100% Money Back Guarantee Description
Data science is the future and among the hottest trends in the job market right now. Not only IT, but every sector, from Banking to Healthcare, and Transport to E-Commerce, is now realizing the disruptive powers of data science and using it to enhance business. Among the most popular programming languages used to write data science applications is R. Open source, highly flexible, a large number of libraries, easy-to-learn - all these make R the go-to software for Data Science. It also has capabilities to produce a range of graphics including charts, plots, and graphs that can be used for presentations.
R Programming for Data Science course prepares you with R Programming capabilities for Data Manipulation, Exploratory Data Analysis and Data Visualization which are an absolute must for being a Data Science expert. You will be part of the most cutting-edge companies that are leading the technology revolution. Enroll now and take advantage of the flexible learning modes and comprehensive workshop offered by KnowledgeHut.
What You Will Learn
1. Basics of R
Learn to install R studio - Explore R language fundamentals, including basic syntax, variables, and types
2. Data Structures
Learn about the different 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. Use functions and import packages
5. Analyzing Data
Manipulate and analyze dataset in R using Dplyr.
6. Data Visualization
Use various R libraries to visualize data. Create and customize plots on real data
Prerequisites
Anybody who is a Data Science aspirant with coding or non-coding background
Who should Attend?
- Those interested in data science who want to learn essential skills
- Those new to R and looking for a more robust, structured learning program
- Software or Data Engineers interested in learning R for Data Science
KnowledgeHut Experience
Instructor-led Experience
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.
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 all about and it is such a popular tool 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 an 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:
Know how to install R, R Studio and other libraries.
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:
Learn how to write custom functions, nested functions and functions with arguments
Learning Objectives:
Learn all about the efficient loop functions available in R which can be written with a single command.
Topics Covered:
- apply
- lapply
- Sapply
- mapply
- tapply
Hands-on:
Work on loop functions available in R.
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:
Work on string manipulations and regular expressions.
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:
Import data, write files and connect to databases.
Learning Objectives:
Manipulate & learn to transform raw data using dplyr. Learn to generate insights from your data.
Topics Covered:
- Clean & Prepare Datasets
- Data Transformations
- Encoding
Hands-on:
Write R code to generate insights from data.
Learning Objectives:
Learn to summarize datasets through descriptive statistics. Use a variety of measurements to better understand you data. Learn to treat missing values. Also, learn how to discover patterns in your data.
Topics Covered:
- Summarize Data
- Statistical analysis of Data
- Extensive Data Exploration for deeper insights
- Missing value treatment
- Quality Analysis
Hands-on:
Write R code to better understand the data.
Learning Objectives:
Learn visualization in R with base and ggplot libraries. Learn Grammar of Graphics in a very structured and easy-to-understand manner.
Topics Covered:
- Visualization with base R
- ggplot2: Grammar of Graphics
- Visualisation using ggplot2
Hands-on:
Write R Code to implement ggplot for data visualization.
Learning Objectives:
Explore a case study.
Topics Covered:
- Real-Life Case Study
Hands-on:
Case Study: House Attributes and Sales Price data. Use this data to explore more. Deep Dive into advanced explorations. Analyze and Visualize missing data, treat missing data to missing value imputation. Visualize data with various libraries. Gain deep insights on your data.
Projects
Create informative visualizations using ggplot
Write R Code to implement ggplot for data visualization
Using House Attributes and Sales Price, perform exploratory data analysis
House Attributes and Sales Price data. Use this data to explore more. Deep Dive into advanced explorations. Analyze and Visualize missing data, treat missing data to missing value imputation. Visualize data with various libraries. Gain deep insights on your data
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 master R programming language and use it to create applications for business solutions.
On completing this course, you will be able to:
- Use R studio
- Create user defined functions
- Manipulate & analyze data
- Visualize data using R libraries like Ggplot, Plotly and Ggvis
By the end of this course, you would have gained knowledge on the use of data science techniques and the R language to build applications on data statistics. This will help you land jobs as data scientists.
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 the written request for refund. Kindly go through our Refund Policy for more details.
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
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