( R Training : https://www.edureka.co/r-for-analytics )

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 24×7 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

Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.

Website: https://www.edureka.co/data-science

Facebook: https://www.facebook.com/edurekaIN/

Twitter: https://twitter.com/edurekain

LinkedIn: https://www.linkedin.com/company/edureka

Customer Reviews:

Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, “Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now…Thanks EDUREKA and all the best. “

Facebook: https://www.facebook.com/edurekaIN/

Twitter: https://twitter.com/edurekain

LinkedIn: https://www.linkedin.com/company/edureka

The case study is quite helpful, where can I get the dataset from?

hi pl. share dataset

Can you share the data set please

Nice video! Can you please provide the dataset?

Please share the data set

Simply awesome… Thanks a lot….

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?

Hi, can i get the houses.csv file?

Thanks

Can you please provide the dataset..

Wonderful session. Have a better understanding of Regression analysis and R. Pls send the dataset (mj.jain87@gmail.com). TIA!!

Please share housedataset csv file

hello sir,can you pleaSE provide dataset?

Please, how can i get the data set used in this video, i will appreciate if sent to me.

Plz forward d dataset

I need the csv file sir..

It is very helpful. Can u please provide houses’s dataset.

On what basis are the numbers mapped to No and Yes ….. and then gas electric etc ….. i didnt catch any statement stating 1=gas 2=electric……..

Please provide dataset

Please forward the data sets

hi! this video is very helpful. Can you share house data set ?

It’s really a nice video!! thanks for the information

Thank you very much!and lovely presentation Hi. I am also interested in the (houses.csv) dataset. please send me

The video is very informative. Can you share the house’s data set?

Please forward dataset

Very much informative and simple

Please send the data file.

please share the data set

please forward the dataset

sir can you please forward the dataset

Hello,

This video is more informative, can u please provide the house’s data set?

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

thanks for your great videos! You were fast when explaining the part of Regression, as i don’t have much background

it seemed harder for me to understand it, and it would be really quite helpful if you put link below to the data with which you are working so that we practice while fellowing your tutorials, otherwise awesome tutorials !

can i get the data set?

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

pls share dataset with me

It was very helpful. can you share data set?

Could u please you share house data set ? I was mailed the diabetes data set for my last query

Cool presentation

Thanks for such an informative session. I would like to receive the data set to practice it. Please share the data set.

Very good presentation. How can I get the dataset?