Description
What You Will Learn
1. Basics of R
Install R studio. Explore R language fundamentals, including basic syntax, variables, and types
2. Data Structures
Learn about data structures that R can handle. Create and manipulate regular R lists, tuple etc.
3. Conditional Statements
Learn about control and loops statements
4. Object Oriented Programming
Learn to write user defined functions and object oriented way of writing classes and objects.
5. Functions
Use functions and import packages
6. Querying and Filtering
Learn to apply data processing functions in R for describing and performing operations on data.
7. Summarizing
Learn to Summarize the data which helps you to take necessary steps for further analysis
Prerequisites
While there are no prerequisites, participants would benefit if they have elementary programming knowledge.
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Who should Attend?
- Those interested in the field of data science
- Those who want to learn R programming from scratch
- Those looking for a robust, structured learning program on R
- Software or Data Engineers interested in learning R Programming
KnowledgeHut Experience
Instructor-led Live Classroom
Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Curriculum Designed by Experts
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
Learn through Doing
Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied in the real world.
Mentored by Industry Leaders
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Advance from the Basics
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Code Reviews by Professionals
Get reviews and feedback on your final projects from professional developers.
Curriculum
Learning Objectives:
Get an idea of what R is and why it is so popular among Data Scientists.
Topics Covered:
- What is R?
- Why is it in demand?
Hands-on: No hands-on
Learning Objectives:
In this module you will learn to install R and its components, install and load R libraries and learn about the frequently used libraries.
Topics Covered:
- Installation of R - step by step
- Installing Libraries
- Getting to know important Libraries
Hands-on:
Know how to install R, R Studio and other libraries.
Learning Objectives:
Learn about data structures in R.
Topics Covered:
- List
- Vectors
- Arrays
- Matrices
- Factors
- String
- Data Frames
Hands-on:
Write R Code to understand and implement R Data Structures.
Learning Objectives:
Learn all about loops and control statements in R.
Topics Covered:
- For Loop
- While Loop
- Break Statement
- Next Statements
- Repeat Statement
- if, if…else Statements
- Switch Statement
Hands-on:
Write R Code to implement loop and control structures in R.
Learning Objectives:
Learn how to write custom functions, nested functions and functions with arguments.
Topics Covered:
- Writing your own functions (UDF)
- Calling R Functions
- Nested Function Calls in R
- Functions with Arguments
- Calling R Functions by passing Arguments
Hands-on:
Write R Code to create your own custom functions without or with arguments. Know how to call them by passing arguments wherever required
Learning Objectives:
Learn all about loop functions available in R which are efficient and can be written with a single command.
Topics Covered:
- apply
- lapply
- sapply
- mapply
- Tapply
Hands-on:
Write R Code to implement various types of apply functions and understand their usage.
Learning Objectives:
Learn all about string manipulations and regular expressions. The functions can be extremely useful for text or unstructured data manipulations.
Topics Covered:
- stringr()
- grep() & grepl()
- regexpr() & gregexpr()
- regexec()
- sub() & gsub()
Hands-on:
Write R Code for string manipulation and handle regular expression.
Learning Objectives:
Learn how to import data from various sources in R and how to write files from R. Also learn how to connect to various databases from R.
Topics Covered:
- Reading data files in R
- Reading data files from other Statistical Software
- Writing files in R
- Connecting to Databases from R
- Data Manipulation & Analysis
Hands-on:
Write R Code to read and write data from/to R. Read data not only from CSV files but also using direct connection to various databases.
Learning Objectives:
Learn how to apply various data processing functions in R. These operations can be useful to describe data and perform certain operations on it. This will help you to take necessary steps for further analysis.
Topics Covered:
- Pipe operator for data processing
- Using the dplyr verbs
- Using the customized function within the dplyr verbs
- Using the select verb for data processing
- Using the filter verb for data processing
- Using the arrange verb for data processing
- Using mutate for data processing
- Using summarise to summarize a dataset
Hands-on:
- Write R code to preprocess, to
- summarize data and basic
- visualization of the data
Learning Objectives:
Learn basic data visualization techniques to build charts using R.
Topics Covered:
- Basic Data Visualization with standard libraries
Hands-on:
Write R code to perform basic visualization of the data
Learning Objectives:
Case Study to explore R.
Topics Covered:
- Case Study: R Programming
Hands-on:
Case Study to explore R
Projects
Create informative visualization using R
Write R code to perform basic visualization of the data
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Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment for me.
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Alexandr Waldroop
Data Architect.Faqs
The Course
Glassdoor has ranked Data Science as the best job in America 3 years in a row, with a median base salary of $110000 and 4,524 job openings. This demand is only increasing year on year, making it the fastest growing tech employment area today. Jobs that require knowledge of data science include Data scientist, Analytics Manager, Database Administrator, Data Engineer, Business Intelligence Developer etc. This course will help you learn the R programming language which is one of the most commonly used languages in the Data Science space.
- How to use the R programming language and its environment
- How to use R functions to manipulate data
- How to analyze and manipulate data with R
By the end of this course, you would have gained knowledge of the use of R language to build applications on data statistics. This will help you land jobs as data analysts.
Tools and Technologies used for this course are
- R
- R Studio
There are no restrictions but participants would benefit if they have elementary programming knowledge.
Yes, KnowledgeHut offers this training online
On successful completion of the course, you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are R experts who have years of industry experience.
Finance Related
Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for a refund. Kindly go through our Refund Policy for more details: Refund Policy.
KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.
The Remote Experience
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor