# Resources

**Places for learning R:**

- Swirl: Learn R in R. It sounds hard but it really isn’t.
- DataCamp: A little heavy with hand-holding but it is great for beginners.
- Coursera R programming course
- Udemy R programming course
- BookDown: Collection of free open source books written by some of the top people. Especially check out these ones:
- Gaston Sanchez, UC Berkeley: So many R tutorials and vignettes that will blow your mind.
- Statistical tools for high-throughput data analysis (STHDA): Maintained by Alboukadel Kassambara (PhD in Bioinformatics and Cancer Biology) who authored several helpful R packages including
`ggpubr`

,`survminer`

,`ggcorplot`

, and`factoextra`

. - useR! Machine Learning Tutorial: Tutorial from the R user conference 2016 focusing on using machine learning algorithms in R.

**Fantastic datasets and where to find them:**

- Kaggle: Community curated datasets from all sorts of disciplines
- Harvard Dataverse: Harvard-managed database containing ~100K datasets from various sources.
- Our World in Data: Numbers of the World

**Websites that give you a helping hand when you are stuck:**

- Stack Overflow: For coding problems
- Cross Validated: For questions about statistics and whatnot
- Biostars:Bioinformatics forum contributed by many across the globe
- R-bloggers: A repository of blogs focusing on R across the globe.

**Places for understanding statistics and machine learning better**

- StatQuest: A great way of learning statistics and machine learning concepts without getting into heavy mathematics.
- Introduction to Statistical Learning: Perfect for understanding how statistics and machine learning works, and it involves minimal maths.
- Elements of Statistical Learning: The big brother of the Introduction to Statistical Learning course above. For a more detailed dive into the concepts.