I’m currently doing a Data Science Challenge!
Goal: Get a job within 12 months! (12th June, 2022)
Programming: Basically none. (Messed around with basic HTML but never got far)
Statistics: 4th year Psychology statistics course (Did well but forgotten most of it)
Maths: High school Math B & C (Did well too but can’t really remember much)
Study an average of 4 hours daily distraction free in a state of deep concentration
Daily Blog Post to summarise things learnt
TLDR – Learn just enough & do. If you don’t understand something, refer back to resource and learn.
I found this video by Tina to be the most congruent to my own beliefs about learning by doing. It is really helpful as it gives a broad overview of Data Science as well! I’ll focus on the Data Science one (Spend about 20-30 hours before first Data Science Project). She also made a video about learning to code by building your own Program with Python, which I will use as well along with the #100days of code as a mini-project.
See on Notion (Warning: It’s Long) – Will supplement gaps of knowledge by self-studying this in parallel.
Rough Timeline of Goals
Goals: Direct Approach
Week 2: Start my first Data Science Project.
Week 5: Start building my first Python application.
Week 9: Python application should be complete? Create another one.
Week 12: Should have cycled a couple of Data Science Projects by now. Keep rinsing and Repeat
Week 30 – Should start looking at completing projects with real world impact
Week 40 – Should be actively competing on Kaggle and looking for jobs.
Goals: Self-Planned Curriculum (Hard to predict how much I can stick to this but this is a rough guide)
Week 1: Khan Linear Algebra & Manga Guide to Linear Algebra (Goal to be able to Start COMPSCI 109A)
Week 1: Udacity: Intro to Data Science (Try to get through this fast)
Week 2: Statistics 110: Probability – Harvard
Week 2: Learn Python the Hard Way / Try Course – edx: Introduction to Computer Science and Programming Using Python (See if it can replace Udacity Course in week 1)
Week 3: Additional Maths to catch up on forgotten things to University level -COMPSCI109A Prerequisites (Test yourself and learn what you don’t klnow)
Week 4: Udacity: Intro to Data Analysis (NumPy & Pandas)
Week 5: Seaborn – Kaggle Tutorial & Jake VanderPlas The Python Visualization Landscape PyCon 2017 – Data Visualization – Overview
Week 5: Think Stats
Week 6: Course – Andrew Ng Stanford & Stanford CS 229 & Finish up on prerequisites for COMPSCI 109A
Week 7: Start COMPSCI 109A – Work through Book – Mathematics for Machine Learning
Week 9: Databases: Relational Databases and SQL, YouTube – Intro to scikit-learn, SciPy2013
Week 10: Book: Intro to Stat Learning in R & Introduction to Data Science with R
Week 11: Video YouTube – Codebasics ML Playlist
Week 13: Keep writing Python Programs – Work through how to think like a Computer Scientist
Week 15: Course – Data Science at Scale Specialization
Week 16: University of Washington – Linear Programming Course
Week 18: Convex Optimization – Stanford
Week: 19: COMPSCI 109B
Week 20: I’ll probably know more by then and also might not finish half the courses listed above!
Updated 9th July, 2021