How To Identify The Best Resources To Be A Self-Taught Data Scientist
- How to identify what you want to be (and finding the right resources)
- Resources I use and what works for me
- Take a look at a couple of Data Science related job descriptions and see what appeals to you. Would you like to be a Machine Learning Engineer, a Data Engineer, a Data Analyst or perhaps a Data Business Analyst?
- Next make a list of all the skills you find in the job descriptions. See if you already have any of those skills. Perhaps you already know a little SQL or Python that can be leveraged or maybe you are good at Data Analysis.
- Next find the gaps in your skills and identify which are the most popular skills among all the job descriptions you have been looking at. Pick about 5 new skills and start working from there.
- Google: Here use all your googling skills..I know you have them.. :-) . Something like 'Top resources to learn xyz' should do the trick.
- Quora: Another great option to look up for advise.
- Class Central: Here you can look up any MOOCs that you have identified and check their ratings/feedback from students who have taken the class. This will help you avoid wasting time on classes that might not suit your learning style.
- Kaggle: It's my go to source for datasets. There are a vast variety of datasets from beginner to advanced level. You can also take part in competitions to see how you rank among others.
- UCI Machine Learning Repository: Another great source for datasets that are segregated by data type, machine learning model etc.
- Kaggle datasets: Find a dataset you like and start working on it. At first it will only be summarizing and cleaning the data but slowly you'll understand how to apply machine learning models.
- Twitter: There is a huge inspiring community on twitter where you can find the latest happenings in the Data Science world. Also there are a couple of learning challenges that occur weekly/monthly, explore and find one what you like. Some of my favorite areâ??-â??#TidyTuesday #SoDS18 #MakeoverMonday and #100daysofMLcode