### I am building a portfolio on a new blog to apply to Data jobs in 2018.

from letustweak |

### I combed through resources and am sharing highlights.

## What is Data Science?

As described in a recent DataCamp podcast with David Robinson,Data Transformation- how can I clean this and reshape it?

Statistical inference- how can I separate signal from noise?

Prediction- I've got some inputs, how can I predict the outputs? (a classifier)

Visualization- how can I better understand this? (making graphs)

Communication- how can I share the results?

## Where I am learning it?

I am reading help pages, documentation, github repos, and stackoverflow to analyze open data sets.I did EliteDataScience's 7 day email introduction and feel confident about learning all of their Machine Learning steps through projects, online resources, and Kaggle's courses + competitions.

### Python

- All courses with Dr. Chuck Severance at Python for Everybody.

- Practical Deep Learning for Coders with Jeremy Howard at fast.ai.

- Exercises on Project Euler

- Practical Deep Learning for Coders with Jeremy Howard at fast.ai.

- Exercises on Project Euler

### SQL

- Exercises on codeacademy, hackerrank, and leetcode.### R

- Tidyverse (Hadley Whickam's R for Data Science)- Rachel Tatman's Data Science in R Kaggle course

### D3

- freeCodeCamp has a beta site offering 6 modules- Blocks contain examples

### More Resources

- DataCamp has some great tutorials in R, python, SQL, and others.- Andrew Ng's Machine Learning Coursera course (old Octave back when it was free).

- Version Control with Git Udacity course

- MITx Introduction to Probability: The Science of Uncertainty

- TypingClub is not a crime

### Online curricula

- David Venturi's DIY Masters (He did some blog updates in 2017)

### Community

I joined the Coursera Data Science community.I attended various free Data Science meetups in SF at Galvanize and Metis.

I am stalking many Data Scientist blogposts and podcasts to keep up with trends and learn proper etiquette.

Looking for an in-person group of DIY Data Scientists who want to meet up and hack on data sets together.

### Stretch Goals

- Kirill Eremenko's Data Science A-Z Udemy course- Udacity Data Analyst Nanodegree

- crush

Hit me up if you are DIY Data Science learning also.