R Programming Primer for Data Science & Analytics
R Essentials for Data Sciencetakes students currently working with Excel (or SAS or another data tool) for numerical analysis and want to get started using more powerful Open Source environments including 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.
In this course we present common scenarios that are encountered in analysis and present practical solutions. Some attention is paid to data science theory including AI grouping theory. A discussion of using R with libraries are included and prepares the user for using Spark/R (and SparklyR).
NOTE: For deeper hands-on coverage or R Programming for Data Science please consider TT6682 R Programming for Data Science & Analytics, a variation of this course with more advanced hands-on and concepts.
This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on R and related tools. Working in a hands-on learning environment, led by our expert practitioner, students will learn R and its ecosystem, and where it’s a better a tool than Excel.
Students will explore:
- Moving from Excel to R
- R Basics
- Reading and Writing Files
- Multiple Dimensions
- Overview of R in Data Science
1. From Excel to R
- Common problems with Excel
- The R Environment
- Hello, R
2. R Basics
- Simple Math with R
- Working with Vectors
- Comments and Code Structure
- Using Packages
- Vector Properties
- Creating, Combining, and Iterating
- Passing and Returning Vectors in Functions
- Logical Vectors
4. Reading and Writing Files
- Text Manipulation
- Working with Dates
- Date Formats and formatting
- Time Manipulation and Operations
6. Multiple Dimensions
- Adding a second dimension
- Indices and named rows and columns in a Matrix
- Matrix calculation
- n-Dimensional Arrays
- Data Frames
7. Overview of R in Data Science
- AI Grouping Theory
- Linear Regression
- Logistic Regression
- Elastic Net
8. Next Steps
- Powerful Data through Visualization: Communicating the Message
- R in Spark
Course delivery details
Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive related (as applicable) project files, code files, data sets and solutions required for the hands-on work.
Classroom Setup Made Simple: Our dedicated tech team will work with you to ensure your classroom and lab environment is setup, tested and ready to go well in advance of the course delivery date, ensuring a smooth start to class and seamless hands-on experience for your students. We offer several flexible student machine setup options including guided manual set up for simple installation directly on student machines, or cloud based / remote hosted lab solutions where students can log in to a complete separate lab environment minus any installations, or we can supply complete turn-key, pre-loaded equipment to bring ready-to-go student machines to your facility. Please inquire for details.
- Price: $1,895.00
- Discounted Price: $1,231.75
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Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...