Search
Steven D.'s Photo

Steven D.

Statistics PhD with Years of Experience

Statistics PhD with Years of Experience

$70/hour

About Steven


Bio

I have a PhD in Statistics (Berkeley), MS in Finance (Berkeley) and BS in Mathematics (Europe). I've been tutoring statistics, stochastic processes, optimization, machine learning, finance and actuarial mathematics for 11 years. I have helped high school students, undergraduate students and graduate students to improve performance on their assignments and exams. I have helped researchers and business professionals with high-level data-driven projects.

My teaching style tends to adapt to...

I have a PhD in Statistics (Berkeley), MS in Finance (Berkeley) and BS in Mathematics (Europe). I've been tutoring statistics, stochastic processes, optimization, machine learning, finance and actuarial mathematics for 11 years. I have helped high school students, undergraduate students and graduate students to improve performance on their assignments and exams. I have helped researchers and business professionals with high-level data-driven projects.

My teaching style tends to adapt to the needs of the student. Some students need structured exposition: block by block, solution by solution. Some students need an informal discussion, connecting various results and ideas in different fields. Some students need me to tell them how to structure the study program and which courses to take over the next 1.5 years. Some clients (often business professionals) just need the data analysis done, as thoroughly as possible, and then they learn from my results and my conclusions. Therefore, the teaching format is flexible. One thing which is uniformly true: the cost of the service increases with the level of work. For example, requesting implementation of a robust pattern recognition system would require a different budget compared to studying Poisson processes or option pricing on the graduate level.

I work in the following packages: R (RStudio), Matlab, Python, Stata, SPSS, JMP, Microsoft Excel, Minitab, EViews, SAS. Depending on student’s needs, I can exploit standard libraries or build my own, customized code. Over the years I have seen some packages improve substantially and I use them more and more often. R and Stata are notable examples. Python is becoming more popular because of its memory management and CPU management style.

My broad statistical training allows me to expose professionals in one applied field to methods most popular in another applied field. For example, some aspiring biostatisticians and medical professionals (not all) come to me with quite narrow data analysis training, centered around ANOV


Policies

  • Tutor’s lessons: In-person
  • Hourly Rate: $70
  • Travel policy: Within 40 miles of Chicago, IL 60655
  • Lesson cancellation: 24 hours notice required
  • No background check

  • Your first lesson is backed by our Good Fit Guarantee

Schedule

Loading...

Sun

Mon

Tue

Wed

Thu

Fri

Sat


Subjects

Business

Actuarial Science,

Actuarial Science

Actuarial Science is scientific field which focuses on modeling insurance risks and managing practical aspects of those. Actuaries are closely linked to financial engineers working on pricing and risk management of credit/mortgage derivatives as well as biostatisticians running survival analyses for patients and other subjects. The three fields employ similar methods and borrow fresh ideas from one another. Of course, there are implementational nuances in each field. There are different ways of monetizing the adverse risks in each field. Actuarial science can be split into two major directions: 1) getting the exact risk profile of a single client, based on his/her characteristics, 2) modeling correlated claims from multiple clients in the same insurance portfolio. I have more than ten years of academic and practical experience in each direction. From the mathematical point of view, the correlated nature of different claims (direction 2) poses the most interesting mathematical challenges. Still, the day-to-day juice filling the salary of an actuary is direction 1: understanding how the risk profile of client A is different from the risk profile of client B as a function of time. My past actuarial projects have included building models for correlated credit risks using standard and non-standard copulas, applying Kendall's tau and Spearman's rho to prescreen relationships between selected risk factors, building a survival curve for a specific segment of subjects using non-parametric smoothing techniques and model-based interpolation, applying extreme value theory to extrapolate survival curves into durations where the data are sparse, running cluster analysis to see if it confirms domain-knowledge based segmentation of subjects into different risk profiles, performing extensive Monte Carlo simulation of relative simple models and identifying situations where their errors are relatively small, testing ad-hoc adjustments implemented in real analytic systems before me, etc. The implementation has been done in MATL
Econometrics,

Econometrics

Over the last eleven years I have worked with more than two hundred economists, social scientists, finance professionals and formally introduced finance companies, including hedge funds. We have dealt with problems pertaining to time series analysis, heteroskedastic models, panel data, structural equation modeling, robust inference, bootstrap, multi-factor representations, etc... Stata, R and EViews were my preferred tools for tackling econometric problems but I also gave clients the option to work in Matlab, SAS or Python. In my humble opinion, JMP, SPSS and Minitab are somewhat simplistic when it comes to serious econometric tasks. They are more suitable for other applied areas, like biology or quality control.
Finance, Microsoft Excel

