five faculty members of the department and one faculty member from outside the department.

State of Art Seminar:

A student enrolled in the PhD programme is formally admitted to the candidacy for the Ph.D. degree after he/she has completed the course requirements for the degree with a CPI of at least 7.0 and has passed the comprehensive examination. Every Ph.D. student admitted to the candidacy for the Ph.D. degree is required to deliver a general seminar covering the State of Art of the area of research. This seminar, viz., the State of Art Seminar must be delivered within six months of passing the comprehensive examination.

Open Seminar:

Before finalizing the thesis, every Ph.D. student must deliver an Open Seminar in which the research work will be presented to obtain comments and criticism, which are to be incorporated in the thesis. The maximum time duration for the submission of the thesis after the delivery of the open seminar is six months.

The PhD Thesis Board consists of three members in addition to the thesis supervisor(s) approved by the Chairman, Senate. At least two members of the board (other than the supervisor(s)) must be from outside the Institute and at least one of these two must be from within the country. The thesis supervisor(s), in consultation with the Head, proposes a list of examiners consisting of at least three extra names over and above the required number of members for the thesis board. The Chairman, Senate in consultation with Chairperson, SPGC selects the members of the thesis board from this list. The names of members of the thesis board are kept confidential till successful completion of the oral examination.

DEPARTMENT OF Mathematics & Statistics

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Kanpur, UP 208016 | Phone: 0512-259-xxxx | Fax: 0512-259-xxxx

Suo-Moto Disclosure

Ph.D. in Statistics

Our doctoral program in statistics gives future researchers preparation to teach and lead in academic and industry careers.

Program Description

Degree type.

approximately 5 years

The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible research electives. Graduates of our program are prepared to be leaders in statistics and machine learning in both academia and industry.

The Ph.D. in Statistics is expected to take approximately five years to complete, and students participate as full-time graduate students. Some students are able to finish the program in four years, but all admitted students are guaranteed five years of financial support.

Within our program, students learn from global leaders in statistics and data sciences and have:

20 credits of required courses in statistical theory and methods, computation, and applications

18 credits of research electives working with two or more faculty members, elective coursework (optional), and a guided reading course

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

SDS 384.2 Mathematical Statistics I
SDS 383C Statistical Modeling I
SDS 387 Linear Models
SDS 384.11 Theoretical Statistics
SDS 383D Statistical Modeling II
SDS 386D Monte Carlo Methods

In addition to the core courses, students of the first year are expected to participate in SDS 190 Readings in Statistics. This class focuses on learning how to read scientific papers and how to grasp the main ideas, as well as on practicing presentations and getting familiar with important statistics literature.

At the end of the first year, students are expected to take a written preliminary exam. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should continue in the Ph.D. program. The exam covers the core material covered in the core courses and it consists of two parts: a 3-hour closed book in-class portion and a take-home applied statistics component. The in-class portion is scheduled at the end of the Spring Semester after final exams (usually late May). The take-home problem is distributed at the end of the in-class exam, with a due-time 24 hours later.

Year 2: Transitioning from Student to Researcher

In the second year of the program, students take the following courses totaling 9 credit hours each semester:

Required: SDS 190 Readings in Statistics (1 credit hour)
Required: SDS 389/489 Research Elective* (3 or 4 credit hours) in which the student engages in independent research under the guidance of a member of the Statistics Graduate Studies Committee
One or more elective courses selected from approved electives ; and/or
One or more sections of SDS 289/389/489 Research Elective* (2 to 4 credit hours) in which the student engages in independent research with a member(s) of the Statistics Graduate Studies Committee OR guided readings/self-study in an area of statistics or machine learning.
Internship course (0 or 1 credit hour; for international students to obtain Curricular Practical Training; contact Graduate Coordinator for appropriate course options)
GRS 097 Teaching Assistant Fundamentals or NSC 088L Introduction to Evidence-Based Teaching (0 credit hours; for TA and AI preparation)

* Research electives allow students to explore different advising possibilities by working for a semester with a particular professor. These projects can also serve as the beginning of a dissertation research path. No more than six credit hours of research electives can be taken with a single faculty member in a semester.

Year 3: Advance to Candidacy

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. At the end of the second year or during their third year, students are expected to present their plan of study for the dissertation in an Oral candidacy exam. During this exam, students should demonstrate their research proficiency to their Ph.D. committee members. Students who successfully complete the candidacy exam can apply for admission to candidacy for the Ph.D. once they have completed their required coursework and satisfied departmental requirements. The steps to advance to candidacy are:

Discuss potential candidacy exam topics with advisor
Propose Ph.D. committee: the proposed committee must follow the Graduate School and departmental regulations on committee membership for what will become the Ph.D. Dissertation Committee
Application for candidacy

Year 4+: Dissertation Completion and Defense

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. Moreover, they are expected to present part of their work in the framework of the department's Ph.D. poster session.

Students who are admitted to candidacy will be expected to complete and defend their Ph.D. thesis before their Ph.D. committee to be awarded the degree. The final examination, which is oral, is administered only after all coursework, research and dissertation requirements have been fulfilled. It is expected that students will be prepared to defend by the end of their fifth year in the doctoral program.

General Information and Expectations for All Ph.D. students

2023-24 Student Handbook
Annual Review At the end of every spring semester, students in their second year and beyond are expected to fill out an annual review form distributed by the Graduate Program Administrator.
Seminar Series All students are expected to attend the SDS Seminar Series
SDS 189R Course Description (when taken for internship)
Internship Course Registration form
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Attending Conferences

Students are encouraged to attend conferences to share their work. All research-related travel while in student status require prior authorization.

Request for Travel Authorization (both domestic and international travel)
Request for Authorization for International Travel

Doing a PhD in Statistics

We live in a data-rich world. The study of statistics allows us to better understand data, measure uncertainty, and calculate risk. The applications of such knowledge are widespread – from economics to medicine. A PhD in Statistics will give you a deep understanding of the mathematical framework which underpins data analysis as we know it. Read on to find out the key information about a PhD in statistics, and whether it is worth it for you.

