[Udemy] - R Programming Hands-on Specialization for Data Science (Lv1) [Getnewcourses]

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1 GB
  • Uploaded Byabia9220
  • Downloads102
  • Last checkedOct. 31st '19
  • Date uploadedOct. 30th '19
  • Seeders 6
  • Leechers7
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Infohash : 8B296C887C3AD0034B221A464609FC41C359C99E

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



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 the creation

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Requirements
There is only one pre-requisite: Passion and commitment to learning!
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 a 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, analyze, model and visualize data to support informed decision making. But there’s one catch: R has quite a steep learning curve!

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How’s this course different from so many other courses?
Many of the available training courses on R programming don’t cover its entirety. To be proficient in R for Data Science requires a 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 within 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.

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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 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 in R.

What you’re going to learn in this course?
The course will start from the very basics of introducing Data Science, the importance of R and then will gradually build your concepts. In the first segment, we’ll start from setting up the R development environment, R Data types, Data Structures (the building blocks of R scripts), Control Structures and Functions.

The second segment comprises applying your learned skills in 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 every day). You will also be exposed to web technologies like HTML, Document Object Model, XPath, RSelenium in the context of web scraping, which 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 analyze real-estate trends in Australia. This will involve acquiring, cleansing, munging and analyzing data using R statistical and visualization 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 enroll 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

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Files:

