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**Description**

R is a programming language and software environment for statistical computing and graphics that is widely used among statisticians and data miners for data analysis. In this course, you'll get a thorough run-through of how R works and how it's applied to data science. Before you know it, you'll be crunching numbers like a pro, and be better qualified for many lucrative careers.

- Access 82 lectures & 9 hours of content 24/7
- Cover basic statistical principles like mean, median, range, etc.
- Learn theoretical aspects of statistical concepts
- Discover datatypes & data structures in R, vectors, arrays, matrices & more
- Understand Linear Regression
- Visualize data in R using a variety of charts & graphs
- Delve into descriptive & inferential statistics

Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Details & Requirements

- Length of time users can access this course: lifetime access
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels

Compatibility

- Internet required

**Terms**

- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.

- Introduction
- You, This course and Us (2:32)
- Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data (12:58)
- R and RStudio installed (5:10)

- The 10 second answer : Descriptive Statistics
- Descriptive Statistics : Mean, Median, Mode (10:07)
- Our first foray into R : Frequency Distributions (6:07)
- Draw your first plot : A Histogram (3:11)
- Computing Mean, Median, Mode in R (2:21)
- What is IQR (Inter-quartile Range)? (8:08)
- Box and Whisker Plots (3:11)
- The Standard Deviation (10:24)
- Computing IQR and Standard Deviation in R (6:06)

- Inferential Statistics
- Drawing inferences from data (3:25)
- Random Variables are ubiquitous (16:54)
- The Normal Probability Distribution (9:31)
- Sampling is like fishing (6:14)
- Sample Statistics and Sampling Distributions (9:25)

- Case studies in Inferential Statistics
- Case Study 1 : Football Players (Estimating Population Mean from a Sample) (6:49)
- Case Study 2 : Election Polling (Estimating Population Proportion from a Sample) (7:51)
- Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean) (13:53)
- Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion) (9:49)
- Case Study 5: A/B Testing (Comparing the means of two populations) (17:18)
- Case Study 6: Customer Analysis (Comparing the proportions of 2 populations) (11:50)

- Diving into R
- Harnessing the power of R (7:26)
- Assigning Variables (8:48)
- Printing an output (13:03)
- Numbers are of type numeric (5:25)
- Characters and Dates (7:30)
- Logicals (3:24)

- Vectors
- Data Structures are the building blocks of R (8:24)
- Creating a Vector (2:23)
- The Mode of a Vector (4:18)
- Vectors are Atomic (2:24)
- Doing something with each element of a Vector (3:09)
- Aggregating Vectors (1:28)
- Operations between vectors of the same length (5:39)
- Operations between vectors of different length (5:30)
- Generating Sequences (6:25)
- Using conditions with Vectors (2:04)
- Find the lengths of multiple strings using Vectors (2:22)
- Generate a complex sequence (using recycling) (2:49)
- Vector Indexing (using numbers) (6:56)
- Vector Indexing (using conditions) (6:18)
- Vector Indexing (using names) (2:27)

- Arrays
- Creating an Array (11:36)
- Indexing an Array (7:38)
- Operations between 2 Arrays (2:09)
- Operations between an Array and a Vector (2:45)
- Outer Products (6:23)

- Matrices
- A Matrix is a 2-Dimensional Array (7:59)
- Creating a Matrix (2:00)
- Matrix Multiplication (2:49)
- Merging Matrices (2:06)
- Solving a set of linear equations (2:06)

- Factors
- What is a factor? (6:48)
- Find the distinct values in a dataset (using factors) (1:28)
- Replace the levels of a factor (2:18)
- Aggregate factors with table() (1:40)
- Aggregate factors with tapply() (5:07)

- Lists and Data Frames
- Introducing Lists (5:11)
- Introducing Data Frames (4:28)
- Reading Data from files (4:52)
- Indexing a Data Frame (5:38)
- Aggregating and Sorting a Data Frame (6:28)
- Merging Data Frames (3:30)

- Regression quantifies relationships between variables
- Introducing Regression (12:22)
- What is Linear Regression? (16:06)
- A Regression Case Study : The Capital Asset Pricing Model (CAPM) (6:34)

- Linear Regression in Excel
- Linear Regression in Excel : Preparing the data (9:53)
- Linear Regression in Excel : Using LINEST() (16:48)

- Linear Regression in R
- Linear Regression in R : Preparing the data (13:05)
- Linear Regression in R : lm() and summary() (16:04)
- Multiple Linear Regression (12:16)
- Adding Categorical Variables to a linear model (7:44)
- Robust Regression in R : rlm() (3:14)
- Parsing Regression Diagnostic Plots (12:10)

- Data Visualization in R
- Data Visualization (6:23)
- The plot() function in R (3:42)
- Control color palettes with RColorbrewer (4:15)
- Drawing barplots (5:25)
- Drawing a heatmap (2:52)
- Drawing a Scatterplot Matrix (3:41)
- Plot a line chart with ggplot2 (8:19)

access

lifetime

content

9 Hours

enrolled

114