Learn By Example: Statistics and Data Science in R

Learn By Example: Statistics and Data Science in R, available at $34.99, has an average rating of 3.8, with 82 lectures, based on 377 reviews, and has 4869 subscribers.

You will learn about Harness R and R packages to read, process and visualize data Understand linear regression and use it confidently to build models Understand the intricacies of all the different data structures in R Use Linear regression in R to overcome the difficulties of LINEST() in Excel Draw inferences from data and support them using tests of significance Use descriptive statistics to perform a quick study of some data and present results This course is ideal for individuals who are Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role or Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science or Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists or Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis It is particularly useful for Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role or Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science or Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists or Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis.

Enroll now: Learn By Example: Statistics and Data Science in R

Summary

Title: Learn By Example: Statistics and Data Science in R

Price: $34.99

Average Rating: 3.8

Number of Lectures: 82

Number of Published Lectures: 82

Number of Curriculum Items: 82

Number of Published Curriculum Objects: 82

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Harness R and R packages to read, process and visualize data
Understand linear regression and use it confidently to build models
Understand the intricacies of all the different data structures in R
Use Linear regression in R to overcome the difficulties of LINEST() in Excel
Draw inferences from data and support them using tests of significance
Use descriptive statistics to perform a quick study of some data and present results

Who Should Attend

Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists
Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis

Target Audiences

Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists
Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.�

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.�

Let’s parse that.

Gentle, yet thorough:�This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.�

Data Science, Statistics and R:�This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.�

Real life examples:�Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.�

What’s Covered:

Data Analysis with R:�Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames

Linear Regression:�Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots

Data Visualization in R:�Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2

Descriptive Statistics:�Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots

Inferential Statistics:�Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Course Curriculum

Chapter 1: Introduction

Lecture 1: You, This course and Us

Lecture 2: Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data

Lecture 3: R and RStudio installed

Chapter 2: The 10 second answer : Descriptive Statistics

Lecture 1: Descriptive Statistics : Mean, Median, Mode

Lecture 2: Our first foray into R : Frequency Distributions

Lecture 3: Draw your first plot : A Histogram

Lecture 4: Computing Mean, Median, Mode in R

Lecture 5: What is IQR (Inter-quartile Range)?

Lecture 6: Box and Whisker Plots

Lecture 7: The Standard Deviation

Lecture 8: Computing IQR and Standard Deviation in R

Chapter 3: Inferential Statistics

Lecture 1: Drawing inferences from data

Lecture 2: Random Variables are ubiquitous

Lecture 3: The Normal Probability Distribution

Lecture 4: Sampling is like fishing

Lecture 5: Sample Statistics and Sampling Distributions

Chapter 4: Case studies in Inferential Statistics

Lecture 1: Case Study 1 : Football Players (Estimating Population Mean from a Sample)

Lecture 2: Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)

Lecture 3: Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)

Lecture 4: Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)

Lecture 5: Case Study 5: A/B Testing (Comparing the means of two populations)

Lecture 6: Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)

Chapter 5: Diving into R

Lecture 1: Harnessing the power of R

Lecture 2: Assigning Variables

Lecture 3: Printing an output

Lecture 4: Numbers are of type numeric

Lecture 5: Characters and Dates

Lecture 6: Logicals

Chapter 6: Vectors

Lecture 1: Data Structures are the building blocks of R

Lecture 2: Creating a Vector

Lecture 3: The Mode of a Vector

Lecture 4: Vectors are Atomic

Lecture 5: Doing something with each element of a Vector

Lecture 6: Aggregating Vectors

Lecture 7: Operations between vectors of the same length

Lecture 8: Operations between vectors of different length

Lecture 9: Generating Sequences

Lecture 10: Using conditions with Vectors

Lecture 11: Find the lengths of multiple strings using Vectors

Lecture 12: Generate a complex sequence (using recycling)

Lecture 13: Vector Indexing (using numbers)

Lecture 14: Vector Indexing (using conditions)

Lecture 15: Vector Indexing (using names)

Chapter 7: Arrays

Lecture 1: Creating an Array

Lecture 2: Indexing an Array

Lecture 3: Operations between 2 Arrays

Lecture 4: Operations between an Array and a Vector

Lecture 5: Outer Products

Chapter 8: Matrices

Lecture 1: A Matrix is a 2-Dimensional Array

Lecture 2: Creating a Matrix

Lecture 3: Matrix Multiplication

Lecture 4: Merging Matrices

Lecture 5: Solving a set of linear equations

Chapter 9: Factors

Lecture 1: What is a factor?

Lecture 2: Find the distinct values in a dataset (using factors)

Lecture 3: Replace the levels of a factor

Lecture 4: Aggregate factors with table()

Lecture 5: Aggregate factors with tapply()

Chapter 10: Lists and Data Frames

Lecture 1: Introducing Lists

Lecture 2: Introducing Data Frames

Lecture 3: Reading Data from files

Lecture 4: Indexing a Data Frame

Lecture 5: Aggregating and Sorting a Data Frame

Lecture 6: Merging Data Frames

Chapter 11: Regression quantifies relationships between variables

Lecture 1: Introducing Regression

Lecture 2: What is Linear Regression?

Lecture 3: A Regression Case Study : The Capital Asset Pricing Model (CAPM)

Chapter 12: Linear Regression in Excel

Lecture 1: Linear Regression in Excel : Preparing the data

Lecture 2: Linear Regression in Excel : Using LINEST()

Chapter 13: Linear Regression in R

Lecture 1: Linear Regression in R : Preparing the data

Lecture 2: Linear Regression in R : lm() and summary()

Lecture 3: Multiple Linear Regression

Lecture 4: Adding Categorical Variables to a linear model

Lecture 5: Robust Regression in R : rlm()

Lecture 6: Parsing Regression Diagnostic Plots

Chapter 14: Data Visualization in R

Lecture 1: Data Visualization

Lecture 2: The plot() function in R

Lecture 3: Control color palettes with RColorbrewer

Lecture 4: Drawing barplots

Lecture 5: Drawing a heatmap

Lecture 6: Drawing a Scatterplot Matrix

Lecture 7: Plot a line chart with ggplot2

Instructors

Loony Corn
An ex-Google, Stanford and Flipkart team

Rating Distribution

1 stars: 5 votes
2 stars: 20 votes
3 stars: 68 votes
4 stars: 138 votes
5 stars: 146 votes

Frequently Asked Questions

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