An in-depth course with hands-on real-world Data Science use-case examples to supercharge your data analysis skills.
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
R is considered as lingua franca of Data Science. Candidates with expertise in this 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 don’t cover R programming in 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-practice
Course Published By Irfan Elahi (Average rating- 4.9/5, Total Ratings- 3 )
Short Biography of Instructor:
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.
Free R Programming Hands-on Specialization for Data Science (Lv1) course