To become a successful data scientist, a mixture of skills is required. Data science is a dynamic field with a constantly changing definition. However, one certainty is that it combines statistics, mathematics, and computer science
Learning a new skillset can be hard. A key tip for data science, is to put what you’ve learned into practice. To this end, playing around on e.g. Kaggle with your newly acquired skills can be useful. Alternatively, you can create your own projects and post them on GitHub.
Resource Overview
- Datacamp: Datacamp offers technically everything you need to get the basics of data science. That being said, it’s not free.
- ForrestKnight: a YouTuber who created an open-source computer science degree on GitHub. It contains many useful resources for data science in general. Check it out here
- CodeEmporium: a YouTuber who explains many concepts of data science without shying away from the statistics and mathematics behind it.
- RitvikMath: a YouTuber who also explains many concepts of data science (with a slightly heavier focus on statistics and mathematics)
- DigitalSreeni: a YouTuber who explains data science concept using live coding in Python
- Lemma: a nice website to get the basics of linear algebra right. This guy also has a dedicated YouTube-channel that explains many other math-subjects (e.g. calculus) useful for data science
- Coursera: Coursera contains many useful MOOCs to get you going in data science. However, specifically the Introduction to Machine Learning course from Stanford is very useful.
- MIT Deep Learning: a great introduction to deep learning from intro to expert
- Learning a new skillset can be hard. A key tip for data science, is to put what you’ve learned into practice. To this end, playing around on e.g. Kaggle with your newly acquired skills can be useful. Alternatively, you can create your own projects and post them on GitHub.
For more tips, you can follow me on Twitter @manojgupta_rch