• • • # ( Highly Atypical Profile alert !!!) Fall 2023- PhD Profile Evaluation in Stat/Biostat

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Student Type :- International Asian Male

Undergrad Institute:- Top 20 engineering colleges in India (Non-IIT)

Degree:- 4 Year Bachelor's in Engineering ( Graduation year 2015)

Major:- Electrical Engineering

Major Percentage:- 75.7/100 ( GPA 8.07/10)

Some Math Courses - Most math courses in my curriculum were numbered as Math 1, Math 2 etc. The topics covered are as follows

Math 1 (Grade A) - Functions of a single variable: Rolle’s Theorem, Mean value theorem, Taylor’s Theorem. Maclaurin’s series, indeterminate forms, maxima and minima. Functions of several variables, limit and continuity, Partial derivatives, differentials, partial derivatives of a composite function, implicit functions. Taylors Theorem, Maxima and minima. Lagranges method. Rienemam Integration. Definition and properties, Fundamental theory of integral calculus, improper integrals, gamma and beta functions. Multiple integrals, definition of double and triple integrals, properties and applications.

Math 2 (Grade A) - Sequence, Infinite series, Comparison test, D’Alembert’s test, Cauchy’s root test. Vector Algebra: Addition and subtraction of vectors, Different types of products of vectors.Solid Geometry: Cartesian coordinates in three dimension, direction cosines, Equations of straight lines, planes and spheres.Marices: Addition and multiplication of matrices, Determinant of a square matrix and its properties, Transpose and inverse, Solutions of system of linear equations. Symmetric, Skew-symmetric and Hermitian matrices. Ranks of a matrix, Eigenvalues and eigenvectors. Characteristic polynomial. Caley-Hamilton theorem and applications.
Ordinary Differential equation: 1st order exact equations, first order linear equations. Second order linear equation with constant co-efficients. Euler Cauchy equation, method of variation of parameters.

Math 3 (Grade A) - Differential equation of second order with variable coefficients. Ordinary point and regular singularity of second order linear differential equations, series solutions. Bessel functions, Legendre polynomials and their orthogonal properties. Fourier series: Periodic functions, Trigonometric series of sine and cosines. Euler formulae, Derichlets’ conditions, even and odd functions, half range sine and cosine series, Fourier series in intervals, multiple Fourier series, Discrete time fourier series.Partial differential equations: Solution of one dimensional wave and diffusion equations and Laplace’s equations of two dimension by method of separation of variables.
Integral transforms: Laplace’s and Fourier transforms, Properties and applications of differential equations. Discrete Fourier transform, Z-Transform, applications to differential equations.

Math 4 (Grade D) - Complex analysis: Functions of a complex variable, limits, continuity and differentiability. Cauchy-Riemann equations complex integration, Cauchy’s fundamental theorem, Cauchy’s; integral formulae, Taylor’s Theorem, Laurent’s theorem, Singularity, Residue Theorem, Contour Integral.
Vector calculus: Scalar and vector fields, Concepts of gradient, divergence and curl and their expression in Cartesian, cylindrical and spherical coordinates, Laplacian in these coordinates, Gauss, Stokes’ and Green’s theorem.
Probability Theory: Definition, Law of probability, conditional probability Baydes’ theorem, random variables, Probability distribution, exponential binomial, Poisson and normal distributions, estimation of parameters.

Work Experience :-  Have approximately 6 years of experience in Data Science and Analytics field in India ( Close to 3 years of experience in a FAANG Company). Have good programming experience in R, Python and Scala. Have delivered end to end projects on statistical analysis for fortune 500 companies.

Grad Institute :- Big 10 Public University in US ( Ranked in top 25  for Statistics according to US News)

Degree:- Master of Statistics ( Entered Fall 2021 , Graduating Fall 2023)

Concentration:- General Track

Overall Percentage:-4/4 ( Till Spring 2022)

Courses Taken :-  Mathematical Statistics (A) , Unsupervised Learning( A+) , Statistical Modelling (A+) , Time Series Analysis  (A+), Advanced Mathematical Statistics (A) , Advanced Statistical Learning (A)

Courses Currently Taking Bayesian Statistics ,  Advanced Deep Learning

Courses Planning to take (Next Spring) Real Analysis ,  Computational Statistics, Advanced Regression

Research Experience :-

1. Did a 2 month statistical analysis project for educational psychology professor. Paper under review

2. Currently working on an NSF funded project where I need to perform statistical analysis for improving engineering education