What Does a PhD in Statistics Focus On?

A Statistics PhD programme can focus on:

Statistical theory and statistical methods
Bayesian statistics
Covariance modelling
High dimensional data
Probability theory
Causal inference
Extreme value theory
Non-parametric regression
Symbolic computation
Applied statistics

The list above is only a small sample of the many different areas within probability and statistics. Many PhD research projects place a particular emphasis on statistics within environmental, biomedical, and social science. Aside from this there is also overlap with other field such as computer science, applied mathematics, and linear algebra.

Browse PhDs in Statistics

Application of artificial intelligence to multiphysics problems in materials design, study of the human-vehicle interactions by a high-end dynamic driving simulator, physical layer algorithm design in 6g non-terrestrial communications, machine learning for autonomous robot exploration, detecting subtle but clinically significant cognitive change in an ageing population, entry requirements for a phd in statistics.

Most Statistics PhD programmes require applicants to have, or expect to obtain, a bachelor’s degree (or international equivalent) in Mathematics or Statistics. However, many Statistics PhD research projects also accept applications from graduate students with a bachelor’s degree in other subjects if they involve a significant mathematical component (such as Data Science , Physics, or Computer Science). Many universities expect first class honours due to the high competition for places, though for some institutions second class honours (2:1) is adequate.

It is also common for universities to accept second class honours (2:1), if the graduate has a master’s degree or relevant work experience.

Universities typically expect international students to provide evidence of their English Language ability. This is usually benchmarked by a IELTS score of 6.5 (with a minimum score of 6 in each component), a TOEFL (iBT) score 92, a CAE and CPE score of 176 or another equivalent. The exact score requirements may differ across different universities.

Duration and Programme Types

The typical doctoral programme in Statistics takes 3-4 years full-time, or 6 years part-time.

A PhD research project in Statistics can focus on a particular application of statistics. For example, you may undertake a PhD in statistical genomics or biostatistics, which would involve interdisciplinary work and additional training modules to understand how statistics can improve biological and genetic study.

In addition to the statistics course modules, you will likely undertake ‘ transferable skills ‘ training in communication, management, and commerce – all of which are skills a good postgraduate research student needs.

As with most PhDs, you will have to complete a dissertation at the end of your postgraduate research project, and undertake an oral examination known as the viva , where you are required to defend your dissertation to a supervisory committee/dissertation committee usually made up of two examiners.

Costs and Funding

Annual tuition fees for PhDs in Statistics are typically around £4,000 to £5,000 for UK/EU students. Tuition fees for international students are usually much higher, typically around £20,000 – £25,000 per academic year. Tuition fees for part time programmes are typically scaled down according to the programme length.

Some Statistics PhD programmes also have additional costs to cover laboratory resources, administration and computational costs.

Together with EPSRC and other national funding sources, many Universities offer postgraduate studentships which cover the tuition fees for Statistical PhD programmes. EPSRC DTA research studentships are available in all areas for UK and EU students. Students who are normally resident in the EU but not in the UK are eligible for EPSRC PhD studentships, but the awards in such cases currently cover only the course fees, not maintenance stipends .

Available Career Paths in Statistics

One of the key advantages of Statistics is that it is a fundamental concept which underpins most industries. Consequently, there are an abundance of career paths available for Statistics PhD doctorates such as agriculture, forensics, machine learning, informatics, geosciences, law and biomathematics.

Examples of common destinations for a Statistics PhD student include:

Actuarial Science – Actuaries are responsible for analysing data to help non-specialists make informed decisions about risks. A good understanding of probability and investment is crucial in this field. Salaries for Statistics PhD students in this field vary, but with around 10 years’ experience typically are around £60,000.
Environmental statistician – In this role, Statistics doctorates use their knowledge to contribute to environmental study. This can include monitoring climate patterns, carrying out flood risk assessments, or transforming large amounts of temperature data into information for the public.
Data Analyst – Some people use their PhD in stats to become data analysts, responsible for data management, developing automated processes, tracking KPIs, and more. Data analysts can be found in various industries form logistics & transport to marketing. Again, with experience Statistics doctorates in this path can expect a lucrative salary.
Medical statistician – PhD graduates in the medical field aid health research in a number of ways, for example analysing data from clinical studies to identify patterns. The NHS, private health companies and the pharmaceutical industry are common employers for those with a PhD degree in statistics or applied statistics.
University lecturer – Often PhD students opt to stay in academia. This can be as a university lecturer where you will teach students about statistical theory.

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Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
Our students receive job offers from top academic and non-academic institutions .
Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
GRE requirement: Waived for Fall 2024.
Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
Deadline: Jan 8, 2024 .
Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
Timeline: P.hD students begin the program in September only. Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
What are the required application materials? Specific admission requirements for our programs can be found here .
Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact gsas-admissions@ columbia.edu .
How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle.
Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
What is the minimum TOEFL or IELTS requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact gsas-admissions@ columbia.edu .
My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously? For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences. Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

This option is only available for fall-term applicants.
Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University 107 Low Library, MC 4303 535 West 116th Street New York, NY 10027
How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
How do I find out which professors are taking on new students to mentor this year? Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at [email protected]

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PhD in Statistics: Course Details, Eligibility, Admission, Fees

PhD in Statistics is a three to six years long doctorate program that deals with the collection, analysis, interpretation, and presentation of numerical data. This course graduate can get many job roles such as Research Analyst, Assistant Professor, Data Analyst, Biostatistician, Data Interpreter, Lecturer, Research Scholar etc.

PhD Statistics Course Details

Degree	Doctorate
Full Form	Doctor of Philosophy in Statistics
Duration	2 Years 11 Months
Age	No age limit
Subjects Required	Masters in Statistics or relevant subject
Minimum Percentage	55%
Average Fees	₹5 - 2 LPA
Average Salary	INR 5 - 10 LPA
Employment Roles	Econometrician, Article Writer, Enumerator, Lecturer, Assistant Professor, Biostatistician, Data Analyst, Research Analyst, Data Interpreter, Research Scholar, Statistician, Content Developer
Top Recruiters	Indian Civil Service, Indian Administration, Census Board

About PhD in Statistics

PhD in Statistics is a three to six years doctorate which is created in such a way that the learners get to know a deep understanding of their chosen topics. Over the past few years, the scope of pursuing PhD courses in India has increased manifolds. Many universities are in front of admission in this PhD course .

