CoVar

Exactly. And to take it one step further, choose one industry you are interested in. That way you will gain invaluable domain experience as you add relevant portfolio projects.

If you don't have an industry in mind, you can use a site like glassdoor.com and search for data scientist positions by city and industry to get a feel for demand.

Usually MOOC for resume don't help as everyone does them. The advice that I found useful for resume building is working on projects that you can catalog in a portfolio.

With regards to gaining math skills, this upcoming MOOC from Microsoft on EdX looks promising[1].

[1] https://www.edx.org/course/essential-mathematics-for-artific...

For those interested, Raj Chetty, the young economist leading the study, taught course titled "Using Big Data to Solve Economic and Social Problem" that is available on Youtube[1].

The popular course is taught as an introduction to economics for Stanford freshman.

https://www.youtube.com/watch?v=eqAeKQJcaUU&list=PLDcD0S_1fQ...

There's lecture notes available from an MIT open course just on pricing that I found helpful. Below are all of the lecture notes [1], and the summary lecture note [2].

[1] https://ocw.mit.edu/courses/sloan-school-of-management/15-81...

[2] https://ocw.mit.edu/courses/sloan-school-of-management/15-81...

Currently in a MS in Statistics program. This website is definitely on my favorites now. I've been collecting class-contained resources before my start of the program next semester. Here they are, in order of depth/difficulty of the subject:

Stanford https://lagunita.stanford.edu/courses/course-v1:OLI+ProbStat...

CMU http://oli.cmu.edu/courses/free-open/statistics-course-detai...

UCI http://ocw.uci.edu/courses/math_131a_introduction_to_probabi...

http://ocw.uci.edu/courses/math_131b_introduction_to_probabi...

MIT https://ocw.mit.edu/courses/electrical-engineering-and-compu...

Harvard https://projects.iq.harvard.edu/stat110

On the same subject, here are some tips I gleamed from Heather at SalesFolk:

One email, one focus. Send 8 different emails to every prospect. Four days apart. 30% responses come from email 5-8.

Keep it conversational, in first person.

Create benefit driven messages. How your product can help solve their problems?

Cold emails should be 2-5 sentences.

Again keep it conversational.

Keep words and sentences bare minimum.

Prove your worth. Show social proof or case study.

A/B test your message. Different value propositions. Different time/day.

Thursday Friday better than Monday.

I think there is some nuance that is missed when performing studies that compares hours practiced to mastery. I'm currently reading a book called A Mind for Numbers (the companion book for the popular MOOC on Coursera called Learning How to Learn) and what I am realizing is that not everyone practices or studies the most optimal way.

"For example, the number of hours of deliberate practice to first reach "master" status (a very high level of skill) ranged from 728 hours to 16,120 hours. This means that one player needed 22 times more deliberate practice than another player to become a master."

Maybe those 728 hours were more efficient and effective deliberate practice than the 16,120 hours.

Edit: the book is called "A Mind for Numbers", not "A Mind for Math"