Computer

Microsoft Excel, Python, R, SPSS

Corporate Training

Finance, Microsoft Excel, SPSS, Statistics

Homeschool

Calculus, Statistics

Math

Actuarial Science,

Actuarial Science

Actuarial Science is scientific field which focuses on modeling insurance risks and managing practical aspects of those. Actuaries are closely linked to financial engineers working on pricing and risk management of credit/mortgage derivatives as well as biostatisticians running survival analyses for patients and other subjects. The three fields employ similar methods and borrow fresh ideas from one another. Of course, there are implementational nuances in each field. There are different ways of monetizing the adverse risks in each field. Actuarial science can be split into two major directions: 1) getting the exact risk profile of a single client, based on his/her characteristics, 2) modeling correlated claims from multiple clients in the same insurance portfolio. I have more than ten years of academic and practical experience in each direction. From the mathematical point of view, the correlated nature of different claims (direction 2) poses the most interesting mathematical challenges. Still, the day-to-day juice filling the salary of an actuary is direction 1: understanding how the risk profile of client A is different from the risk profile of client B as a function of time. My past actuarial projects have included building models for correlated credit risks using standard and non-standard copulas, applying Kendall's tau and Spearman's rho to prescreen relationships between selected risk factors, building a survival curve for a specific segment of subjects using non-parametric smoothing techniques and model-based interpolation, applying extreme value theory to extrapolate survival curves into durations where the data are sparse, running cluster analysis to see if it confirms domain-knowledge based segmentation of subjects into different risk profiles, performing extensive Monte Carlo simulation of relative simple models and identifying situations where their errors are relatively small, testing ad-hoc adjustments implemented in real analytic systems before me, etc. The implementation has been done in MATL
Biostatistics,

Biostatistics

Over the last eleven years I have worked with more than a hundred of PhDs, MDs, pharmaceutical professionals and biologists on problems pertaining to spatial patterns of species in different remote areas, intra-year growth dynamics, GWAS, epidemiology and drug assessment. The posed problems called for a collection of statistical methods each time. The examples were generalized linear models, zero inflation, multivariate analysis of variance, survival analysis, latent state detection, propensity score matching, spatial statistics, ... Typically, genetics called for high-tech statistical weaponry while drug testing could be done using more traditional statistical tools. The following statistical packages were used in the process: R, Minitab, SAS, JMP, SPSS and Matlab. I have a PhD in Statistics (Berkeley).
Calculus, Probability, R, SPSS, Statistics

Most Popular

Calculus, Statistics

Other

Finance

Science

Biostatistics,

Biostatistics

Over the last eleven years I have worked with more than a hundred of PhDs, MDs, pharmaceutical professionals and biologists on problems pertaining to spatial patterns of species in different remote areas, intra-year growth dynamics, GWAS, epidemiology and drug assessment. The posed problems called for a collection of statistical methods each time. The examples were generalized linear models, zero inflation, multivariate analysis of variance, survival analysis, latent state detection, propensity score matching, spatial statistics, ... Typically, genetics called for high-tech statistical weaponry while drug testing could be done using more traditional statistical tools. The following statistical packages were used in the process: R, Minitab, SAS, JMP, SPSS and Matlab. I have a PhD in Statistics (Berkeley).
Econometrics

Econometrics

Over the last eleven years I have worked with more than two hundred economists, social scientists, finance professionals and formally introduced finance companies, including hedge funds. We have dealt with problems pertaining to time series analysis, heteroskedastic models, panel data, structural equation modeling, robust inference, bootstrap, multi-factor representations, etc... Stata, R and EViews were my preferred tools for tackling econometric problems but I also gave clients the option to work in Matlab, SAS or Python. In my humble opinion, JMP, SPSS and Minitab are somewhat simplistic when it comes to serious econometric tasks. They are more suitable for other applied areas, like biology or quality control.

Summer

Calculus, Statistics
Contact Steven

Response time: 1 day

$70/hour

Steven D.'s Photo

Steven D.

$70/hour

  • No subscriptions or upfront payments

  • Only pay for the time you need

  • Find the right fit, or your first hour is free

Contact Steven

Response time: 1 day