Data Science and Analytics using R programming
4.2 (612 ratings)
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Data Science and Analytics using R programming

Learn to Describe, Visualize and Analyze data using R; Learn basic Statistical Inference and Regression Methods using R
4.2 (612 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,241 students enrolled
Last updated 7/2017
English
Price: $55
30-Day Money-Back Guarantee
Includes:
  • 5 hours on-demand video
  • 1 Article
  • 16 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Program using R up to an intermediate level
  • Learn how to use R Studio
  • Learn to install and use packages in R
  • Learn basic statistics
  • Learn how to visualize data in R
View Curriculum
Requirements
  • Some prior programming experience (in any language) could prove helpful.
  • Some background in basic Mathematics could prove helpful.
Description

This course teaches and demonstrates some of the most basic, yet crucial concepts in Data Science and Analytics.

  1. We will introduce R and the features that make it so useful as a Data Science and Data Analytics platform.
  2. We will introduce RStudio, the most popular, free Integrated Development Environment for writing R code.
  3. We will learn common methods for reading data into R and how to create and use dataframes in R. 
  4. We will learn about R Packages -- how to find them, install them and use them in R. 
  5. We will learn how to visualize univariate data. In particular, we will learn how to use R to create common univariate graphs (e.g. bar plots, histograms, density plots, box plots)
  6. We will learn how to summarize data in R, including the use of descriptive statistics and cross-tabulations.
  7. We will learn Statistical methods for analyzing relationships between two variable using R (e.g. correlations, t-tests, chi-square tests). 
  8. We will learn more on how to visualize multi-variate data. In particular, we will learn how to use R to visualize relationships among two or more variables (e.g. scatter plots, scatter-plot matrices, line plots, correlograms, mosaic plots).


Who is the target audience?
  • This course is for you if you are a student or a working professional who want to learn Data Analytics
  • This course is for you if you want to learn Programming, using R.
  • This course is for you if you want to learn basic Statistics, using R.
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Curriculum For This Course
24 Lectures
04:47:15
+
Introduction, Reading and Describing Data
7 Lectures 01:29:24

We will address the following issues:

  • What is R?
  • Downloading and Installing R
  • The R Environment
  • Reviewing R's Popularity
Preview 12:03

We will address the following issues:

  • What is Rstudio? 
  • Downloading and Installing Rstudio
  • RStudio's IDE Features
Introduction to RStudio
10:31

We will address the following issues:

  • Introduction to R Packages
  • Demonstrating the use of a Package
  • Downloading and Installing a Package
    • Using the console
    • Using the RStudio GUI
    • List of available packages from CRAN
R Packages
17:18

We will explore a dataset related to store sales of Coke and Pepsi.

Understanding a Sample Retail Dataset
12:31

We will learn to use the following R functions related to reading data in R:

  • setwd()
  • read.csv()
  • View()
Reading Data into R
05:09

We will learn how to find the summary statistics for a given dataset in R. We will learn how to use the following functions in R:

  • summary()
  • describe()
  • head(), tail(), some()
  • dim()
  • min(), max()
  • mean(), median(), sd()
Describing Data
18:31

We will learn to use the following R functions related to segmenting and summarizing data in R:
  • aggregate()
  • by()
  • apply()


    R functions - aggregate(), by(), apply()
    13:21
    +
    Visualizing Data
    6 Lectures 01:18:54

    We will explore a dataset related to Customer Relationship Management.

    Understanding the CRM data
    13:40

    We will learn how to create and use Histograms for data visualization, using R.

    (a) Single Variable Visualization
    11:01

    We will learn how to create and use BoxPlots for data visualization, using R.

    (b) Single Variable Visualization
    12:33

    We will learn how to create and use ScatterPlots for data visualization, using R.

    Preview 12:32

    We will continue discussing Scatterplots. We will learn how to create Multi-Panel plots, using R.

    (b) Relationships Between Continuous Variables
    11:04

    We will learn how to create Scatterplot Matrices in R using the pairs() and scatterplotmatrix() functions and discuss their use.

    We will learn how to execute and interpret the Pearson's Correlation Test, using the cor.test() function in R. We will also learn how to visualize correlations using the corrplot() and corrplot.mixed() functions in R.

    (c) Relationships Between Continuous Variables
    18:04
    +
    Visualizing Data using the lattice Package
    2 Lectures 22:40

    We will explore a dataset related to Cable TV Subscribers.

    Understanding a Dataset related to Cable TV Subscribers
    07:55

    We will learn how to use the lattice() package for data visualization, in R.

    In particular, we will learn how to visualize discrete variables using the histogram() function in package lattice. We will also learn how to visualize continuous variables using the barchart() and bwplot() functions in package lattice.


    Visualizing Groups using Package lattice
    14:45
    +
    Frequency and Contingency Tables
    5 Lectures 01:09:26

    We will explore a dataset related to Arthritis treatment. We will learn how to create One-Way Tables in R using the table() and prop.table() functions. We will also learn how to create Two-Way Tables, using the xtabs(), margin.table(), addmargins(), CrossTable() functions in R.

    Preview 19:06

    We will continue to explore a dataset related to Arthritis treatment. We will learn how to create Three-Way Tables using the xtabs(), ftable(), margin.table(), prop.table() functions in R.

    Three-Way Contingency Tables
    11:19

    We will learn how to run a Chi-Square Test of Independence, using the chisq.test() function in R. We will also learn about how to interpret and analyze Chi-Square test results.

    Chi-Square Test of Independence
    10:25

    We will learn how to run a Fisher's Exact Test, using the fisher.test() function in R. We will also learn how to find Measures of Association using the assocstats() function in R. We will also learn how to interpret and analyze Fisher's Exact test results and Measures of Association.

    Fisher's Exact Test, Measures of Association
    08:52

    We will learn how to select subsets of a dataset using the table() and prop.table() functions in R. We will also learn how to create new column variables in a dataframe. We will use this methodology for comparing groups of data, using R.

    Comparing Groups
    19:44
    +
    Linear Regression
    4 Lectures 26:36
    Simple Linear Regression (Part 1)
    03:07

    Simple Linear Regression (Part 2)
    04:22

    Simple Linear Regression (Part 3)
    08:23

    Simple Linear Regression (Part 4)
    10:44
    About the Instructor
    Prof. Sameer Mathur
    4.2 Average rating
    1,144 Reviews
    2,964 Students
    2 Courses
    Marketing Professor at IIM Lucknow

    Prof. Sameer Mathur has a Ph.D. and M.S. in Marketing from the prestigious Tepper School of Business, Carnegie Mellon University, USA, (Tepper is ranked in the top 20 management programs in the world by the Economist, Forbes, Bloomberg, US News and World Report.)

    He is a professor at the Indian Institute of Management (IIM), Lucknow, India. He was previously a professor in the Marketing department at the prestigious McGill University, Canada for several years. (McGill University is ranked no. 1 in Canada and no 21 in the world.)

    He has a M.S. in Computer Science from the University of Illinois at Urbana-Champaign, USA (UIUC's Computer Science program is ranked in the top 5 in the world by US News and World Report.)

    He has a Bachelors in Technology from the Indian Institute of Technology, Roorkee.

    He teaches MBA-level courses in Marketing Management; MBA electives on Brand Management; Promotions Strategy; Ph.D. seminars on Game Theory and Regression Analysis.

    He has published research in global journals such as International Journal of Research in Industrial Organization; International Journal of Production Economics, Tourism Economics. Thus, he possesses a distinguished, multi-disciplinary portfolio of research and teaching credentials.