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Nam T.

Statistician / Programmer for Math and Computer Science Tutoring

Statistician / Programmer for Math and Computer Science Tutoring

$200/hour

  • 140 hours tutoring

About Nam


Bio

Hi!

Myself: 
For the last decade, my day job has been being a hybrid statistician / programmer. More specifically, I build predictive models while ingesting a lot of data, and then I have to bring those models into production (with programming). I also recently finished a master's degree in statistics, wanting more foundation for what I do day-to-day.

Tutoring Experience: 
I tutored whilst in my undergrad, but a lot of my tutoring also comes from explaining things to colleagues or...

Hi!

Myself: 
For the last decade, my day job has been being a hybrid statistician / programmer. More specifically, I build predictive models while ingesting a lot of data, and then I have to bring those models into production (with programming). I also recently finished a master's degree in statistics, wanting more foundation for what I do day-to-day.

Tutoring Experience: 
I tutored whilst in my undergrad, but a lot of my tutoring also comes from explaining things to colleagues or on-boarding new colleagues. Further, I ran a summer internship program, where I developed a curriculum for grad students of varying skill levels.

Math and Computer Science Background: 
I'm constantly having to use math/statistics to answer questions revolving my work. Further, I'm always in a state where I'm reading new academic papers that might give me an edge in my day job. Lastly, to further my own "self-education" during work, I finished a master's degree in statistics, seeking out a more theoretical underpinning of what I do. With regards to Computer Science, I have over a decade of industry programming experience, initially building "full stack" web applications before "full stack" was a word to now building latency sensitive applications.

Expectations: 
It takes two to tango. While I can help you understand and grasp material, for the material to become ingrained, it's going to take hard work on your part as well! I can help setup a game plan for you that can result in that mastery. If that sounds like something you're good with, then please contact me!

Best,
Nam


Education

University of Oklahoma
Mathematics
Texas A&M
Masters

Policies


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Subjects

Computer

R,

R

I've used R in both a personal capacity as well as a professional capacity for a decade and change now. When analyzing data sets and running statistical tests, I prefer to use R over Python (which I *do* use just as much), since the built-in toolset for R facilitates this much more easily, e.g., summary(lm(y ~ x, dat)) as opposed to having to import the "statsmodels" package in python (which I can do and have done before). In regards to fitting models, I'm familiar with glm, lm, L1/L2 regularization via elasticnet, etc. To be sure, I can do the parallel in python easily (sklearn, although there are subtleties). To be sure, even over that decade of R usage, I'm still continuing to learn new things as the "meta" shifts, e.g., I've switched over to tidyverse/ggplot/dplyr ever since it was introduced by Mr. Wickham. I'm also familiar with what it takes to incorporate R into a pipeline, e.g., data ingestion, fitting models, serializing the results to disk to be used by another process. And, lastly, while I don't profess to know *everything* about R, I'm quick to fill in missing holes in my knowledge base when pointed out by either reading the source code off of github or using my superb googling skills ;)
Computer Programming, Python

Corporate Training

Statistics

Statistics

For the past decade (and change), I've been working in a statistical (as well as programming) context, where I look at dirty, messy data sets; clean up the data (yay programming); extract/engineer interesting features; and build predictive (bagging/boosting/linear regression) as well as inferential models (make the residuals look normal and IID). In between all of that, I've done other "statistics," e.g., frequentist A/B testing (simultaneous confidence intervals) or "bayesian" A/B testing (sample from the posterior distributions). From a pedagogic perspective, I have experience explaining *why* t-tests work and statistical significance is a thing , to the subtlety of bagging (dealing with low bias high variance procedures), to why "rejection sampling" works, and to everything before and after. I love statistics and hope I can convey some of it to you!

Homeschool

Statistics,

Statistics

For the past decade (and change), I've been working in a statistical (as well as programming) context, where I look at dirty, messy data sets; clean up the data (yay programming); extract/engineer interesting features; and build predictive (bagging/boosting/linear regression) as well as inferential models (make the residuals look normal and IID). In between all of that, I've done other "statistics," e.g., frequentist A/B testing (simultaneous confidence intervals) or "bayesian" A/B testing (sample from the posterior distributions). From a pedagogic perspective, I have experience explaining *why* t-tests work and statistical significance is a thing , to the subtlety of bagging (dealing with low bias high variance procedures), to why "rejection sampling" works, and to everything before and after. I love statistics and hope I can convey some of it to you!
Algebra 2

Math

R,

R

I've used R in both a personal capacity as well as a professional capacity for a decade and change now. When analyzing data sets and running statistical tests, I prefer to use R over Python (which I *do* use just as much), since the built-in toolset for R facilitates this much more easily, e.g., summary(lm(y ~ x, dat)) as opposed to having to import the "statsmodels" package in python (which I can do and have done before). In regards to fitting models, I'm familiar with glm, lm, L1/L2 regularization via elasticnet, etc. To be sure, I can do the parallel in python easily (sklearn, although there are subtleties). To be sure, even over that decade of R usage, I'm still continuing to learn new things as the "meta" shifts, e.g., I've switched over to tidyverse/ggplot/dplyr ever since it was introduced by Mr. Wickham. I'm also familiar with what it takes to incorporate R into a pipeline, e.g., data ingestion, fitting models, serializing the results to disk to be used by another process. And, lastly, while I don't profess to know *everything* about R, I'm quick to fill in missing holes in my knowledge base when pointed out by either reading the source code off of github or using my superb googling skills ;)
Statistics,