According to Wikipedia "Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional, to begin with, a statistical population or a statistical model to be studied."

TABLE OF CONTENTS

PhD in Statistics Eligibility Criteria
PhD in Statistics Admission Process
Who Should Do a PhD in Statistics

Types of PhD in Statistics

Popular PhD in Statistics Entrance Exams

Study PhD in Statistics in India

Top PhD in Statistics Colleges
Study PhD in Statistics in Abroad
PhD in Statistics Fees Structure
PhD in Statistics Syllabus and Subjects

Why Choose PhD in Statistics?

Phd in statistics course comparison.

Scope of Higher Education for PhD in Statistics

Preparation Tips for PhD in Statistics

Salary of a PhD in Statistics Graduate
Career Options after PhD in Statistics
Skills to Excel

Eligibility Criteria for PhD in Statistics

As all the students know how important a PhD in Statistics course is, that’s why it’s quite difficult to get admitted into their respective interested universities. Those students who want to take enrollment in this doctorate program have to qualify for the eligibility criteria i.e one should have a minimum mark of 55% in either MSc or MA in Mathematics or Statistics.

There are few PhD Agricultural entrance exams such as GATE, CSIR- UGC NET, UGC-NET, and BHU which have cutoffs to qualify. There is no age limit for the students for taking admission to this course.

How To Get Admission for PhD in Statistics?

Admission to the PhD in Statistics course is not impossible if the students ensure that they research the details well. The universities follow the process of entrance-based admissions. The admission process with respect to admission through exams varies from college to college. Online and offline both facilities are available to the students for enrollment. The students are guided to check the college website to get detailed information

How to Apply?

There are two ways to apply for admission in the PhD Statistics course. Either online by visiting the college website and following the steps given there or offline by visiting the university's office.

There are certain colleges that take interviews for checking the knowledge and communication skills of the candidates.

Selection Process

The selection process of the candidates for getting admission in the respective PhD in Statistics course ends on interview. The students who have cracked the entrance exams with good marks can give interviews for the further joining process. The last step of the admission procedure decides whether the student is eligible to take admission or not.

Who Should Pursue a PhD in Statistics

A PhD in Statistics is a three to six years doctorate program that makes a student’s career in the public, private, academic, and non-profit sectors. When it comes to your career graph. A PhD in Statistics degree gives a variety of job opportunities to the graduates.

One of the most sought-after jobs for a PhD Statistics degree holder is of a lecturer or a professor in a university or a researcher in most fields.

Along with this, a PhD in Statistics degree holder can get a job in Ecological, Medical, Census, Election, Crime, Education, Film, Cricket, Tourism, Insurance, Statistical Research, Indian Statistical Services, etc. Candidates will have in-depth knowledge and develop mastery over the subjects they have chosen for specialization, which will be extremely useful for them in their careers. Those learners who are interested in these specializations should pursue this course.

When to do a PhD in Statistics?

Aspirants are eligible to pursue a PhD in Statistics course only if they have completed their master's degree in a similar course/ field/ stream in which they want to pursue a PhD in Statistics. Some colleges also specify that candidates need to have completed an MPhil to pursue a PhD in Statistics course offered by them.

A PhD in Statistics is a research-based doctorate program and that's the reason this course can be done in any of three modes i.e full-time, part-time, and distance learning mode. Aspirants can pursue the PhD in Statistics course at many top universities.

Full-Time PhD in Statistics

A full-time PhD Statistics program duration is three to six years that is provided by many top respected colleges or universities. This full-time course is all about classroom-based studying, research projects, and assignments. There are no limitations on the age for taking admissions.

Part-Time PhD in Statistics

A part-time PhD Agricultural program is for 6-10 years. This course is basically created for the students who are working somewhere or are professionals. A part-time mode is all about having limited classes. Part-time PhD is mainly focused on research for the development of the company rather than individual research work.

Distance PhD in Statistics

A distance PhD in Statistics in India program is quite easy for getting knowledge at any time from anywhere. Many people believe in pursuing this course because of no issue of being physically present at the universities. The objective behind the creation of this distance learning program is to provide the degree along with the knowledge while working.

Popular Entrance Exams for PhD in Statistics

Various institutes and testing agencies conduct PhD in Statistics entrance exams for admissions regarding PhD in Statistics and Technology. The following are some of these entrance examinations:

CSIR- UGC NET

A Quick Glance at the PhD in Statistics Entrance Exams

Students can access the PhD in Statistics course details by going to the college's official website to which they are interested in applying. The specialization plays a vital role in the college's approach to the entrance exam. Below listed are some of the general guidelines of the PhD in Statistics entrance examinations:

The exam pattern includes common topics from 10+2, graduation and postgraduation level i.e. technology, life sciences, mathematics, sciences, and general aptitude.
The papers are objective and MCQ-based.
The syllabus, mode of examination, and question pattern may change according to a university/conducting body.

India is home to some of the PhD in Statistics colleges in the world. Aspirants have many options to choose from in terms of the best PhD in Statistics course as per their preferences. Depending on the type of PhD Statistics programs offered, candidates will have to make the appropriate choice. Below are some of the top colleges in India offering PhD in Statistics courses:

Top 10 PhD in Statistics Colleges in India

Below is the list of the top best PhD in Statistics colleges in the country:

PhD Statistics Colleges
SL. NO.	NAME OF THE COLLEGES
1
2
3
4
5
6
7
8
9

Top PhD in Statistics Colleges in New Delhi

Delhi, the educational hub of India, stands in the fourth position in producing PhD in Statistics candidates. Here are the top 5 PhD in Statistics Colleges in New Delhi:

SL. NO.	Institution
1
2
3
4
5

Top PhD in Statistics Colleges in Chennai

The top 5 PhD in Statistics Colleges in Chennai are given below:

