R: Complete Data Analysis Solutions
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R: Complete Data Analysis Solutions

Learn by doing - solve real-world data analysis problems using the most popular R packages
3.0 (5 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.
186 students enrolled
Created by Packt Publishing
Last updated 2/2017
English
Current price: $10 Original price: $200 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 4 hours on-demand video
  • 30 Articles
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Extract, transform, and load data from heterogeneous sources
  • Understand how easily R can confront probability and statistics problems
  • Get simple R instructions to quickly organize and manipulate large datasets
  • Predict user purchase behavior by adopting a classification approach
  • Implement data mining techniques to discover items that are frequently purchased together
  • Group similar text documents by using various clustering methods
View Curriculum
Requirements
  • You are expected to know basics of R programming. You should have R installed on your system and your system should be connected to the Internet. That’s all really!
Description

If you are looking for that one course that includes everything about data analysis with R, this is it. Let’s get on this data analysis journey together.

This course is a blend of text, videos, code examples, and assessments, which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of solving data analysis problems with R.

The R language is a powerful open source functional programming language. R is becoming the go-to tool for data scientists and analysts. Its growing popularity is due to its open source nature and extensive development community. R is increasingly being used by experienced data science professionals instead of Python and it will remain the top choice for data scientists in 2017. Big companies continue to use R for their data science needs and this course will make you ready for when these opportunities come your way.

This course has been prepared using extensive research and curation skills. Each section adds to the skills learned and helps us to achieve mastery of data analysis. Every section is modular and can be used as a standalone resource.

This course has been designed to include topics on every possible requirement from a data scientist and it does so in a step-by-step and practical manner. This course covers step-by-step and practical solutions to data analysis using R. It covers every required topic and also adds an introduction to machine learning.

We will start off with learning how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation will be provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We will then understand how easily R can confront probability and statistics problems and look at R instructions to quickly organize and manipulate large datasets. We will then learn to predict user purchase behavior by adopting a classification approach and implement data mining techniques to discover items that are frequently purchased together. Finally, we will offer insight into time series analysis on financial data, after which there will be detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.

This course has been authored by some of the best in their fields:

Yu-Wei, Chiu (David Chiu)

Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a start-up company that mainly focuses on providing big data and machine learning products. He specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences.

Selva Prabhakaran

Selva Prabhakaran is a data scientist with a large E-commerce organization. In 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.

Tony Fischetti

Tony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems.

Viswa Viswanathan

Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. In addition to teaching at the university, Viswa has conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education.

Shanthi Viswanathan

Shanthi Viswanathan is an experienced technologist who as a consultant, has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling.

Romeo Kienzler

Romeo Kienzler is the Chief Data Scientist of the IBM Watson IoT Division and working as an Advisory Architect helping client worldwide to solve their data analysis problems. His current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J.


This course is a blend of text, videos, and assessments, all packaged together keeping your journey in mind. It combines some of the best that Packt has to offer in one complete package. It includes content from the following Packt products:

  • R for Data Science Cookbook by Yu-Wei, Chiu (David Chiu)
  • R for Data Science Solutions [video] by Yu-Wei, Chiu (David Chiu)
  • Mastering R Programming [video] by Selva Prabhakaran
  • Data Analysis with R by Tony Fischetti
  • R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan
  • Learning Data Mining with R [video] by Romeo Kienzler


Who is the target audience?
  • This course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data analysis for the first time. Those already familiar with the basics of R, but want to learn to efficiently analyze real-world data problems will also find this course a match for their needs.
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Curriculum For This Course
109 Lectures
05:21:47
+
Data Extracting, Transforming, and Loading
9 Lectures 26:10


Reading and writing CSV files
01:10

Scanning text files
02:18

Working with Excel files
01:52

Reading data from databases
04:00

Scraping web data
04:59

Accessing Facebook data
03:21

Working with Twitter
01:56

Test Your Knowledge
2 questions
+
Data Preprocessing and Preparation
10 Lectures 26:18

Converting data types
02:31

Working with the date format
02:47

Adding new records
02:06

Filtering data
03:26

Dropping data
01:39

Merging and sorting data
03:57

Reshaping data
02:39

Detecting missing data
02:31

Imputing missing data
02:40

Test Your Knowledge
2 questions
+
Data Manipulation
13 Lectures 28:08

Managing data with a data.table
05:10

Performing fast aggregation with a data.table
01:59

Merging large datasets with a data.table
02:34

Subsetting and slicing data with dplyr
02:04

Sampling data with dplyr
01:21

Selecting columns with dplyr
02:22

Chaining operations in dplyr
02:04

Arranging rows with dplyr
01:16

Eliminating duplicated rows with dplyr
01:08

Adding new columns with dplyr
01:05

Summarizing data with dplyr
01:09

Merging data with dplyr
01:37

Test Your Knowledge
2 questions
+
Simulation from Probability Distributions
9 Lectures 21:32
Generating random samples
02:25

Understanding uniform distributions
01:35

Generating binomial random variates
02:27

Generating Poisson random variates
02:03

Sampling from a normal distribution
05:26

Sampling from a chi-squared distribution
01:51

Understanding Student's t-distribution
01:53

Sampling from a dataset
01:48

Simulating the stochastic process
02:04

Test Your Knowledge
2 questions
+
Statistical Inference in R
9 Lectures 23:45
Getting confidence intervals
05:06

Performing Z-tests
03:07

Performing student's T-tests
02:11

Conducting exact binomial tests
02:12

Performing Kolmogorov-Smirnov tests
02:05

Working with the Pearson's chi-squared tests
01:32

Understanding the Wilcoxon Rank Sum and Signed Rank tests
01:42

Conducting one-way ANOVA
03:13

Performing two-way ANOVA
02:36

Test Your Knowledge
2 questions
+
Rule and Pattern Mining with R
8 Lectures 22:00
Transforming data into transactions
04:25

Displaying transactions and associations
02:25

Mining associations with the Apriori rule
03:36

Pruning redundant rules
02:02

Visualizing association rules
02:11

Mining frequent itemsets with Eclat
02:31

Creating transactions with temporal information
02:17

Mining frequent sequential patterns with cSPADE
02:32

Test Your Knowledge
2 questions
+
Time Series Mining with R
9 Lectures 26:23
Creating time series data
04:03

Plotting a time series object
02:03

Decomposing a time series
02:28

Smoothing a time series
04:04

Forecasting a time series
03:25

Selecting an ARIMA model
02:55

Creating an ARIMA model
02:00

Forecasting with an ARIMA model
01:53

Predicting stock prices with an ARIMA model
03:31

Test Your Knowledge
2 questions
+
Text Analytics In-depth
6 Lectures 36:35
Scraping web pages and processing texts
08:19

Corpus, TDM, TF-IDF, and word cloud
08:58

Cosine similarity and Latent Semantic Analysis
07:03

Extracting topics with Latent Dirichlet Allocation
05:01

Sentiment scoring with tidytext and Syuzhet
04:06

Classifying texts with RTextTools
03:08

Test Your Knowledge
2 questions
+
Sources of Data
5 Lectures 19:39
Relational databases
04:35

Using JSON
07:12

XML
05:53

Other data formats
00:58

Online repositories
00:59

Test Your Knowledge
2 questions
+
Let's Do A Project: Social Network Analysis
4 Lectures 24:54
Downloading social network data using public APIs
05:59

Creating adjacency matrices and edge lists
04:19

Plotting social network data
08:46

Computing important network metrics
05:49

Test Your Knowledge
2 questions
3 More Sections
About the Instructor
Packt Publishing
3.9 Average rating
7,219 Reviews
51,672 Students
616 Courses
Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.