Thinking about a career switch and Data scientist/ML learning has caught my eye. I have a masters in electronic materials engineering from top ranked program. I’ve been told that a data scientist needs to understand the following core concepts: Basic Statistics (equivalent to AP stat or STAT100) Python/R/SQL Linear algebra To this end, I’ve down the following: 99% course mastery on AP Stat Khan Academy: understand null hypothesis testing, t-tests, z-tests, chi-squared, and all the basic probability, mean, median, mode, analyzing 2-way table, etc. did this in about 2 weeks. Working through CodeCademy’s Python course. Currently 70% through in about 2 weeks. Hoping to finish by early next week. From here, I was considering going through the R and SQL program followed by the entirety of CodeCademy’s Data Science pathway (which seems to overlap heavily with the material I’ve already covered in AP stat and their python program. However, it seems to have the following additional topics: Python Lambda Functions, Pandas, Data Wrangling and tidying, Matplotlib, Seaborn, NLP, supervised Learning, k-nearest neighbor, k-means clustering, hierarchical clustering, and miscellaneous projects. Does this sound like a good plan for someone trying to break into the space? I’m trying to avoid going back to school because I truly believe it’s not necessary these days, but I also want to make sure I’m going down the right path.