W ! E 6 1 N 0 2 R O F R Data Analysis and Visualization </> CURATED COURSE R Data Analysis and Visualization A course in five modules Master the art of building analytical models using R with your Course Guide Edwin Moses Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language To contact your Course Guide Email: [email protected] BIRMINGHAM - MUMBAI Meet Your Course Guide Welcome to this course on R, the statistical programming language for data scientists and statisticians. With this course, you'll embark on a journey of learning R for data science. This course has been designed for you by me Edwin Moses—your Course Guide. I am here to help you experience a wonderful journey on a tried and tested path. I've designed this course for you and you'll be seeing me through the whole journey, offering you my thoughts and ideas behind what you're going to learn next and why I recommend each step. I'll provide tests and quizzes to help you reflect on your learning, and code challenges that will be pitched just right for you through the course. If you have any questions along the way, you can reach out to me over email and I'll make sure you get everything from the course that we've planned – for you to become a working R developer. Details of how to contact me are included on the first page of this course. Course Structure The R learning path created for you has five connected modules. Each of these modules are a mini-course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module! Now, let’s look at the pathway these modules create and how they will take you from doing data analysis with R to creating analytical models based on machine learning. Course journey This course begins by looking at the Data Analysis with R module. This module will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs. This module will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. Through inspecting large datasets using tableplot and stunning three-dimensional visualizations, you will know how to produce, customize, and publish advanced visualizations using this popular, and powerful, framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this module feeling confident in your ability to know which data mining algorithm to apply in any situation. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. The Course Roadmap and Timeline Here's a view of the entire course plan before we begin. This grid gives you a topic overview of the whole course and its modules, so you can see how we will move through particular phases of learning to use R, what skills you’ll be learning along the way, and what you can do with those skills at each point. I also offer you an estimate of the time you might want to take for each module, although a lot depends on your learning style how much you’re able to give the course each week! Module 1: Data Analysis with R Chapter 1: RefresheR 3 Navigating the basics 3 Getting help in R 9 Vectors 10 Functions 16 Matrices 19 Loading data into R 22 Working with packages 25 Chapter 2: The Shape of Data 29 Univariate data 29 Frequency distributions 30 Central tendency 34 Spread 38 Populations, samples, and estimation 41 Probability distributions 43 Visualization methods 48 Chapter 3: Describing Relationships 57 Multivariate data 57 Relationships between a categorical and a continuous variable 58 Relationships between two categorical variables 63 The relationship between two continuous variables 66 Visualization methods 74 Chapter 4: Probability 83 Basic probability 83 A tale of two interpretations 89 Sampling from distributions 90 The normal distribution 94 [ i ]