Predictive Analytics Using R | Data Science With R | Data Science Certification Training | Edureka




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** Data Science Certification using R: https://www.edureka.co/data-science-r-programming-certification-course **
This Edureka video on “Predictive Analytics Using R”, will help you learn about how predictive analytics works and how it can be implemented using R to solve real-world problems. Below are the topics covered in this module:

0:56 What is Predictive Analytics?
2:02 Stages of Predictive Analytics
7:28 Predictive Analytics Using R
9:36 Predictive Analytics Use case
12:20 Demo

Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist

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About the Course

Edureka’s Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.

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Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:

1. Gain insight into the ‘Roles’ played by a Data Scientist
2. Analyze Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyze data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

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Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’

2. Analytics Managers who are leading a team of analysts

3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics

4. Business Analysts who want to understand Machine Learning (ML) Techniques

5. Information Architects who want to gain expertise in Predictive Analytics

6. ‘R’ professionals who want to captivate and analyze Big Data

7. Hadoop Professionals who want to learn R and ML techniques

8. Analysts wanting to understand Data Science methodologies.

For online Data Science training, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

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Comment List

  • edureka!
    January 17, 2021

    Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Data Science Training Certification Curriculum, Visit our Website: http://bit.ly/37q65Oc

  • edureka!
    January 17, 2021

    When I am building the model and running the knn , it is saying-'train' and 'class' have different lengths. I am not able to get output. Plz help me in resolving this issue. Thanks

  • edureka!
    January 17, 2021

    Fantastic…

  • edureka!
    January 17, 2021

    Nice 👍🏻

  • edureka!
    January 17, 2021

    Very well prepared presentation 👍

  • edureka!
    January 17, 2021

    Hi can you please help to get the cancer data dataset location to practice

  • edureka!
    January 17, 2021

    Great video .Please make a similiar videon on prescriptive and simulation analysis. It would be really helpful.

  • edureka!
    January 17, 2021

    Hi.. thanks for uploading such a terrific video with nice elaboration. I will appreciate if you post any demo for a Geographical Information System (GIS) based predictive analysis using R.

  • edureka!
    January 17, 2021

    Good video . I just had an refreshing part here

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