SL.NO.	Institution
1
2
3
4
5

Top PhD in Statistics Colleges in Pune

Below is the list of colleges which are the top colleges for PhD in Statistics in Pune:

SL.NO.	Institution
1
2
3
4
5	NIMS University

Top PhD in Statistics Colleges in Bangalore

Banglore city has some of the best colleges for PhD in Statistics courses in India. Here is the list of top colleges for PhD in Statistics in Bangalore:

Top PhD in Statistics Colleges in Kolkata

Check the Table below for the top colleges in Kolkata:

SL.NO.	Institution
1
2
3
4
5	LBS

Top PhD in Statistics Colleges in Hyderabad

The Telangana state capital has some premier institutions in the country for PhD in Statistics courses in India. Check the table below for the top PhD Statistics colleges in Hyderabad:

SL.NO.	Institutions
1
2
3
4
5

Top PhD in Statistics Government Colleges

There are several top Government Colleges offering quality PhD in Statistics programmes across the country. Check the table below for the top PhD in Statistics government colleges in India:

PhD Statistics Government Colleges
Sl.No	Institution
1
2
3
4
5

Top PhD in Statistics Private Colleges

India has seen significant growth in the number of quality PhD in Statistics private colleges that offer some of the best programmes in the country. Check the table below for the top PhD in Statistics private colleges in India:

PhD Statistics Private Colleges
SL.NO.	Institutions
1
2
3
4
5

Study PhD in Statistics Abroad

Students can opt to study a PhD in Statistics course abroad if they can afford it. The PhD Statistics course abroad is up to 5-8 years, depending on the type of course, college and country.

The benefits of studying a PhD in Statistics course abroad are access to some of the best resources, facilities, and faculties, apart from worldwide exposure in terms of subject matter and other cultures.

Top PhD in Statistics Colleges Abroad

The table below contains the list of some of the best colleges abroad for PhD in Statistics:

PhD Statistics Colleges Abroad
Institution	Fees
	USD 17,787
	USD 34,747
	USD 64,347
	AUD 16,779
	USD 45,700
	USD 66,096

Top PhD in Statistics Colleges in the USA

The USA is home to some of the best universities and colleges offering top-notch PhD in Statistics programmes in the world. The US is the best country for PhD in Statistics studies and settling abroad. It's a PhD Statistics-level that the USA really shines through. The table below contains the list of top colleges of PhD in Statistics in the USA:

Top PhD in Statistics Colleges in the Uk

A PhD in Statistics is a research degree and is the highest award available at universities in the UK. The study is based on a substantial research project on an area of academic interest, typically up to 100,000 words in length, written as a thesis which then must be defended in an oral examination in front of a panel of experts.

The table below contains the list of top colleges of PhD in Statistics in the Uk:

Top PhD in Statistics Colleges in Canada

An increasingly attractive and multicultural study destination, Canada is a great option to consider for your PhD Statistics studies, offering a wealth of research opportunities to help you expand your expertise. Here are the top universities for PhD in Statistics in Canada:

Top PhD in Statistics Colleges in Australia

A PhD in Statistics in Australia means that you will develop your knowledge and skills, which ultimately increases your chances for employment within Australia and in any country. According to the UN's Education Index, Australia's education system ranks first. The table below shows the top universities of PhD in Statistics in Australia:

Top PhD in Statistics Colleges in Germany

It is much easier to get a PhD in Statistics in a European university, which takes about 3-4 years in a good university in Europe. The PhD Statistics from Germany in engineering enjoys an outstanding reputation. Germany's research institutions, universities and companies welcome international researchers and offer excellent opportunities for doctoral students.

The table below shows the top universities in Germany colleges of PhD in Statistics:

SL.NO.	Institution
1
2
3
4

Fee Structure for PhD in Statistics

The fee structure for PhD in Statistics varies for different Universities. Also, the fee structure varies following the course and University. Students can download the admission brochure as well as the course curriculum to get the details of PhD Statistics admission and fee structure.

The average PhD in Statistics fee in India is INR 5,000 - 2 LPA. Now, read below to know about the fee structure of different universities.

Fee Strucutre for PhD Statistics in India
Name of the college	Fees Structure
Banaras Hindu University, Uttar Pradesh	INR 22,268 PA
University Of Hyderabad, Hyderabad	INR 11,200PA
Aligarh Muslim University, Aligarh	INR 9,285 LPA
Amity University, Patna	INR 1 LPA
Banasthali Vidyapeeth, Radhakishnpura	INR 1.4 LPA

Syllabus and Subjects for PhD in Statistics

PhD in Statistics is a doctorate course in Agriculture. The course may have a duration of 2 years and it is a full-time course. It is a research-based course. In the PhD in Statistics duration, the candidates get to learn the collection, analysis, interpretation, and presentation of numerical data. This course mainly covers areas like Math and Statistics, etc. The students come to know about the fundamentals of agriculture and crop production. Though the actual course offerings might differ from one university to another, here is a list of major subjects which are commonly studied under a PhD Statistics:

Statistical Methods
Probability Theory
Vectors and Matrices
C or C++ Programming
Numerical Analysis
Elementary Inference
Statistical Quality Control
Mathematical Analysis

Read More: PhD in Statistics Subjects and syllabus

When students decide to pursue the PhD in Statistics qualification, they should research PhD in Statistics course details to ensure that they know the course they are enrolled in. Some of the common queries that students encounter are, "What is PhD Statistics" and "Why PhD Statistics?". To understand the answer to these questions, we can make it simpler by breaking it down into the following three short questions:

What is PhD in Statistics All About?

A PhD in Statistics or Doctor of Philosophy is a doctoral research degree and is normally the highest level of academic qualification one can achieve. A PhD in Statistics degree holder can get a job as Research Analyst, Assistant Professor, Data Analyst, Biostatistician, Data Interpreter, Lecturer, Research Scholar, and many more.

The course has a specialization that deals with Statistical Methods and Mathematical Analysis. Students need to check the PhD in Statistics course outline perfectly.

What do PhD in Statistics Graduates do?

