Introduction to Probability and Data with R

If you have a Calculus Background, you can't go wrong with Harvard Stat 110 lectures (online).

If not, you also cannot go wrong with: https://www.coursera.org/learn/probability-intro

If it’s basic understanding that you’re jumping into statistical learning too soon?

These topics covered in the book then are bit too advanced or require a lot of background.

Why not start with OLS and look into a course for statistics in data science.

This is a good course I’ve heard: link

I'm taking the course ' Introduction to Probability and Data ' on coursera, and the professor is explaining the how random sampling and random assignment is an essential part of an experiment to be able to generalize the results and deduce the cause.

I did this statistics course

But for the visualization part of this project, I just used online sources

Advice for a mathematical moron

I am interested in learning Machine Learning as a hobby and, maybe, in the distant future as a career. The problem is, I have a graduate degree in a totally unrelated field and am a dunce when it comes to math.

I read the Super Harsh Guide and quickly realized that Elements is well out of my depth, so I began reading the (apparently) easier Introduction to Statistical Learning; the material covered within is still somewhat beyond me. Are there any suggestions as to where to start for someone who knows very little math beyond basic introductory algebra? I know it's a big ask and I'm aware that I'll likely never work at Google Brain, however I'm really interested in the topic and would like to become more educated for my own personal satisfaction.

I have been looking at the Intro to Probability and Data course for introductory statistics and the Mathematics for Machine Learning: Linear algebra and Calculus courses for general math. Do these seem sufficient for getting into the Intro book? Contrarily, is this overkill/should I just read the Introduction to Stat Learning book and glean as much as I can without any prep? Will I even be able to understand these courses with only a basic algebra background?

I know this is a text dump; thanks for reading and please know that any insight is much appreciated.

This map was made as a part of my final project for one of Coursera/Duke's Statistics in R class.
Data was provided by the class, sourced from the CDC's annual BRFSS survey.

Code is hosted on my Github in the form of an R Markdown file.

Bonus lollipop map that includes all states and territories

I am planning to update with the 2017 BRFSS data when it comes out.

No worries. I think DS Math Skills is ok if you want a review of very basic math topics, but there are better courses by Duke:

Introduction to Probability and Data Inferential Statistics Bayesian Statistics Linear Regression and Modeling

https://www.coursera.org/specializations/jhu-data-science

https://www.coursera.org/learn/probability-intro

https://www.coursera.org/specializations/executive-data-science

https://www.coursera.org/specializations/data-science-python

https://www.coursera.org/learn/inferential-statistics-intro

https://www.coursera.org/learn/basic-statistics

https://www.edx.org/course/statistics-and-r

https://www.edx.org/course/using-python-for-research

https://www.edx.org/course/python-for-data-science

https://www.edx.org/course/statistical-thinking-for-data-science-and-analytics

DataCamp.com

UT admissions sent an alternative list of courses, saying they can't give a definitive answer about the UCSD ones.

The courses they recommended are here:

STATISTICS

SDS 302: Fundamentals of Statistics, edX.org, MIT, 18 weeks: https://www.edx.org/course/fundamentals-of-statistics Statistics: Unlocking the World of Data, edX.org, Univ. of Edinburgh, 8 weeks https://www.edx.org/course/statistics-unlocking-the-world-of-data Basic Statistics: coursera.org, Univ. of Amsterdam, 8 weeks: https://www.coursera.org/learn/basic-statistic

Taken Together: 1) Introduction to Probability and Data with R, coursera.org, Duke Univ., 5 weeks https://www.coursera.org/learn/probability-intro 2) Inferential Statistics, coursera.org, Duke Univ., 5 weeks https://www.coursera.org/learn/inferential-statistics-intro

SDS 328M: BioStatistics: edX.org, Doane Univ., 8 weeks: https://www.edx.org/course/biostatistics-2

Taken Together: 1) Summary Statistics in Public Health, coursera.org, Johns Hopkins Univ., 4 weeks https://www.coursera.org/learn/summary-statistics 2) Hypothesis Testing in Public Health, coursera.org, Johns Hopkins Univ., 4 weeks https://www.coursera.org/learn/hypothesis-testing-public-health 3) Simple Regression Analysis in Public Health, coursera.org, Johns Hopkins Univ., 4 weeks https://www.coursera.org/learn/simple-regression-analysis-public-health

CALCULUS/MATH

Calculus 1A,B,C mitX, 13 weeks each https://www.edx.org/course/calculus-1a-differentiation Linear Algebra - Foundations to Frontiers, edX.org, Univ of Texas, 15 weeks https://www.edx.org/course/linear-algebra-foundations-to-frontiers The Math of Data Science: Linear Algebra, edX.org, RICE, 8 weeks https://www.edx.org/course/math-of-data-science-linear-algebra Gilbert Strang. RES.18-010 A 2020 Vision of Linear Algebra. Spring 2020. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.