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Introduction to R
Self-Paced
Learning Style
Course
Learning Style
Beginner
Difficulty
5 Hours
Course Duration
Course Info
Certificate
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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.
Course Information
About this course:
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.
Course Objective:
- R Language and Mathematics
- How to work with R Vectors
- How to read and write data from files, and how to categorize data in factors
- How to work with Dates and perform Date math
- How to work with multiple dimensions and DataFrame essentials
- Essential Data Science and how to use R with it
- Visualization in R
- How R can be used in Spark
Audience:
- Beginner level data analysts
- Data scientists who need to learn the essentials of how to program in R
- Graduating students aspiring to become data scientists
- Analysts working with MS Excel, Matlab, Mathematica or SAS
Prerequisite:
- The R programming language course requires the students to have functional knowledge and basic skills of SQL and databases.
- In addition to this, no prior knowledge of R or any other programming language is required.
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Outline
More Information
| 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 |
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Course Expert:
Tom Robertson
(Data Science Enthusiast)
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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