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### Courses which will help you with Data Science

- Data visualization principles
- How to communicate data-driven findings
- How to use ggplot2 to create custom plots
- The weaknesses of several widely-used plots and why you should avoid them

- The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
- How to use models to aggregate data from different sources
- The very basics of Bayesian statistics and predictive modeling

- The basics of machine learning
- How to perform cross-validation to avoid overtraining
- Several popular machine learning algorithms
- How to build a recommendation system
- What is regularization and why it is useful

- Basic R syntax
- Foundational R programming concepts such as data types, vectors arithmetic, and indexing
- How to perform operations in R including sorting, data wrangling using dplyr, and making plots

- Important concepts in probability theory including random variables and independence
- How to perform a Monte Carlo simulation
- The meaning of expected values and standard errors and how to compute them in R
- The importance of the Central Limit Theorem