Learn By Example: Statistics and Data Science in R
This training uses the R programming language as a guide to statistics and data science. This addresses both the theoretical dimensions of Statistical Concepts and the application of R in reality.
Self-Paced
Learning Style
Course
Learning Style
Beginner
Difficulty
9 Hours
Course Duration
Course Info
Certificate
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9:00 AM - 5:00 PM CST
Duration - 0 Days -
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This training uses the R programming language as a guide to statistics and data science. This addresses both the theoretical dimensions of Statistical Concepts and the application of R in reality.
Course Information
About the course:
This training uses the R programming language as a guide to statistics and data science. This addresses both the theoretical dimensions of Statistical Concepts and the application of R in reality.
Audience:
- Engineers who wish to explore basic statistics and lay the basis for a Data Science career
- Business professionals or MBA graduates looking to transition into a substantially quantitative position
- People who have dealt mainly with software like Excel and want to know how to use R to evaluate statistics
- Analytics specialists who have worked primarily in Descriptive analytics and are involved in being modelers or data scientists
Prerequisite:
- No requirements: We begin from fundamentals and discuss everything you need to learn. As part of the program, we will install RStudio and R, and use it for most of the cases. Excel is being used for one of the instances and is believed to have a basic understanding of excel.
Outline
More Information
| Subjects | Big Data |
|---|---|
| Lab Access | No |
| Technology | Microsoft |
| Learning Style | Self-Paced Learning |
| Learning Type | Course |
| Difficulty | Beginner |
| Course Duration | 9 Hours |
| Language | English |
| VPA Discount | VPA Discount |
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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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