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30-Day Money-Back Guarantee

This course includes:

  • 11 hours on-demand video
  • 12 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science R

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
Rating: 3.4 out of 53.4 (524 ratings)
21,426 students
Created by Irfan Elahi
Last updated 5/2017
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

9 sections • 87 lectures • 10h 59m total length

  • Preview03:21
  • Why you should learn R?
    10:26
  • What you will learn in this course?
    07:29

  • 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 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

  • 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
  • Preview05:45
  • 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

  • Matrices - Getting Acquainted, Creation and its operational functions!
    11:21
  • Matrices - Creation and Implications related to its Dimensions
    06:51
  • Preview14:29
  • 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

  • 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
  • Preview05:14
  • 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

  • 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

  • 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

  • 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
  • Preview11:18
  • 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

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 this course is for:

  • 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

Featured review

Mashrur Arafin Ayon
Mashrur Arafin Ayon
9 courses
2 reviews
Rating: 5.0 out of 5a year ago
The Course was very helpful. For a beginner like me who is having high expectation on Data Science and R, do consider taking this course. It starts from the basic, then gives you a solid foundation and last but not the least provides a hand on practical experience on how to use the knowledge provided. Overall its a mixture of basic knowledge on R and its application in the web.

Instructor

Irfan Elahi
Data Scientist in the world's largest consultancy firm
Irfan Elahi
  • 3.6 Instructor Rating
  • 1,042 Reviews
  • 30,536 Students
  • 2 Courses

A full stack scalable analytics specialist, working in the world's largest consultancy firm in Australia, with a growing portfolio of successful projects delivering substantial impact and value in multiple capacities across telecom, retail, energy and health-care sectors.


Additionally:

• Artificial Intelligence (AI) stream-lead in Deloitte Australia's Azure Enablement Initiative

• Member of Deloitte Australia's ClearLight initiative managing AWS and Azure platform for enablement and assets prototyping

• Trainer of Deloitte's internal Data Science training program

• Author of "Scala Programming for Big Data Analytics" book published by Apress

• Technical reviewer of "Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark" book published by Apress

• Instructor of Apache Spark and R Programming courses on Udemy with thousands of students enrolled from all around the world

• Designated author of the largest Data Science publication (Towards Data Science) on Medium

• Speaker at DataWorks Summit in 2017 in Sydney on in-memory Big Data Technologies

• Speaker in Data Analytics Explained meetup and in multiple universities all around the world

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