R Programming Hands-on Specialization for Data Science (Lv1) 9. Data Science Application in R - Automated Web Scraping Bot
  • 26.1 gecko_driver_download_link.txt.txt (0.0 KB)
  • 12. Web Scraping on Steroids - XPath in R (2).vtt (1.0 KB)
  • 26. Handling RSelenium's Driver Issues.vtt (1.8 KB)
  • 7. Web Scraping - Contextual understanding of HTML.vtt (3.5 KB)
  • 24. Installing and Loading RSelenium.vtt (5.1 KB)
  • 15. Automating Web Scraping - RSelenium!.vtt (6.0 KB)
  • 17. Automated Web Scraping - installing RSelenium in R.vtt (6.5 KB)
  • 23. Systematic analysis of website for efficient Scraping.vtt (6.6 KB)
  • 35.1 final_script_web_scraping.txt.txt (6.7 KB)
  • 25. Starting Selenium Server - The right way!.vtt (6.9 KB)
  • 18. Automated Web Scraping - Initialising RSelenium Server.vtt (7.1 KB)
  • 21. Web Scraping Use Case Context Setting.vtt (7.3 KB)
  • 35. Orchestrating Automation of Web Scraping Routine.vtt (8.7 KB)
  • 11. Web Scraping on Steroids - XPath in R!.vtt (9.2 KB)
  • 22. Web Scraping Pipeline - Deep dive of workflow pattern.vtt (9.3 KB)
  • 3. Web Scraping - Use Case Custom Churn Analysis.vtt (10.4 KB)
  • 33. Advanced Data Munging - Discretizing Continuous Values.vtt (11.1 KB)
  • 8. Web Scraping - Contextual Understanding of HTML Tags.vtt (12.8 KB)
  • 6. Use Case Custom Churn - Performing Data Cleansing.vtt (13.0 KB)
  • 34. Advanced Data Frames Manipulation.vtt (13.6 KB)
  • 2. Web Scraping - One Simple yet Powerful Way to do so!.vtt (14.5 KB)
  • 1. Web Scraping - Setting Context + Highlighting Use-Cases.vtt (15.3 KB)
  • 1.1 r_web_scraping.pdf.pdf (145.1 KB)
  • 7.1 r_html.pdf.pdf (156.6 KB)
  • 11.1 r_xpath.pdf.pdf (223.5 KB)
  • 15.1 r_selenium.pdf.pdf (223.8 KB)
  • 12. Web Scraping on Steroids - XPath in R (2).mp4 (2.3 MB)
  • 26. Handling RSelenium's Driver Issues.mp4 (3.6 MB)
  • 15. Automating Web Scraping - RSelenium!.mp4 (8.6 MB)
  • 17. Automated Web Scraping - installing RSelenium in R.mp4 (9.1 MB)
  • 24. Installing and Loading RSelenium.mp4 (9.5 MB)
  • 7. Web Scraping - Contextual understanding of HTML.mp4 (10.5 MB)
  • 21. Web Scraping Use Case Context Setting.mp4 (10.6 MB)
  • 18. Automated Web Scraping - Initialising RSelenium Server.mp4 (10.7 MB)
  • 25. Starting Selenium Server - The right way!.mp4 (13.0 MB)
  • 22. Web Scraping Pipeline - Deep dive of workflow pattern.mp4 (13.8 MB)
  • 11. Web Scraping on Steroids - XPath in R!.mp4 (17.6 MB)
  • 8. Web Scraping - Contextual Understanding of HTML Tags.mp4 (20.9 MB)
  • 35. Orchestrating Automation of Web Scraping Routine.mp4 (21.4 MB)
  • 23. Systematic analysis of website for efficient Scraping.mp4 (22.1 MB)
  • 6. Use Case Custom Churn - Performing Data Cleansing.mp4 (23.8 MB)
  • 1. Web Scraping - Setting Context + Highlighting Use-Cases.mp4 (24.0 MB)
  • 3. Web Scraping - Use Case Custom Churn Analysis.mp4 (25.0 MB)
  • 33. Advanced Data Munging - Discretizing Continuous Values.mp4 (25.6 MB)
  • 34. Advanced Data Frames Manipulation.mp4 (33.7 MB)
  • 2. Web Scraping - One Simple yet Powerful Way to do so!.mp4 (33.7 MB)
2. R Fundamentals
  • 5. Handling Working Directory.html (0.1 KB)
  • 2. Getting to know R - Setting Context.vtt (2.7 KB)
  • 4. R Basics - Loading and Executing R scripts from local file system.vtt (6.7 KB)
  • 1. Installing R (console) and RStudio (IDE).vtt (9.4 KB)
  • 3. R Basics - Working Directory, Environment Variables and more!.vtt (15.5 KB)
  • 1.1 1_R_intro.pdf.pdf (152.7 KB)
  • 2. Getting to know R - Setting Context.mp4 (3.6 MB)
  • 4. R Basics - Loading and Executing R scripts from local file system.mp4 (11.7 MB)
  • 1. Installing R (console) and RStudio (IDE).mp4 (13.6 MB)
  • 3. R Basics - Working Directory, Environment Variables and more!.mp4 (27.5 MB)
4. R Data Structure - Vectors
  • 8. Indexing Vectors.html (0.1 KB)
  • 7. Vectors - Assigning Attributes and its use-case as Metadata.vtt (6.3 KB)
  • 5. Vectors - Flatness property and its critical implications in Indexing!.vtt (6.6 KB)
  • 2. Vectors - Comparing different ways to create vectors in R!.vtt (6.7 KB)
  • 6. Vectors - Labels and their Advanced Usage in Indexing.vtt (9.2 KB)
  • 3. Vectors - Understanding Indexing like never before!.vtt (10.9 KB)
  • 4. Vectors - Indexing (Out of Bound scenarios) and How Pros use it!.vtt (11.9 KB)
  • 1. Vectors - Creation, Homogeneity, Coercion Implications and Important Functions!.vtt (14.7 KB)
  • 1.1 r_vectors.pdf.pdf (135.5 KB)
  • 7. Vectors - Assigning Attributes and its use-case as Metadata.mp4 (10.6 MB)
  • 5. Vectors - Flatness property and its critical implications in Indexing!.mp4 (10.8 MB)
  • 2. Vectors - Comparing different ways to create vectors in R!.mp4 (11.2 MB)
  • 6. Vectors - Labels and their Advanced Usage in Indexing.mp4 (16.6 MB)
  • 4. Vectors - Indexing (Out of Bound scenarios) and How Pros use it!.mp4 (19.3 MB)
  • 3. Vectors - Understanding Indexing like never before!.mp4 (19.7 MB)
  • 1. Vectors - Creation, Homogeneity, Coercion Implications and Important Functions!.mp4 (23.5 MB)
3. R Data Types
  • 8. Data Types Coercions.html (0.1 KB)
  • 4. Character Data Type (Atomic) + Important Data Transformation Functions (2).vtt (4.3 KB)
  • 6. Logical Data Type (Atomic) and Its known Implications.vtt (5.3 KB)
  • 3. Character Data Type (Atomic) + Important Data Transformation Functions (1).vtt (5.9 KB)
  • 2. Complex and Character Data Types (Atomic).vtt (6.7 KB)
  • 5. Character Data Type (Atomic) + Important Data Transformation Functions (3).vtt (8.7 KB)
  • 7. Atomic Data Types and Nuances in Coercioning (ExplicitImplicit).vtt (10.7 KB)
  • 1. R Atomic Data Types Intro - What you must know about Numeric and Integers in R.vtt (11.0 KB)
  • 1.1 r_data_types_1.pdf.pdf (188.7 KB)
  • 4. Character Data Type (Atomic) + Important Data Transformation Functions (2).mp4 (6.9 MB)
  • 6. Logical Data Type (Atomic) and Its known Implications.mp4 (8.3 MB)
  • 3. Character Data Type (Atomic) + Important Data Transformation Functions (1).mp4 (8.5 MB)
  • 2. Complex and Character Data Types (Atomic).mp4 (9.6 MB)
  • 5. Character Data Type (Atomic) + Important Data Transformation Functions (3).mp4 (13.8 MB)
  • 7. Atomic Data Types and Nuances in Coercioning (ExplicitImplicit).mp4 (16.1 MB)
  • 1. R Atomic Data Types Intro - What you must

Code:

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