R Programming Hands-on Specialization for Data Science (Lv1)
4.2 (166 ratings)
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R Programming Hands-on Specialization for Data Science (Lv1)

An in-depth course on R language with real-world Data Science examples to supercharge your R data analysis skills
4.2 (166 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.
12,424 students enrolled
Created by Irfan Elahi
Last updated 5/2017
English
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Current price: $10 Original price: $135 Discount: 93% off
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Includes:
  • 11 hours on-demand video
  • 12 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Setup and Use Development Environment for R
  • Install and Use Packages in R
  • Learn and use Atomic Data Types in R
  • Learn and apply advanced explicit/Implicit Coercioning in R
  • Learn multiple approaches to create vectors in R
  • Understand nuances and implications in Vector Coercions
  • Understand Vector indexing principles in R
  • Understand and leverage Vectors' flatness property
  • Understand Vector Labels and Attributes and their practical use-cases
  • Learn Matrices and multiple approaches for creation
  • Learn how Matrices Dimension Property works
  • Learn advanced techniques for Matrices Indexing
  • Learn Matrices Operations and Important Functions
  • Learn the amazing use-cases of Lists
  • Learn to leverage Lists' Recursive Nature
  • Learn multiple ways to create Lists (including from other data structures)
  • Learn critical nuances in Lists Indexing, Labels and Lists Properties
  • Learn multiple approaches to create Data Frames (including from other data structures)
  • Learn Data Frames sub-setting (beginner to advanced)
  • Learn how to impute missing values in Data Frames for efficient Data Analysis
  • Learn R Control Structures (Conditional statements and loops)
  • Learn to create and use R Functions
  • Understand Web Scraping Process
  • Learn R's Apply family of functions for advanced data manipulation
  • Learn Multiple ways to perform Web Scraping in R
  • Learn how to perform Data Munging, Cleansing and Transformation in R
  • Learn HTML and Document Object Model in the context of Web Scraping
  • Learn XPath Query Language for Web Scraping
  • Learn RSelenium setup and usage for advanced Web Scraping
  • Learn Regular Expression Functions in R for advanced analysis
  • Learn advanced Data Frames techniques for efficient data analysis
  • Learn how to perform statistical analysis and visualisation to derive insights in R
View Curriculum
Requirements
  • There is only one pre-requisite: Passion and commitment to learn!
  • No prior Programming or Data Science experience needed
  • All the software/tools are open-source and available for Free!
  • A computer (Windows or Linux) with internet connection needed for hands-on exercises
Description

R is considered as lingua franca of Data Science. Candidates with expertise in R programming language are in exceedingly high demand and paid lucratively in Data Science. IEEE has repeatedly ranked R as one of the top and most popular Programming Languages. Almost every Data Science and Machine Learning job posted globally mentions the requirement for R language proficiency. All the top ranked universities like MIT have included R in their respective Data Science courses curriculum. 

With its growing community of users in Open Source space, R allows you to productively work on complex Data Analysis and Data Science projects to acquire, transform/cleanse, analyse, model and visualise data to support informed decision making. But there's one catch: R has quite a steep learning curve! 

How's this course different from so many other courses?

Many of the available training courses on R programming don't cover it its entirety. To be proficient in R for Data Science requires thorough understanding of R programming constructs, data structures and none of the available courses cover them with the comprehensiveness and depth that each topic deserves. Many courses dive straight into Machine Learning algorithms and advanced stuff without thoroughly comprehending the R programming constructs. Such approaches to teach R have a lot of drawbacks and that's where many Data Scientists struggle with in their professional careers.

Also, the real value in learning R lies in learning from professionals who are experienced, proficient and are still working in Industry on huge projects; a trait which is missing in 90% of the training courses available on Udemy and other platforms.

This is what makes this course stand-out from the rest. This course has been designed to address these and many other fallacies and uniquely teaches R in a way that you won't find anywhere else. Taught by me, an experienced Data Scientist currently working in Deloitte (World's largest consultancy firm) in Australia and has worked on a number of Data Science projects in multiple niches like Retail, Web, Telecommunication and web-sector. I have over 5 years of diverse experience of working in my own start-ups (related to Data Science and Networking), BPO and digital media consultancy firms, and in academia's Data Science Research Labs. Its a rare combination of exposure that you will hardly find in any other instructor. You will be leveraging my valuable experience to learn and specialize R. 

What you're going to learn in this course?

The course will start from the very basics of introducing Data Science, importance of R and then will gradually build your concepts. In the first segment, we'll start from setting up R development environment, R Data types, Data Structures (the building blocks of R scripts), Control Structures and Functions. 