Students can select the subjects of their choice depending on their interest in job prospects available in the specific field. Since graduates with a PhD Statistics degree possess skills in various subjects, there are career opportunities available in multiple fields for them.

In addition, there are many responsibilities that graduates of this role have to undertake, which makes the role very dynamic and diverse.

Data Analyst: One of the popular roles undertaken by graduates of the PhD in Statistics course is Data Analyst. Although responsibilities might vary based on position and industry, data analysts are often in charge of gathering data, organizing it, and pinpointing trends and patterns. In the later stages of a project, data analysts produce visualizations and reports and communicate their findings to leadership

Reasons Why PhD in Statistics Can Fetch You a Rewarding Career?

PhD in Statistics courses are a very reflective and exciting stream of education. Students can gauge the intrinsic worth of a PhD Statistics course because it offers more avenues of employment opportunities than any other stream. Thus, the PhD in Statistics job scope is forever widening and appealing.

Diversity in Job roles : There is a diverse range of job roles available for the graduates of this course. Since the specializations available to the students are very flexible, it enables the students to pursue a wide range of roles in their careers.

Read More: PhD in Statistics Jobs & Scope

PhD in Statistics stands for Doctor of Philosophy in Statistics and is typically catered to students interested in and inclined towards research-based degrees and professional application. Here is a course comparison of PhD Statistics with another course:

PhD Statistics vs PhD Agronomy

PhD Statistics vs Agronomy: Course Comparison
	PhD Statistics	PhD Agronomy
	Doctor of Philosophy in Statistics	Doctor of Philosophy in Agronomy
	Statistics and Mathematics	Agronomy
	2 Years	3-6 Years
	Masters in Science or relevant course with a minimum of 55% marks	Masters in Agronomy or relevant course with a minimum of 50% marks
	GATE, CSIR- UGC NET, UGC-NET, BHU	CSIR UGC NET, UGC NET, AAU VET, OUAT
	Amity University, Annamalai University, Christ University, Aligarh University	Bihar Agricultural University, Bhagalpur, College of Postgraduate Studies, Shillong, Rajasthan College of Agriculture, Udaipur.
	INR 10,000- 1.5 LPA	INR 1- 6 LPA

There are many tips that students must note when deciding to pursue a PhD in Statistics degree. Some important preparation tips are listed below to ensure that the students crack the course and pass the exams without any hurdles.

Improve Vocabulary: Improving vocabulary is very necessary as the PhD in Statistics course deals with many writing and reading activities. So having excellent communication and writing skills are a plus for the student studying the PhD in Statistics course.

Read and Practice More: Being up to date with the syllabus every day is very important. Practising and reading more will help the student be thorough with the syllabus and do well in the exams.

Have Intrinsic Knowledge and Interest in Subjects: Having intrinsic knowledge about the subject and having the same interest will keep the student motivated to learn more than what's in the syllabus.

Revise Methodically: Revising from time to time can be a key to scoring well in the final exam. Keep revising regularly and understand the subject properly. Revision is the key to scoring good marks.

Salary of a PhD in Statistics Graduates

The salary depends on the kind of job you get/you choose to do, what your PhD in Statistics research area is and where you are employed.No prior experience is required to be a PhD in Statistics Student. The average Salary of a PhD in Statistics graduate starts from INR 3 - INR 8 LPA (Source: Payscale), depending on the stream you belong from.

Career Options After PhD in Statistics

A PhD in Statistics degree is pursued by a majority of students because it offers better career options, for example, in the field of academics and research. PhD in Statistics in itself is a broad term, it has many specializations that have different career options and jobs. PhD Statistics Jobs include:

Senior Data Science Researcher
Life science Teaching Faculty
Quantitative Research Scientist
Associate Research Scientist, Human Biology
Dolat Capital - Quantitative Research Scientist
Research Scientist

Skills That Make You The Best PhD in Statistics Graduates

For studying a course like PhD in Statistics, there are so many skills that a student needs to have. The skills help the students to get well-maintained dignity, respect, and so much attention.

So, that’s why the students need to put their efforts to come out of their comfort zones and earn respect as much as possible. Some of these skills include:

Academic Ability
Accountability
Persistence
Good Communication Skills
Time Management
Open-mindedness and Curiosity to Learn Something New
Ability to Think

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CIS Graduate Program Admissions Statistics

Statistics by program:.

Doctoral Program

Fall 2023 PhD admission statistics

1,508 applicants to the doctoral program
92 candidates admitted
38 students matriculated
Candidates admitted to the doctoral program: Average GPA: 3.7

Fall 2022 PhD admission statistics

1,031 applicants to the doctoral program
82 candidates admitted
32 students matriculated
Candidates admitted to the doctoral program: Average GPA: 3.78

Fall 2021 PhD admission statistics

1,141 applicants to the doctoral program
79 candidates admitted
45 students matriculated
Candidates admitted to the doctoral program: Average GPA: 3.8

Fall 2020 PhD admission statistics

924 applicants to the doctoral program
98 candidates admitted
29 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 167/Q 161/AW 4

Fall 2019 PhD admission statistics

826 applicants to the doctoral program
86 candidates admitted
44 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 160/Q 167/AW 4

Fall 2018 PhD admission statistics

756 applicants to the doctoral program
75 candidates admitted
26 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 162/Q 167/AW 4.5

Fall 2017 PhD admission statistics

522 applicants to the doctoral program
35 students matriculated

Fall 2016 PhD admission statistics

424 applicants to the doctoral program
91 candidates admitted
25 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 157/Q 167/AW 4

Fall 2015 PhD admission statistics

471 applicants to the doctoral program
51 candidates admitted
16 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 162/Q 166/AW 4.5

Fall 2014 PhD admission statistics

448 applicants to the doctoral program
50 candidates admitted
14 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 163/Q 167/AW 4.5

Fall 2013 PhD admission statistics

501 applicants to the doctoral program
49 candidates admitted
15 students matriculated
Candidates admitted to the doctoral program: Average GRE V 161/Q 166/AW 4

Fall 2012 PhD admission statistics

545 applicants to the doctoral program
60 candidates admitted
Candidates admitted to the doctoral program: Average GRE V 610:160/Q 790:164/AW 4.5

