This course will expand your understanding of statistics so you can create analytic models in R. High-level data science techniques will be presented to you in a practical manner, to help you bridge the gap between the questions you wish to answer, the data used for analysis, and how to create some of the classic models used in data analytics.
You will start off by understanding dimensionality reduction and data mining in R and learning how to simplify complex datasets and unearth patterns from data. Moving on, you will understand hypothesis testing and p-values. You will also demonstrate one-sample and two-sample tests and the benefits they provide as very sophisticated analytical techniques. You will understand how data can give you predictive insights into the future and will conclude by presenting data in a way that will allow you to answer questions with data-driven confidence.
By the end of the course, you will be capable of utilizing R's statistical prowess to analyze a variety of datasets and present these insights effectively.
This course is for people from the business and scientific sectors who would like to broaden their data analytic capabilities using R. Having a working knowledge of R as well as basic understanding of statistics is assumed.
Accelerate your data analytic capabilities from a basic understanding to being able to apply and interpret results effectively, providing deep insight into a plethora of data-driven scenarios
Contains the maximum number of practical examples and mini tests with minimal lecturing to encourage a natural and self-rewarding progression
Perform and interpret results from the most used statistical and ML techniques used by data professionals such as linear models, k-means clustering, and Principal Component Analysis.