Data Science using R
This course helps you to program Programming in R And R Studio. It covers various topics like Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2. Data science is related to data mining and big data. Learn how to use R and RStudio to turn raw data into insight, knowledge, and understanding. This course introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Because it focuses not only on small coding examples, but also on real-world projects and use cases.
Data Science using R
This course helps you to program Programming in R And R Studio. It covers various topics like Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2. Data science is related to data mining and big data. Learn how to use R and RStudio to turn raw data into insight, knowledge, and understanding. This course introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Because it focuses not only on small coding examples, but also on real-world projects and use cases.
Course Introduction
This 90hrs (Lectures + hands-on Lab) Data Science training is targeted to engineers and technical personnel involved in data science and analysis of large set of data. Data science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data. This course introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Data Science is a lab-intensive course and objectives are accomplished mainly through hands on learning. The key to a high success rate is based on the program’s objectives as follows:
- Course contents are based on industry requirement
- Dedicated Monitoring to evaluate and report candidates progress
- Extensive hands-on lab exercises
- Industry acclaimed, experienced and certified instructors
Course Highlights
- Project manager can be assigned to track candidates’ performance
- Curriculum based on industry needs.
- This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises
- Facility of Lab on cloud available (based on booking)
- Courseware includes reference material to maximize learning.
- Assignments and test to ensure concept absorption.
- Courseware includes reference material to maximize learning.
- Assignments and test to ensure concept absorption.
- Repeating of lectures allowed (based on seat availability)
Course Objectives
- Learn how to code in R
- Learn how to use R
- Learn the core principles of programming
- Take you from a SQL beginner to an SQL developer
- Learn how to create vectors in R
- Learn how to create variables
- “Start with why” something works in R, not just “how”
- Learn how to build and use matrices in R
- Learn the matrix() function, learn rbind() and cbind()
- Learn how to install packages in R
- Learn how to customize R studio to suit your preferences
- Understand the law of large numbers
- Understand the normal distribution
- Practice working with statistical data in R
- Deal with date-times in R
- Practice working with financial data in R
- Ability to think about code and implements
- Understand how and why code works the way it does
- Practice working with sports data in R
- Perform Data Preparation in R
- Identify missing records in data frames
- Locate missing data in your data frames
- Apply the median imputation method to replace missing records
- Apply the factual analysis method to replace missing records
- Explain why NA is a third type of logical constant
- Create a time series plot in R
- Add your own functions into apply statements
- Nest apply(), lapply() and sapply() functions within each other
Course Topics
This course is created to impart knowledge and skills related to data science & analytics, data prep and visualization. This course will help candidates to Learn Programming In R And R Studio. Course content covers all major modules like Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2, Statistical Analysis in Business.
Core Programming Principles:
- Describe buzzwords R
- Variable and Types of Variables
- Using Variables
- Local Variables and Operators
- Control statements
- “ while ” Loop
- “ for ” Loop
- Conditional Statements
- If- statement
- Law Of Large Number
- Problem Solving
Fundamentals Of R:
- What is a Vector?
- Vector Creation
- Use of [] Brackets
- Operation on Vectors
- Functions in R
- Packages in R
- Statement Analysis
- Real world example
Matrices:
- Analyzing Trends
- matrices
- Creation of Matrix
- Naming Dimensions in R
- Use Of Colnames() and Rownames
- Various Operation Matrix
- Visualizing with Matplot()
- Subsetting
- Function Declaration in R
- Data Insights
Data Frames:
- Analysis of Raw Data
- Importing Data into R
- Exploring your Dataset
- Uses of $
- Operation with Data Frame
- Filtering a Data Frame
- Introduction to Qplot
- Visualization using Qplot
- Building DataFrames
- Merging DataFrames
- Visualizing with Qplot
Visualization With GGPlot:
- Movie ratings data analysis
- Fundamentals of graphics – GGPlot
- What is factor
- Plotting with layers
- Overriding Aesthetics
- Mapping vs Setting
- Histogram and Density Charts
- Introduction to Layer Tips
- Statistical Transformations
- Use of Facets
- Play with Coordinates
- Play by adding Themes
Data Preparation:
- RstProject: Financial Review
- Import Data into R
- gsub() vs sub()
- Dealing with missing data
- What is NA?
- How to locate missing data
- Which() for non missing data
- Data Filtering
- Removing records with missing data
- Resetting the dataframe index
- Replacing missing data
- Visualizing Results
Lists in R:
- RstProject: Machine Utilization
- Import data into R
- D/T handling in R
- Naming components of a list
- Extracting components lists: [] vs [[]] vs $
- Adding and deleting components
- Subsetting a list
- Creating a time-series plot
Apply- Family Of Function:
- RstProject: Weather Patterns
- Import data into R
- What is Apply family?
- Using Apply()
- Creation of apply () with Loops
- Use of predefined functions
- Adding your own Functions
- Nesting apply() functions
- Introduction to which.max() and which.min()
Class Room Project:
- Financial Review
- OS Utilization
- Pattern Analysis
- Statement Analysis
Introduction TO Data Science Tools:
- Tableau
- Alteryx
Lab Topics
Not Available
Virtual Classroom
- Instructor led online training is an ideal vehicle for delivering training to individuals anywhere in the world at any time.
- This innovative approach presents live content with instructor delivering the training online.
- Candidates will be performing labs remotely on our labs on cloud in presence of an online instructor.
- Rstforum uses microsoft lync engine to deliver instructor led online training.
- Advances in computer network technology, improvements in bandwidth, interactions, chat and conferencing, and realtime audio and video offers unparalleled training opportunities.
- Instructor led online training can helps today’s busy professionals to perform their jobs and upgrade knowledge by integrating self-paced instructor led online training in their daily routines.
Miscellaneous
- Minimum batch size required for batch is 10 participants in the this course.
- The RST Forum reserves the right to cancel/postpone the class.
- Course schedule will be provided before commencement of the course.
- Certificate of participation will be awarded to participants with a minimum 90% attendance.
- All attendees are to observe the Copyright Law on intellectual properties such as software and courseware from respective vendors.
- The RST Forum reserves the right to include external participants in the program either for the entire course or individual courses.
- The RST Forum reserves the right to change/alter the sequence of courses. RST FORUM published Book would be given at 50% discounted rate to the forum students.