How I Got Started in Data Science Part 1

Recently I’ve been nosing around in data science and decided to start studying it more seriously.

Why data science?

My job at the Good Kitchen has changed and evolved tremendously since I started. I started out in marketing, but now I primarily function in an operations role. I liaise with our co-manufacturer, generate a lot of production instructions, and do a lot of analysis. I really enjoy and am good at the analysis and instruction part but am not as fond of the food manufacturing and operational management part.

Intrinsically I know I need to keep learning and evolving. I recently had an interaction where it would have been handy to know SQL as part of our migration from Magento to Shopify. So in order to stay effective for my job as my company keeps evolving I need to stay on top of my skill development.

I’ve been seeing a lot of posts by Paul Graham about Lambda School on Twitter. On a whim I start reading about it and became really interested. I’ve always meant to learn more computer programming and math to increase my competencies.

Lambda school offers intensive courses on a variety of programming related careers. The one that attracted me the most was data science. It just spoke to me. However, the commitment seemed a bit much (9 – 18 months). But the next start date was July 1 so I decided to start learning and then commit.

Where I Always Start: Google and Research

I started Googling around for “beginner data science”. I found a really helpful set of reading: Beginner Data Science on Medium. I used this to foster my first plan for getting my feet wet in data science.

The Plan

Goal: Get my feet wet with minimal cost and start learning without jumping into an intensive bootcamp or online degree program.

Plan: Math and coding first

I found this article and really liked the infographic. I really made sense to me, you’re going to use programming to execute statistical principles. So why not learn at the same time?

I decided to start with a set of core courses (all of these are free).

1. Codeacademy: Learn How to Code (Time to completion: 15 minutes)
2. CodeAcademy: Learn Statistics With Python
3. Udacity: Intro to Statistics
4. Udacity: Intro to Descriptive Statistics
5. Udacity: Intro to Inferential Statistics

These should be enough to accomplish the most important thing right now. Not having the perfect plan, but getting started with positive action.

Bootcamps / Courses for Later

1. Lambda School Data Science Track
2. School of AI: Data Lit
3. Coursera: A Crash Course in Data Science
4. Flatiron School: Data Science Online
5. Udacity: Data Scientist Nano Degree
6. Thinkful: Data Science Flexible
7. Datacamp: Data Scientist with Python
8. Dataquest: Learn Data Science