Fall 2011 PhD admission statistics

526 applicants to the doctoral program
65 candidates admitted
22 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 610/Q 790/AW 4.5

Fall 2010 PhD admission statistics

465 applicants to the doctoral program
Candidates admitted to the doctoral program: Average GRE: V 580/Q 790/AW 4.5

Fall 2009 PhD admission statistics

478 applicants to the doctoral program
48 candidates admitted
20 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 620/Q 780/AW 4.5

Fall 2008 PhD admission statistics

470 applicants to the doctoral program
58 candidates admitted
Candidates admitted to the doctoral program: Average GRE: V 620/Q 790/AW 5.0

Fall 2007 PhD admission statistics

491 applicants to the doctoral program
Candidates admitted to the doctoral program: Average GRE: V 620/Q 780/AW 5.0

Fall 2006 PhD admission statistics

480 applicants to the doctoral program
33 candidates admitted
11 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 610/Q 790/AW 5.0

Fall 2005 PhD admission statistics

515 applicants to the doctoral program
21 candidates admitted
9 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 620/Q 780/A 760/AW 5.0

Fall 2004 PhD admission statistics

683 applicants to the doctoral program
45 candidates admitted
21 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 650/Q 780/A 770/AW 5.0

Fall 2003 PhD admission statistics

873 applicants to the doctoral program

Fall 2002 PhD admission statistics

643 applicants to the doctoral program
57 candidates admitted
27 students matriculated
Candidates admitted to the doctoral program: Average GRE: V 600/Q 780/A 760

Fall 2001 PhD admission statistics

514 applicants to the doctoral program
56 candidates admitted
Candidates admitted to the doctoral program: Average GRE: V 600/Q 780/A 740

Fall 2000 PhD admission statistics

367 applicants to the doctoral program
54 candidates admitted
Candidates admitted to the doctoral program: Average GRE: V 620/Q 780/A 740

CIS/MSE Program

Fall 2022 CIS/MSE admission statistics

1974 applicants to the CIS/MSE program
172 candidates admitted
55 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 160/Q 168/AW 4.3

Fall 2021 CIS/MSE admission statistics

1782 applicants to the CIS/MSE program
204 candidates admitted
63 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 160/Q 168/AW 4.2

Fall 2020 CIS/MSE admission statistics

1538 applicants to the CIS/MSE program
192 candidates admitted
61 students matriculated

Fall 2019 CIS/MSE admission statistics

1299 applicants to the CIS/MSE program
169 candidates admitted
58 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 158/Q 168/AW 4

Fall 2018 CIS/MSE admission statistics

1339 applicants to the CIS/MSE program
138 candidates admitted
46 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 159/Q 168/AW 4

Fall 2017 CIS/MSE admission statistics

1264 applicants to the CIS/MSE program
130 candidates admitted
Candidates admitted to the CIS/MSE program: Average GRE: V 158/Q 167/AW 4

Fall 2016 CIS/MSE admission statistics

1021 applicants to the CIS/MSE program
131 candidates admitted

Fall 2015 CIS/MSE admission statistics

954 applicants to the CIS/MSE program
117 candidates admitted
Candidates admitted to the CIS/MSE program: Average GRE: V 160/Q 167/AW 4

Fall 2014 CIS/MSE admission statistics

752 applicants to the CIS/MSE program
135 candidates admitted
57 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 158/Q 166/AW 4

Fall 2013 CIS/MSE admissions statistics

789 applicants to the CIS/MSE program
104 candidates admitted
30 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 158/Q 166/ AW 4

Fall 2012 CIS/MSE admissions statistics

760 applicants to the CIS/MSE program
133 candidates admitted
Candidates admitted to the CIS/MSE program: Average GRE: V 620:161/Q 790:164/ AW 4

Fall 2011 CIS/MSE admissions statistics

585 applicants to the CIS/MSE program
171 candidates admitted
70 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 590/Q 780/ AW 4

Fall 2010 CIS/MSE admissions statistics

410 applicants to the CIS/ MSE program
152 candidates admitted
50 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 570/Q 780/ AW 4

Fall 2009 CIS/MSE admissions statistics

294 applicants to the CIS/MSE program
71 candidates admitted

Fall 2008 CIS/MSE admissions statistics

303 applicants to the CIS/MSE program
43 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 580/Q 780/ AW 4

Fall 2007 CIS/MSE admissions statistics

302 applicants to the CIS/MSE program
Candidates admitted to the CIS/MSE program: Average GRE: V 550/Q 780/ AW 4

Fall 2006 CIS/MSE admissions statistics

214 applicants to the CIS/MSE program
99 candidates admitted
34 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 570/Q 780/ AW 4.5

Fall 2005 CIS/MSE admissions statistics

197 applicants to the CIS/MSE program
112 candidates admitted
Candidates admitted to the CIS/MSE program: Average GRE: V 540/Q 770/A 760/ AW 4.5

Fall 2004 CIS/MSE admissions statistics

204 applicants to the CIS/MSE program
110 candidates admitted
Candidates admitted to the CIS/MSE program: Average GRE: V 560/Q 770/A 720/ AW 5.0

Fall 2003 CIS/MSE admissions statistics

233 applicants to the CIS/MSE program
144 candidates admitted
51 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 570/Q 770/A 710/ AW 5.0

Fall 2002 CIS/MSE admissions statistics

210 applicants to the CIS/MSE program
88 candidates admitted
Candidates admitted to the CIS/MSE program: Average GRE: V 570/Q 770/A 720

Fall 2001 CIS/MSE admissions statistics

155 applicants to the CIS/MSE program
87 candidates admitted
31 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 560 /Q 760 /A 700

Fall 2000 CIS/MSE admissions statistics

228 applicants to the CIS/MSE program
89 candidates admitted
40 students matriculated
Candidates admitted to the CIS/MSE program: Average GRE: V 550 /Q 760 /A 720

