Learning Style
Learning Style
Difficulty
Course Duration
The Introduction to R is a primary course for aspiring data scientists who are currently working with Microsoft Excel, Matlab, Mathematica or SAS for numerical analysis of large sets of data. The course enables the candidates in using more powerful OpenSource environments especially the R programming language. R is a functional programming environment employed by many data analysts and data scientists, easily accessible to non-programmers and naturally extending a skill set that is common to data analysts and data scientists. It's the perfect tool for when the one has a statistical, numerical, or probabilities-based problem based on real data and they've pushed those tools past their limits. This fundamental course covers all the necessary topics required to kick start the candidates in working with R programming language.
This introductory course provides a complete coverage of the fundamentals of R programming language, including its uses, benefits and applications. The students who enroll in this course will move one step closer to become accomplished data scientists. The course covers the umbrella of technologies that are on the leading edge of data science development focused on R and related tools.
| Subjects | App Development, IT Ops & Management |
|---|---|
| Lab Access | No |
| Technology | Microsoft |
| Learning Style | Self-Paced Learning |
| Learning Type | Course |
| Difficulty | Beginner |
| Course Duration | 5 Hours |
| Language | English |
| VPA Discount | VPA Discount |
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.
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