Getting Started with R for Data Science

This is a hands-on introductory course to help you analyze, interpret, and optimize data in R. Find out how to load, analyze, and manipulate data from large data sets to make informed decisions

  • Comprehensive training through 26 video sessions.
  • Create a codebook so that the data can be presented in a summary
  • Understand how to use visualization to understand data and tell a story.
  • Get data from numerous sources such as files, databases, and even Twitter
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    Self-Paced

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    Course Features

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    Getting Started with R for Data Science

    The R language is a powerful open source functional programming language. R is becoming the go-to tool for data scientists and analysts. Its growing popularity is due to its open source nature and extensive development community.

    This course will take you on a journey to become an efficient data science practitioner as you thoroughly understand the key concepts of R. Starting from the absolute basics, you will quickly be introduced to programming in R. You will see how to load data into R for analysis, and get a good understanding of how to write R scripts. We will delve into data types in R, and you'll gain the ability to read and write data to and from databases as well as files. You will also get to know how to perform basic analysis of the data.

    Course Objectives
    • Write R code that can be executed outside RStudio
    • Get data from numerous sources such as files, databases, and even Twitter
    • Clean data before the analysis phase begins
    • Load libraries into RStudio for use within the analysis phase
    • Perform data cleaning on a dataset
    • Create a codebook so that the data can be presented in a summary
    Curriculum
    Module 1:

    Introducing R

    • The Course Overview
    • What is R?
    • Data Structures within R
    • Writing a Simple Program in R
    Module 2:

    Getting Data Into R

    • The Structure of a DataFrame
    • Creating a DataFrame from a CSV File
    • Creating a DataFrame from a Database
    • Creating a DataFrame from a Zip File
    Module 3:

    Cleaning And Blending Data

    • The Tools Available for Cleaning Data
    • Dealing with Null Values
    • Standardizing Date Formats
    Module 4:

    Cooking The Codebook

    • What Is a Codebook and Why Create One?
    • Creating the Codebook Using Standard R API Functionality
    • Manually Creating a Custom Codebook
    Module 5:

    Data Mining And Analysis

    • Introduction to Data Mining and Analysis
    • The Tools and Techniques for Creating the Story
    • Regression Analysis with R
    Module 6:

    A Picture Paints A Thousand Words

    • Data Visualization Tools
    • Creating Static Visualization Plots
    • Creating Interactive Plots
    • Publishing the Graphics
    Instructor

    Mykola Kolisnyk

    Mykola has been working in test automation since 2004. He has been involved with various activities including creating test automation solutions from scratch, leading test automation teams, and working as a consultant with test automation processes. During his working career, he has had experience with different test automation tools such as Mercury WinRunner, MicroFocus SilkTest, SmartBear TestComplete, Selenium-RC, WebDriver, Appium, SoapUI, BDD frameworks, and many other different engines and solutions. He has had experience with multiple programming technologies based on Java, C#, Ruby, and so on, and with different domain areas such as healthcare, mobile, telecoms, social networking, business process modeling, performance and talent management, multimedia, e-commerce, and investment banking.

    Richard Skeggs

    Richard Skeggs is not new to big data as he has over 15 years of experience in creating big data repositories and solutions for large multinational organizations in Europe. Having become a single father, he has changed his focus and is now working within the academic and research community. Richard has special interest in big data and is currently undertaking research within the field. His research interests revolve around machine learning, data retrieval, and complex systems.

    Certification

    A test will be conducted at the end of the course. On completion of the test with a minimum of 70% marks, training.com will issue a certificate of successful completion from NIIT.

    Five re-attempts will be provided in case the candidate scores less than 70%.

    A Participation certificate will be issued if the candidate does not score 70% after five attempts.

    Pre-requisites

    Basic knowledge of data analysitcs and basic programming background with Math as one of the subjects.

    FAQs

    Who should go for this Course?

    This course is a good choice for anyone aspring a career in Data Science involving complex data analytics and data mining techniques. Professionals working on Data Mining Projects can also pursue this course to help them gain an extra edge over complex data mining algorithms development using R Language.

    Where can I find my session schedule?

    The session schedule will be available in the training.com Student portal - Learning Plan section. You can login to your training.com account to view the same.

    What is your refund policy?

    Upon registering for the course, if for some reason you are unable or unwilling to participate in the course further, you can apply for a refund. You can initiate the refund any time before start of the second session of the course by sending an email to support@training.com , with your enrolment details and bank account details (where you want the amount to be transferred). Once you initiate a refund request, you will receive the amount within 21 days after confirmation and verification by our team. This is provided if you have not downloaded any courseware after registration.

    Why is it called Self Paced course?

    Self Paced courses are comprised of several learning videos into a course structure broken down into Learning Modules and Sessions. The learner is required to go through the videos topic-wise in the structure sequence of the course to learn the concepts. Being Self Paced, there is no intervention of any external faculty or additional mentor in learning.

    Being a self paced course, how will my attendance be tracked and marked?

    you login into your training.com account to watch the videos, attendance for it will be marked automatically.

    How will the assessment be conducted for my certification?

    After each module, a multiple choice questions type online assessment will be conducted. 5 Attempts will be allowed for the assessment to be completed. The minimum pass percentage for each assessment is 70%. On successfully clearing the assessment, a verified certificate from NIIT shall be awarded otherwise the certificate of participation will be issued.

    What are the minimum system requirements to attend the course?

      Minimum system requirements for accessing the courses are:

    • Personal computer or Laptop with web camera
    • Headphone with Mic
    • Minimum 4 Mbps broadband connection

    Is there an official support desk for technical guidance during the training program?

    Yes.For immediate technical support during the live online classroom sessions, you can call 91-9717992809 or 0124-4917203 between 9:00 AM and 8:00 PM IST. You can write to support@training.com for all other queries and our team will be happy to help you.

    Course Features

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