Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka

This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session:

1. Why Data Mining?
2. What is Data Mining
3. Knowledge Discovery in Database
4. Data Mining Tasks
5. Programming Languages for Data Mining
6. Case study using R

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#LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience

How it Works?

1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

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About the Course

Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling 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. Analyse 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. Analyse 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

Customer Reviews:

[preetijoshi]

absolutely up to the mark, simple, informative and really as per industry requirement....

[nelsonrodelas]

thank you ssoooo much. it helps me alot....

[kagelove]

god damn that was a really good vid thank you so much

[ganugowda]

Hey it's very helpful
Can u please provide house data set, !

[faezemohagheghian]

Nice video! Can you please provide the dataset?

[palaniappanpalaniandi]

Very informative video that I ever could you please state clearly how to obtain result by implementing different model

[lohitroyal]

Informative.. Kindly share the data set and the code. It would be rely helpful.

[lisamcgourty]

Great video, very well explained, thank you so much, could you please share the dataset so that I can practice on, thank you again !!!

[jammy]

Great!! One of the best tutorials on data science I have ever come across. Please send me the csv file.

[rahulmjagan]

Got the data set. Very informative and helpful. Thank you.

[markevogt]

OVERALL a very nicely-prepared presentation !
I learned a great deal from this one single example - thanks !!
The only thing I found confusing was the final pair of examples - trying to fit a single straight line to data that CLEARLY was only VAGUELY "linear" with Room Area, and then only small homes; to dare to assert that there is a LINEAR relationship when easily 1/3 of ALL the data points WEREN'T even CLOSE to the "best line" is quite a stretch - THIS IS where a NeuralNet-based (NOT a polynomial-equation-based) approach to creating a predictive model should be considered :-) ...

I only hope my own videos comes across as well prepared as yours !
:-)
-Mark

[DoomDaam]

this is by far one of the best videos I have watched on R

[chitradhawale]

This is an excellent video . I really appreciate your efforts you took for all us interested in R as a piece of your precious knowledge on this channel! Could you please send me a dataset?

[rajatsharma]

honestly this is one of the best video available that precisely explains how do we actually build data mining models. I was so confused before and had no clue about mining or data analysis but this video has clarified everything. Thanks!

[ruhishabreen]

hi I found all u r videos informative .so I have decided to buy all videos related to R language.so can u tell me the procedure to do the same?

[narayananshankaran]

Simple tutorial on data mining, liked and subscribed. Please send the CSV file.

[aryalabaner]

Nice video. Easy to understand. Can I also have the data set to work on?

[tapanjeetroy]

Thanks a lot sir.. You are doing a great job

[ashdhuri]

The perfect video I've been looking for.
Amazingly explained. Thanks a lot🤘❤️

[testasd]

Hi. I am also interested in the (houses.csv) dataset. Thank you very much!