MCIT Program

Fall 2022 MCIT admission statistics

1225 applicants to the MCIT program
118 candidates admitted
73 students matriculated
Candidates admitted to the MCIT program: Average GRE: V 162/Q 168/AW 4.3

Fall 2021 MCIT admission statistics

1064 applicants to the MCIT program
75 students matriculated

Fall 2020 MCIT admission statistics

674 applicants to the MCIT program
97 candidates admitted
48 students matriculated
Candidates admitted to the MCIT program: Average GRE: V 160/Q 166/AW 4.2

Fall 2019 MCIT admission statistics

552 applicants to the MCIT program
78 candidates admitted
49 students matriculated
Candidates admitted to the MCIT program: Average GRE: V 161/Q 166/AW 4

Fall 2018 MCIT admission statistics

714 applicants to the MCIT program
73 candidates admitted
Candidates admitted to the MCIT program: Average GRE: V 161/Q 167/AW 4

Fall 2017 MCIT admission statistics

619 applicants to the MCIT program
69 candidates admitted

Fall 2016 MCIT admission statistics

526 applicants to the MCIT program
Candidates admitted to the MCIT program: Average GRE: V 160/Q 165/AW 4

Fall 2015 MCIT admission statistics

445 applicants to the MCIT program
68 candidates admitted
Candidates admitted to the MCIT program: Average GRE: V 160/Q 166/AW 4.5

Fall 2014 MCIT admission statistics

331 applicants to the MCIT program
Candidates admitted to the MCIT program: Average GRE: V 159/Q 165/AW 4

Fall 2013 MCIT admissions statistics

292 applicants to the MCIT program
59 candidates admitted
28 students matriculated
Candidates admitted to the MCIT program: Average GRE – V 159/Q 165/AW 4

Fall 2012 MCIT admissions statistics

214 applicants to the MCIT program
Candidates admitted to the MCIT program: Average GRE – V 610:160/Q 780:163/AW 4

Fall 2011 MCIT admissions statistics

160 applicants to the MCIT program
64 candidates admitted
Candidates admitted to the MCIT program: Average GRE – V 620/Q 770/AW 4

Fall 2010 MCIT admissions statistics

107 applicants to the MCIT program
42 candidates admitted
24 students matriculated
Candidates admitted to the MCIT program: Average GRE – V 570/Q 740/AW 4.5

Fall 2009 MCIT admissions statistics

92 applicants to the MCIT program

Fall 2008 MCIT admissions statistics

71 applicants to the MCIT program
41 candidates admitted
Candidates admitted to the MCIT program: Average GRE – V 570/Q 730/AW 4

Fall 2007 MCIT admissions statistics

Candidates admitted to the MCIT program: Average GRE – V 570/Q 760/AW 4

Fall 2006 MCIT admissions statistics

84 applicants to the MCIT program
46 candidates admitted
Candidates admitted to the MCIT program: Average GRE – V 600/Q730/AW 4.5

Fall 2005 MCIT admissions statistics

47 applicants to the MCIT program
25 candidates admitted
Candidates admitted to the MCIT program: Average GRE – V 590/Q 730/A 750; AW 4.5

Fall 2004 MCIT admissions statistics

73 applicants to the MCIT program
Candidates admitted to the MCIT program: Average GRE – V 520/Q 730/A 670; AW 4.5

Fall 2003 MCIT admissions statistics

97 applicants to the MCIT program
Candidates admitted to the MCIT program: Average GRE – V 560/Q 740/A 710; AW 5.0

Fall 2002 MCIT admissions statistics

104 applicants to the MCIT program
72 candidates admitted
Candidates admitted to the MCIT program: Average GRE – V 560/Q 750/A 700

Fall 2001 MCIT admissions statistics

123 applicants to the MCIT program
90 candidates admitted
Candidates admitted to the MCIT program: Average GRE – V 570/Q 770/A 700
As the methods for computing grade point averages vary greatly depending on the institution, country, etc., it is not possible to provide average GPAs. Whatever method is used for computing the grade point average, candidates are expected to have a high GPA.
All candidates are evaluated equally.
Please note that the GRE scores listed above are averages.

Graduate Program:

Master of Computer and Information Technology
MSE in Data Science
MSE in Embedded Systems (EMBS)
MSE in Robotics
MSE in Computer Graphics and Game Technology
Fellowships + Aid
New Students
How to Register
Graduation + Thesis Information
Academic + Industry Job Postings

Your CIS Contacts:

Redian Furxhiu Graduate Coordinator for on-campus MCIT, CIS/MSE and CGGT programs Office: 308 Levine Phone: 215-898-1668 Email: [email protected]

Staci Kaplan Program Manager for DATS (Data Science MSE) Office: 308 Levine Phone: 215-573-2431 Email: [email protected]

Britton Carnevali Doctoral Program Manager Office: 310 Levine Phone: 215-898-5515 Email: [email protected]

Mariel Celentano Graduate Coordinator for ROBO Office: 459 Levine Phone: 215-573-4907 Email: [email protected]

Liz Wai-Ping Ng Associate Director for Embedded Systems MSE program Office: 313 Levine Phone: 215-898-8543 Email: [email protected]

Julia Esposito PICS Program Coordinator, SCMP Academic Coordinator Office: 3401 Walnut, 5th Fl. Phone: 215-573-6037 Email: [email protected]

IMAGES

The Countries With The Most Doctoral Graduates [Infographic]
How long, on average, does it take to obtain a PhD in Physics?
How The Average Age Of PhD Students In The United States Compares To
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Most of the data in your PhD will come from the last few months! : r
Top Five Critical Factors to be Considered While doing PhD Statistics

VIDEO

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Is PhD End
Pursue PhD Or Take Up A Job? What To Choose?
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Should I PhD or Not?

