Hi there! Great question! First of all, Data Science has become a buzzword and an umbrella term. Data Science is a combination of 3 fields - Computer Science, Math and Statistics, and the Business Domain. If you use use scientific methods, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain insights from data, I'd consider you a Data Scientist.
Topics to learn:
- Programming (Python is extremely popular in DS)
Python for Everybody Specialization (course)
Automate the Boring Stuff with Python (book)
- Math/Statistics (linear algebra, calculus, probability)
Mathematics for Machine Learning Specialization (course)
The Elements of Statistical Learning (book)
SQL course on Udemy (course)
Kaggle - Learn Pandas (course)
- Exploratory Data Analysis (EDA)
Data Visualization with Python (course)
- Machine Learning
Machine Learning by Stanford on Coursera (course)
DeepLearning.AI Specialization (course)
- GitHub - to host your projects (more of that below)
Git & GitHub - The Complete Git & GitHub Course (course)
Framework of learning:
- Learn just enough to get you started
- Do a project
- Be consistent/accountable
Practice makes it permanent! The best way to learn Data Science is by doing Data Science. And projects are a great way to learn faster, more deeply, retain information for a longer period, and showcase your skills. Learning is a never-ending process. I know this list might feel overwhelming because there's too much to learn. Break it down to small steps, learn something new everyday, and apply it to a personal project. It can be anything you're interested in! You will get a sense of accomplishment when you finish a project and will see amazing results in the long run.
Also, you don't have to wait to finish one topic to start the next one. I'd recommend learning different topics at the same time and applying them to your projects. Once you're done learning something, iterate over steps 1 and 2 by learning something new and starting a new project or improving the old one. It's a great way to stay excited and motivated throughout this journey. Once you're comfortable, you can start adding more topics.
Lastly, hold yourself accountable. Set deadlines to your learning process and be consistent. Don't try to learn everything at once or you might get overwhelmed and burnout. Accountability is key to doing anything and will be important to not give up.
Book recommendations/extra material:
This is not a perfect roadmap, there are tons of extra materials to learn. But hopefully I covered the basics to get you started. Data Science is HARD, so I'll finish this with a cliché motivational quote:
“The beginning is perhaps more difficult than anything else, but keep heart, it will turn out all right.” — Vincent van Gogh
Good luck :)