Statistics

For the past decade (and change), I've been working in a statistical (as well as programming) context, where I look at dirty, messy data sets; clean up the data (yay programming); extract/engineer interesting features; and build predictive (bagging/boosting/linear regression) as well as inferential models (make the residuals look normal and IID). In between all of that, I've done other "statistics," e.g., frequentist A/B testing (simultaneous confidence intervals) or "bayesian" A/B testing (sample from the posterior distributions). From a pedagogic perspective, I have experience explaining *why* t-tests work and statistical significance is a thing , to the subtlety of bagging (dealing with low bias high variance procedures), to why "rejection sampling" works, and to everything before and after. I love statistics and hope I can convey some of it to you!
Algebra 2

Most Popular

Statistics,

Statistics

For the past decade (and change), I've been working in a statistical (as well as programming) context, where I look at dirty, messy data sets; clean up the data (yay programming); extract/engineer interesting features; and build predictive (bagging/boosting/linear regression) as well as inferential models (make the residuals look normal and IID). In between all of that, I've done other "statistics," e.g., frequentist A/B testing (simultaneous confidence intervals) or "bayesian" A/B testing (sample from the posterior distributions). From a pedagogic perspective, I have experience explaining *why* t-tests work and statistical significance is a thing , to the subtlety of bagging (dealing with low bias high variance procedures), to why "rejection sampling" works, and to everything before and after. I love statistics and hope I can convey some of it to you!
Algebra 2

Summer

Statistics,

Statistics

For the past decade (and change), I've been working in a statistical (as well as programming) context, where I look at dirty, messy data sets; clean up the data (yay programming); extract/engineer interesting features; and build predictive (bagging/boosting/linear regression) as well as inferential models (make the residuals look normal and IID). In between all of that, I've done other "statistics," e.g., frequentist A/B testing (simultaneous confidence intervals) or "bayesian" A/B testing (sample from the posterior distributions). From a pedagogic perspective, I have experience explaining *why* t-tests work and statistical significance is a thing , to the subtlety of bagging (dealing with low bias high variance procedures), to why "rejection sampling" works, and to everything before and after. I love statistics and hope I can convey some of it to you!
Algebra 2

Ratings and Reviews


Rating

5.0 (80 ratings)
5 star
(80)
4 star
(0)
3 star
(0)
2 star
(0)
1 star
(0)

Reviews

Show reviews that mention

All reviews

Great teacher, enjoyable lesson!

Nam did an incredible job with understanding where I was currently at with my prior experience and the direction that I was hoping to head. I’m looking forward to my next lesson and am excited to continue learning from Nam!

Amber, 2 lessons with Nam

talented, knowledgeable, sound teacher of R technology

Nam is an excellent R tutor. He seeks to understand your needs, and he gets you to those results. He is engaging, easy to work with, and he uses his teaching skills and experiences to quickly tailor what you are hoping to learn from him to a style that will work well for you. Give him a go if you're lucky enough to get his time and you'll see results.

J, 3 lessons with Nam

AMAZING 5 STARS

Nam was extremely understanding of how new I was to stats and taught me a ton of stuff in a short window without me feeling overwhelmed. Also is a great guy with a good sense of humor.

Lucas, 1 lesson with Nam

Hands-down the best tutor out there

Nam is single-handedly the best tutor I've ever had. I was really impressed by: A) how prepared he was for our lesson (he had worked on a Jupyter Notebook in advance to show me how to create linear regression models in Python) and B) how knowledgable he was about statistics. He was so encouraging, patient and receptive to questions, even when I had to interrupt him from time to time (sorry Nam!). Not all smart people are good teachers, but Nam is both. Excited to work again with him in the future!

Isabelle, 3 lessons with Nam

Knowledgable & Diligent

Nam worked with me on R fundamentals with regards to loading/cleaning data & using trend lines to make predictions. Worked through that & more with me, was very efficient & willing to answer all questions.

Cass, 1 lesson with Nam

Literally the best tutor ever.

Nam was extremely helpful with coding in R. He was receptive to questions and made sure I understood the concepts as well as the code. He is so kind and easy to learn from. He can dumb down or complicate his style of explaining topics based on your knowledge level of the subject. He also just has a large knowledge of R overall and is great at adapting to exactly what you need to be done. He is best tutor.

Danielle, 1 lesson with Nam

The PERFECT tutor!!!

I️ am a student at ASU in applied statistics (ABS350 level). Within minutes Nam had responded to my tutoring request, set up an appointment, and was VERY knowledgeable on the topic. Not to mention this was all very last minute and he was right there when I️ needed him!!! If you're looking for someone to jump right in with you and your studies...Nam is your guy! Thanks Nam for all your hard work!

Kendall, 1 lesson with Nam

Systems Analysis

Nam and I went over M/M/1 and M/M/N queueing systems and how to derive various information using arrival rate. Great lesson overall.

Jack, 22 lessons with Nam

Statistics/Probability

Nam is a great tutor. He keeps things lighthearted and is willing to work through things at the students pace. Very knowledgeable guy!

Jack, 22 lessons with Nam
Contact Nam

Response time: 4 hours

$200/hour

Nam T.'s Photo

Nam T.

$200/hour

  • No subscriptions or upfront payments

  • Only pay for the time you need

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

Contact Nam

Response time: 4 hours