The second segment comprises of applying your learned skills on developing industry-grade Data Science Application. You will be introduced to the mind-set and thought-process of working on Data Science Projects and Application development. The project will then focus on developing automated and robust Web Scraping bot in R. You will get the amazing opportunities to discover what multiple approaches are available and how to assess alternatives while making design decisions (something that Data Scientists do everyday). You will also be exposed to web technologies like HTML, Document Object Model, XPath, RSelenium in the context of web scraping, that will take your data analysis skills to the next level. The course will walk you through the step by step process of scraping real-life and live data from a classifieds website to analyse real-estate trends in Australia. This will involve acquiring, cleansing, munging and analyzing data using R statistical and visualisation capabilities.

Each and every topic will be thoroughly explained with real-life hands-on examples, exercises along with disseminating implications, nuances, challenges and best-practices based on my years of experience. 

What you will gain from this course will be incomparable to what's currently available out there as you will be leveraging my growing experience and exposure in Data Science. This course will position you to not only apply for Data Science jobs but will also enable you to use R for more challenging industry-grade projects/problems and ultimately it will super-charge your career.

So take the decision and enrol in this course and lets work together to make you specialize in R Programming like never before!

Who is the target audience?
  • Anyone who wants to get started or advance further in Data Science
  • Anyone who wants to develop expertise in R programming based on best-practices
  • Anyone who wants to learn how to use R for real-life challenging Data Science projects and applications
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Curriculum For This Course
87 Lectures
10:59:42
+
Introduction
3 Lectures 21:16

Why you should learn R?
10:26

What you will learn in this course?
07:29
+
R Fundamentals
4 Lectures 28:45

How to install R (console) and RStudio (IDE) to get started with R language.

Installing R (console) and RStudio (IDE)
07:26

Getting to know R - Setting Context
02:19

R Basics - Working Directory, Environment Variables and more!
13:13

R Basics - Loading and Executing R scripts from local file system
05:47

Handling Working Directory
1 question
+
R Data Types
7 Lectures 46:13
R Atomic Data Types Intro - What you must know about Numeric and Integers in R?
09:45

Complex and Character Data Types (Atomic)
05:43

Character Data Type (Atomic) + Important Data Transformation Functions (1)
04:55

Character Data Type (Atomic) + Important Data Transformation Functions (2)
03:52

Character Data Type (Atomic) + Important Data Transformation Functions (3)
07:55

Logical Data Type (Atomic) and Its known Implications
04:30

Atomic Data Types and Nuances in Coercioning (Explicit/Implicit)
09:33

Data Types Coercions
1 question
+
R Data Structure - Vectors
7 Lectures 59:11
Vectors - Creation, Homogeneity, Coercion Implications and Important Functions!
12:47

Vectors - Comparing different ways to create vectors in R!
05:36

Vectors - Understanding Indexing like never before!
10:12

Vectors - Indexing (Out of Bound scenarios) and How Pros use it!
10:25


Vectors - Labels and their Advanced Usage in Indexing
08:53

Vectors - Assigning Attributes and its use-case as Metadata
05:33

Indexing Vectors
1 question
+
R Data Structure - Matrices
8 Lectures 01:00:07
Matrices - Getting Acquainted, Creation and its operational functions!
11:21

Matrices - Creation and Implications related to its Dimensions
06:51


Matrices - Dimensions (Advanced) and Intro to Indexing
04:25

Matrices - Indexing Continued
03:46

Matrices - Advanced Indexing using DimensionNames
04:47

Matrices - Even more Advanced Indexing!
07:40

Matrices - Operations!
06:48
+
R Data Structure - Lists
8 Lectures 46:49
Lists - Getting Introduced to one of the most powerful data structures in R
06:52

Lists - Comparing with Vectors w.r.t Heterogeneity and Introducing Indexing
05:11

Lists - Comprehending their Recursive Nature in comparison with Vectors
03:49


Lists - Nuances in Determining Length in the context of Recursiveness
04:21

Lists - Nuances in Determining Length and Class of Elements
06:37

List - Advanced Indexing also using Labels
09:04

List - Comparison of Indexing ways and Implications
05:41
+
R Data Structure - Data Frames
9 Lectures 01:31:35
Data Frames - Introducing The holy grail of processing Structured Data
09:41

Data Frames - Creation and important functions for Basic Exploratory Analysis
13:40