COMMENTS

Quora
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[Q] how hard is it to get into a PhD program : r/statistics
First year Stats PhD student here, I can share advice based on my application experience. First, your GPA of course is only one component of your application. Rec Letters, research/intern experience, upper level coursework, and strength of personal statement all matter as well. I personally spent a lot of time thinking about what I wanted to ...
What are the benefits of getting a PhD in statistics over a MS in
So, if you have good record (not just grades but grasp), you may be able to get to the same level of salary as a PhD would. This is because statistics is an applied branch and is in demand. Share. Improve this answer. Follow answered Aug 22, 2012 at 22:22. Stat-R Stat-R. 2,635 ...
What are the benefits of getting a PhD in statistics?
A PhD in statistics is more flexible and useful that PhDs in some other areas. The usual issue with PhDs one hears about is that one becomes over-qualified for non-academic work once one has a PhD. Additionally, there is a lot of time spent getting it. However, statistics is intrinsically an applied science, and one that is in big demand across ...
r/statistics on Reddit: Why did you choose to pursue a PhD in
In almost all of my applications, the common theme is that I say I'm interested in health/neuroscience/medical applications. I realized, that I probably should have applied to PhD biostats programs. Frankly, this was my mess up, and I don't want to make my recommenders go out of their way to write more letters for more programs than I told ...
Is a PhD In Statistics Worth It?
In other words, if you're looking at the question through a purely financial lens, yes, a PhD in statistics is worth it. But there's one caveat. The lifetime earnings of a PhD vs a master's recipient in statistics isn't all too significant (on average about $3.6 million vs $3.45 million).
Choosing Statistics PhD: Harvard vs Berkeley?
I know that Berkeley is a better fit for my research interests and I know that research fit is one of the most important parts of choosing a PhD. However, I also hear people say that you should choose a place that you would enjoy and fit in for the next 5 years. I think I would enjoy my time at Harvard more. In addition, Harvard grads seem to ...
[D] I find a post in Quora (whether AI is statistics or not) from a PhD
"AI" as we know it did not grow out of the statistics community. Sean McClure, whose Quora post OP quotes in this post, can fuck right off. AI is certainly an interdisciplinary field with many roots, but any expert (and probably any novice) in the field will tell you statistics is a key part of artifical intelligence.
How Much Of Machine Learning Is Computer Science Vs. Statistics?
Answer by Michael Hochster, PhD in Statistics from Stanford; Director of Research at Pandora, on Quora: I don't think it makes sense to partition machine learning into computer science and statistics.
phd
My question is about this apparently well-received answer on Quora: Q: Which PhD programs in Germany would you recommend? ... Since the OP has the goal of staying in academia after PhD, I wonder if anyone can supply some statistics on how many graduating PhD students in mathematics stay in academia (say for 2/5/10 years after graduation), for ...
ph-d-mathematics-statistics
The minimum number of courses to be done is 6, out of which 3 are compulsory: Algebra, Mathematical Methods and Analysis for Ph.D. Mathematics, and Analysis, Probability Theory and Statistical Inference for Ph.D. Statistics. Minimum research credit requirement for the programme is 80 and students are required to register for 16 research credit ...
Ph.D. in Statistics
The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible ...
Doing a PhD in Statistics
Costs and Funding. Annual tuition fees for PhDs in Statistics are typically around £4,000 to £5,000 for UK/EU students. Tuition fees for international students are usually much higher, typically around £20,000 - £25,000 per academic year. Tuition fees for part time programmes are typically scaled down according to the programme length.
Is a PhD from Harvard worth it? : r/PhD
PhD's are less about "getting all the points possible" and more "is this going to support my research / career agenda." ... /r/Statistics is going dark from June 12-14th as an act of protest against Reddit's treatment of 3rd party app developers. _This community will not grant access requests during the protest.
Department of Statistics
The PhD program prepares students for research careers in probability and statistics in both academia and industry. The first year of the program is devoted to training in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses and s
PhD Program information
PhD Program information. The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course ...
PhD in Statistics: Course Details, Eligibility, Fees, Admission
The PhD Statistics course abroad is up to 5-8 years, depending on the type of course, college and country. The benefits of studying a PhD in Statistics course abroad are access to some of the best resources, facilities, and faculties, apart from worldwide exposure in terms of subject matter and other cultures. Top PhD in Statistics Colleges Abroad
PhD Degree Program
691 Special Topics (topics on rotating basis): Applied Statistics Seminar. Graduate courses offered in our MS Program, subject to approval of advisor and/or graduate directors can be found here. Approved graduate courses offered by other departments (e.g. Math, CS, or ECE) can be found here. Link to Typical Ph.D. Plan Examination Requirement
Is a Master's in Biostatistics worth it nowadays? : r/statistics
Bishops_Guest. •. Yes, If you want to work in academia a PhD is required for pretty much any of the interesting stuff. Still, if I remember correctly, in all biostatistics industry jobs it is about 70% PhD and 30% MS. Out of all the high use statistics disciplines, biostats has the highest PhD rate.
CIS Graduate Program Admissions Statistics
Candidates admitted to the doctoral program: Average GPA: 3.7. Candidates admitted to the doctoral program: Average GPA: 3.78. Candidates admitted to the doctoral program: Average GPA: 3.8. Candidates admitted to the doctoral program: Average GRE: V 167/Q 161/AW 4. Candidates admitted to the doctoral program: Average GRE: V 160/Q 167/AW 4.
Best Statistics Courses Online with Certificates [2024]
Choosing the right statistics course depends on your current knowledge level and career aspirations. Beginners should look for courses that cover the basics of probability, descriptive statistics, and introductory inferential statistics.Those with some experience might benefit from intermediate courses focusing on specific statistical methods like regression analysis, hypothesis testing, and ...
[Q] What is an industry PhD? And how does one apply/start one?
The typical setup of an "industry PhD" is you have a company come together with an academic research group for a joint project. The company puts in some money (usually matched by the government through a region- specific grant program, in Canada it would be NSERC Engage most likely) and they provide a problem for which they need some R&D work done on the cheap by one or more graduate students.
PhD—Doctor of Philosophy in Nursing Science
The Nursing Science Statistics Area of Concentration requires a minimum of 14 credits of advanced statistical methods coursework above and beyond the 10 credits of statistics required for the PhD degree. Of these, two credits are fulfilled by the CSSS Seminar, and a minimum of 12 credits are fulfilled by at least four advanced statistics courses, three of which must come from the list of CSSS ...