3. Working with my time series professor on an independent study ( Change point detection)

Research Interests  Time Series analysis and applications, Bayesian Inference, STEM Education

GRE:- 323 :- 167Q, 156 V, 4 AWA

LORs:- Expecting good LoRs from my research project advisors

Programs Aiming-

STAT PhD

My Alma Mater , NCSU, UT Austin , TAMU

STAT PhD offered by Business School

Chicago Booth (UChicago) , Fox Business School ( Temple University) , Marshal School of Business ( USC) ,  McCombs School of Business ( IROM Statistics Program in UT Austin)

Biostat PhD

UNC Chapel Hill, Yale University, UC Davis , UC San Diego

1. Since I don't have real analysis explicitly in my transcript, can I even think of applying for a PhD in statistics program for Fall 2023 ?

2. Some courses on linear algebra and analysis are covered by my undergrad math courses but I had completed them way back in 2011 and 2012. Would this gap have a negative impact on my application? Additionally, my transcript doesn't have  any math course name explicitly written. How do I let the admission committee know the math topics I have learnt in my undergrad?

3. Since I have been in the industry for quite a while, I was hoping to apply to some of the PhD in Statistics programs offered by business schools. Any idea how they fare as compared to regular Phd in Statistics?

4. Most important question : Does the list of programs above even attainable for someone with my profile? Or should I completely forget about a PhD in Statistics?  I understand that a PhD involves a lot of pre-requisite theoretical background, so I was looking for some applied PhD programs ( Like Biostat etc.) .

I have spent countless hours researching about different programs where someone like me can apply. Was hoping to get some guidance in this forum about programs I can safely target this fall. Any help is appreciated !

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7 hours ago, fulldarklowtar said:

Student Type :- International Asian Male

Undergrad Institute:- Top 20 engineering colleges in India (Non-IIT)

Degree:- 4 Year Bachelor's in Engineering ( Graduation year 2015)

Major:- Electrical Engineering

Major Percentage:- 75.7/100 ( GPA 8.07/10)

Some Math Courses - Most math courses in my curriculum were numbered as Math 1, Math 2 etc. The topics covered are as follows

Math 1 (Grade A) - Functions of a single variable: Rolle’s Theorem, Mean value theorem, Taylor’s Theorem. Maclaurin’s series, indeterminate forms, maxima and minima. Functions of several variables, limit and continuity, Partial derivatives, differentials, partial derivatives of a composite function, implicit functions. Taylors Theorem, Maxima and minima. Lagranges method. Rienemam Integration. Definition and properties, Fundamental theory of integral calculus, improper integrals, gamma and beta functions. Multiple integrals, definition of double and triple integrals, properties and applications.

Math 2 (Grade A) - Sequence, Infinite series, Comparison test, D’Alembert’s test, Cauchy’s root test. Vector Algebra: Addition and subtraction of vectors, Different types of products of vectors.Solid Geometry: Cartesian coordinates in three dimension, direction cosines, Equations of straight lines, planes and spheres.Marices: Addition and multiplication of matrices, Determinant of a square matrix and its properties, Transpose and inverse, Solutions of system of linear equations. Symmetric, Skew-symmetric and Hermitian matrices. Ranks of a matrix, Eigenvalues and eigenvectors. Characteristic polynomial. Caley-Hamilton theorem and applications.
Ordinary Differential equation: 1st order exact equations, first order linear equations. Second order linear equation with constant co-efficients. Euler Cauchy equation, method of variation of parameters.

Math 3 (Grade A) - Differential equation of second order with variable coefficients. Ordinary point and regular singularity of second order linear differential equations, series solutions. Bessel functions, Legendre polynomials and their orthogonal properties. Fourier series: Periodic functions, Trigonometric series of sine and cosines. Euler formulae, Derichlets’ conditions, even and odd functions, half range sine and cosine series, Fourier series in intervals, multiple Fourier series, Discrete time fourier series.Partial differential equations: Solution of one dimensional wave and diffusion equations and Laplace’s equations of two dimension by method of separation of variables.
Integral transforms: Laplace’s and Fourier transforms, Properties and applications of differential equations. Discrete Fourier transform, Z-Transform, applications to differential equations.