Data Frames - More Important Functions for Basic Exploratory Analysis
09:35

Data Frames - Creation from Lists
02:18

Data Frames - Creation from Lists, Matrices and Vectors
03:28

Data Frames - Everything you need to know about Subsetting
15:43

Data Frames - Handling Missing Values like Pros!
10:27

Data Frames - Imputing Missing Values like Pros!
14:01

Data Frames - Advanced Subsetting Techniques for robust analytics
12:42
+
R Control Structures
5 Lectures 36:00
While Loops in R
05:33

For Loops in R - Intro and Practical Use-Cases
11:48

If Else Structures in R
04:45

If Else Structures in R (2)
10:25

If Else Structures in R (3)
03:29
+
Data Science Application in R - Automated Web Scraping Bot
36 Lectures 04:29:46
Web Scraping - Setting Context + Highlighting Use-Cases
13:16

Web Scraping - One Simple yet Powerful Way to do so!
12:14

Web Scraping - Use Case: Custom Churn Analysis
08:56

Use Case: Custom Churn - Performing Data Munging and Transformations
08:49

Use Case: Custom Churn - Performing Data Munging and Transformations
12:57


Web Scraping - Contextual understanding of HTML
03:17

Web Scraping - Contextual Understanding of HTML Tags
11:04

Web Scraping - How to exploit the Structure of Web Page for Efficient Scraping
06:17

Web Scraping - Contextual Understanding of HTML Document Object Model (DOM)
06:17

Web Scraping on Steroids - XPath in R!
08:29

Web Scraping on Steroids - XPath in R (2)
01:00

Web Scraping using XPath - Programmatic Extraction of Data from HTML Tags
05:19

Web Scraping using XPath - Programmatic Extraction of Data from HTML Tags (2)
03:11

Automating Web Scraping - RSelenium!
05:21

Automated Web Scraping - Contextual Understanding of Selenium Components
06:10

Automated Web Scraping - installing RSelenium in R
05:01

Automated Web Scraping - Initialising RSelenium Server
05:40

Automated Web Scraping - Connecting to RSelenium Server using Reference Class
09:07

Automated Web Scraping - Navigating and Sending Key Strokes in Web Pages
11:13

Web Scraping Use Case Context Setting
06:20

Web Scraping Pipeline - Deep dive of workflow pattern
07:42

Systematic analysis of website for efficient Scraping
05:55

Installing and Loading RSelenium
04:36

Starting Selenium Server - The right way!
05:40

Handling RSelenium's Driver Issues
01:48

Launching Selenium Server jar with correct driver settings (part 2)
03:50

Web Scraper Program Initialisation and Remote Driver Object Instantiation
06:34

Navigating web pages using RSelenium and Using Xpath for data extraction
10:01

Using R's Apply Family of Functions for Data Extraction from RSelenium Objects
05:49

Advanced Data Munging using R Regex and String Processing Functions
08:13

Advanced Data Munging using R Regex and String processing functions (II)
03:39

Advanced Data Munging - Discretizing Continuous Values
09:07

Advanced Data Frames Manipulation
11:54

Orchestrating Automation of Web Scraping Routine
07:41

Advanced Statistical Analysis and Visualisation for Informed Decision Making
16:01
About the Instructor
Irfan Elahi
4.2 Average rating
166 Reviews
12,424 Students
1 Course
Data Scientist in World's Largest Consultancy Firm

My name is Irfan Elahi and I am currently working as Data Scientist in World's largest consultancy firm: Deloitte Touche Tohmatsu in Melbourne, Australia. 

I have over 5 years of multi-disciplinary experience in Data Science and Machine Learning and have worked in a number of verticals like consultancy firms, my own start-ups and academia research lab. Over the years I have worked on a number of Data Science and Machine Learning projects in different niches like Telecommunication, Retail, Web, Public Sector and Energy with the goal to enable businesses to derive immense value from their data-assets.

The genesis of data science is that it is cross-disciplinary. My diverse exposure, engineering background, intellectual curiosity combined with my passion to teach are the secret ingredients that make my training courses uniquely stand-out from the rest. I believe in perfection and in-depth coverage of concepts in pedagogy. Being a self-taught Data Scientist, I possess strong empathy as I've seen the struggle to advance in Data Science. Also, blessed with the opportunity to work in multiple and huge-scale projects with the industry experts and Data Science vendors (e.g. IBM, Cloudera, Microsoft, MapR, HortonWorks), I discover and employ the best practices in this field in my professional career and believe in sharing those with my students.

Apart from that, I love public-speaking and have delivered multiple talks about Data Science in different universities and seminars across the globe.