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Data is everywhere, and statisticians and analysts everywhere need to handle this data efficiently and tactfully. In comes R, a powerful programming language, arming developers with the tools to cater to their needs. This course will give you everything you need to start making software that can unlock your statistics and data.

The course is broken down into three parts. The first part will introduce R Studio and the basics of R—using packages and teaching you programming concepts such as variables, vectors, arrays, loops, and matrices. By solving coding challenges, you will gain a strong foundation for data munging.

With the basics mastered, we will take you through a number of topics such as handling dates with the lubridate package, handling strings with the stringr package, writing functions, debugging, error handling, and writing an apply family of functions. When you’ve mastered data munging, we’ll focus on visualizing data using base graphics.

Naturally, the next step is to learn how to make statistical inferences. We walk you through the fundamentals of univariate and bivariate analysis, computing confidence intervals, interpreting p values, and working with statistical significance. You’ll see how and when to use some of the commonly used statistical tests. With that, you will be ready for your first full-scale data analysis project to test the skills you’ve learned.

Finally, you will glimpse two powerful packages for data munging, the dplyr and data.table, which have both seen a rise in the R community. It is imperative to learn about both of these packages because much modern R code has been written using them.

With the help of interesting examples and coding challenges, this course will ensure that you have all the hacks and tricks you need to get started with R.

About The Author

Selva Prabhakaran is a data scientist with a global e-commerce organization. During his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife.

Who is the target audience?
  • If you are looking to start your data science career, or are already familiar with data science, statistics, and machine learning concepts, but want to switch to R, this course will be a great place to start

Course Curriculum

Section 1
1.1 The course overview 00:00:00
1.2 Installing R 00:00:00
1.3 Installing RStudio 00:00:00
1.4 Installing Packages 00:00:00
Section 2: Working with vectors
2.1 Data Types and Data Structures 00:00:00
2.2 Vectors 00:00:00
2.3 Random Numbers, Rounding and Binding 00:00:00
2.4 Missing Values 00:00:00
2.5 The Which() Operator 00:00:00
Section 3: R Essentials
3.1 Lists 00:00:00
3.2 Set Operations 00:00:00
3.3 Sampling and Sorting 00:00:00
3.4 Check Conditions 00:00:00
3.5 For Loops 00:00:00
Section 4: Dataframes and Matrices
4.1 Dataframes 00:00:00
4.2 Importing and Exporting Data 00:00:00
4.3 Matrices and Frequency Tables 00:00:00
4.4 Merging Dataframes 00:00:00
4.5 Aggregation 00:00:00
4.6 Melting and Cross Tabulations with dcast() 00:00:00
Section 5: Core Programming
5.1 Dates 00:00:00
5.2 String Manipulation 00:00:00
5.3 Functions 00:00:00
5.4 Debugging and Eror Handling 00:00:00
5.5 Fast Loops with apply() 00:00:00
5.6 Fast Loops with lapply(), sapply() and vapply() 00:00:00
Section 6: Making Plots with Base Graphics
6.1 Creating and Customizing an R Plot 00:00:00
6.2 Drawing Plots with 2 y Axes 00:00:00
6.3 Multiplots and Custom Layout 00:00:00
6.4 Creating Basic Graph Types 00:00:00
Section 7: Statistical Inference
7.1 Univariate Analysis 00:00:00
7.2 Norman Distribution, Central Limit Theorem, and Confidence Intervals 00:00:00
7.3 Correlation and Covariance 00:00:00
7.4 Chi-sq Statistic 00:00:00
7.5 ANOVA 00:00:00
7.6 Statistical Tests 00:00:00
Section 8: R Very Own Project
8.1 Project 1 – Data Munging and Summarizing 00:00:00
8.2 Project 2 – Visualization with Base Graphics 00:00:00
8.3 Project 3 – Statistical Inference 00:00:00
Section 9: DPlyR and Pipes
9.1 Pipes with Magrittr 00:00:00
9.2 The 7 Data Manipulation Verbs 00:00:00
9.3 Aggregation and Special Functions 00:00:00
9.4 Two Table Verbs 00:00:00
9.5 Working With Databases 00:00:00
Section 10: data.table
10.1 Understanding Basics, Filter, and Select 00:00:00
10.2 Understanding Syntax, Creating and Updating Columns 00:00:00
10.3 Aggregating Data, .N, and .I 00:00:00
10.4 Chaining, Functions, and .SD 00:00:00
10.5 Fast Loops with set(), Keys, and Joins 00:00:00

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