Math 4 (Grade D) - Complex analysis: Functions of a complex variable, limits, continuity and differentiability. Cauchy-Riemann equations complex integration, Cauchy’s fundamental theorem, Cauchy’s; integral formulae, Taylor’s Theorem, Laurent’s theorem, Singularity, Residue Theorem, Contour Integral.
Vector calculus: Scalar and vector fields, Concepts of gradient, divergence and curl and their expression in Cartesian, cylindrical and spherical coordinates, Laplacian in these coordinates, Gauss, Stokes’ and Green’s theorem.
Probability Theory: Definition, Law of probability, conditional probability Baydes’ theorem, random variables, Probability distribution, exponential binomial, Poisson and normal distributions, estimation of parameters.

Work Experience :-  Have approximately 6 years of experience in Data Science and Analytics field in India ( Close to 3 years of experience in a FAANG Company). Have good programming experience in R, Python and Scala. Have delivered end to end projects on statistical analysis for fortune 500 companies.

Grad Institute :- Big 10 Public University in US ( Ranked in top 25  for Statistics according to US News)

Degree:- Master of Statistics ( Entered Fall 2021 , Graduating Fall 2023)

Concentration:- General Track

Overall Percentage:-4/4 ( Till Spring 2022)

Courses Taken :-  Mathematical Statistics (A) , Unsupervised Learning( A+) , Statistical Modelling (A+) , Time Series Analysis  (A+), Advanced Mathematical Statistics (A) , Advanced Statistical Learning (A)

Courses Currently Taking Bayesian Statistics ,  Advanced Deep Learning

Courses Planning to take (Next Spring) Real Analysis ,  Computational Statistics, Advanced Regression

Research Experience :-

1. Did a 2 month statistical analysis project for educational psychology professor. Paper under review

2. Currently working on an NSF funded project where I need to perform statistical analysis for improving engineering education

3. Working with my time series professor on an independent study ( Change point detection)

Research Interests  Time Series analysis and applications, Bayesian Inference, STEM Education

GRE:- 323 :- 167Q, 156 V, 4 AWA

LORs:- Expecting good LoRs from my research project advisors

Programs Aiming-

STAT PhD

My Alma Mater , NCSU, UT Austin , TAMU

STAT PhD offered by Business School

Chicago Booth (UChicago) , Fox Business School ( Temple University) , Marshal School of Business ( USC) ,  McCombs School of Business ( IROM Statistics Program in UT Austin)

Biostat PhD

UNC Chapel Hill, Yale University, UC Davis , UC San Diego

1. Since I don't have real analysis explicitly in my transcript, can I even think of applying for a PhD in statistics program for Fall 2023 ?

2. Some courses on linear algebra and analysis are covered by my undergrad math courses but I had completed them way back in 2011 and 2012. Would this gap have a negative impact on my application? Additionally, my transcript doesn't have  any math course name explicitly written. How do I let the admission committee know the math topics I have learnt in my undergrad?

3. Since I have been in the industry for quite a while, I was hoping to apply to some of the PhD in Statistics programs offered by business schools. Any idea how they fare as compared to regular Phd in Statistics?

4. Most important question : Does the list of programs above even attainable for someone with my profile? Or should I completely forget about a PhD in Statistics?  I understand that a PhD involves a lot of pre-requisite theoretical background, so I was looking for some applied PhD programs ( Like Biostat etc.) .

I have spent countless hours researching about different programs where someone like me can apply. Was hoping to get some guidance in this forum about programs I can safely target this fall. Any help is appreciated !

You say nothing about your career goals? Why the PhD? This seems like a lot to do for you at first glance. Applying to different fields will very likely require for you to change your approach for each program. Also keep in mind that PhD in stat run by business schools tend to be very small (which makes them extremely competitive).

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16 hours ago, Dr-H said:

You say nothing about your career goals? Why the PhD? This seems like a lot to do for you at first glance. Applying to different fields will very likely require for you to change your approach for each program. Also keep in mind that PhD in stat run by business schools tend to be very small (which makes them extremely competitive).

Apologies for not mentioning it earlier. With a PhD I intend to study and perform research on change detection and anomaly detection at a deeper level and hopefully join academia after completion. I am not looking to go back to industry at the moment.

Agreed , I have to fine tune my statement of purpose for each program. But the common theme of my research interest is time series analysis. As time series in itself has many applications (from business to healthcare), I believe applying to different schools make sense. However, please let me know if the list of schools I am targeting is good enough or if I need to make any changes.

With regards to Business schools, I am aware of their small cohort size making them more competitive. However, since I believe their research is more "applied", I have kept them in the list. Is there any specific thing business schools look for in their statistics PhD applicant pool?

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