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    Introduction to Data Science with R - Simpliv

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    Website https://www.simpliv.com/machinelearning/introduction-to-data-science-with-r | Want to Edit it Edit Freely

    Category training;webinar

    Deadline: April 24, 2019 | Date: April 24, 2019-May 03, 2019

    Venue/Country: online course, U.S.A

    Updated: 2019-04-22 20:08:02 (GMT+9)

    Call For Papers - CFP

    About this Course

    This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

    Basic knowledge

    This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs

    What you will learn

    Writing simple R programs to do basic mathematical and logical operations

    Loading structured data in a R environment for processing

    Creating descriptive statistics and visualizations

    Finding correlations among numerical variables

    Using regression analysis to predict the value of a continuous variable

    Building classification models to organize data into pre-determined classes

    Organizing given data into meaningful clusters

    Applying basic machine learning techniques for solving various data problems

    Contact Us:

    simplivllcatgmail.com

    Phone: 76760-08458

    Email: sudheeratsimpliv.com

    Phone: 9538055093

    To read more and register: https://www.simpliv.com/machinelearning/introduction-to-data-science-with-r


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
    Disclaimer: ourGlocal is an open academical resource system, which anyone can edit or update. Usually, journal information updated by us, journal managers or others. So the information is old or wrong now. Specially, impact factor is changing every year. Even it was correct when updated, it may have been changed now. So please go to Thomson Reuters to confirm latest value about Journal impact factor.