Business analytics and data mining Modeling using R

3,000.00

 

Prof. Gaurav Dixit

IIT Roorkee

*Additional GST and optional Exam fee are applicable.

SKU: IIT Roorkee Category:

Description

 

Objective of this course is to impart knowledge on use of data mining techniques for deriving business intelligence to achieve organizational goals. Use of R (statistical computingCSS – MOOCs Proposal software) to build, assess, and compare models based on real datasets and cases with an easy-to-follow learning curve.

PREREQUISITES

Basic Statistics Knowledge

INDUSTRY SUPPORT 

Big Data companies, Analytics & Consultancy companies, Companies with Analytics Division

 

ABOUT THE INSTRUCTOR

Dr. Gaurav Dixit is an Assistant Professor in the Department of Management Studies at the IndianInstitute of Technology Roorkee. He earned his doctoral degree from the Indian Institute ofManagement Indore and an engineering degree from Indian Institute of Technology (BHU) Varanasi.Previously, he worked in Hewlett-Packard (HP) as software engineer, and Sharda Group ofInstitutions as project manager on deputation.Gaurav’s research focuses on information technology (IT) strategy, electronic commerce, electronicwaste, data mining, and big data analytics and provides insights on business and social value of IT.His research has appeared in quality journals & conferences, including Resources, Conservation andRecycling, Journal of Global Information Technology Management, Sustainable Production andConsumption, Journal of Information Technology Management, DIGITS conference, India FinanceConference, Indian Academy of Management conference, and Academy of Management conference.

Additional information

Institute

IITR

Total hours

30

Certification Process

1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

CRITERIA TO GET A CERTIFICATE
Assignment score = Score more than 50% in at least 9/12 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.

CERTIFICATE TEMPLATE

Course Details

Week1:General Overview of Data Mining and its Components Introduction and Data Mining Process Introduction to R Basic Statistical Techniques
Week2:Data Preparation and Exploration Visualization Techniques
Week3:Data Preparation and Exploration Visualization Techniques Dimension Reduction Techniques Principal Component Analysis
Week4:Performance Metrics and Assessment Performance Metrics for Prediction and Classification
Week5:Supervised Learning Methods Multiple Linear Regression
Week6:Supervised Learning Methods Multiple Linear Regression
Week7:Supervised Learning Methods Naà ̄ve Bayes
Week8:Supervised Learning Methods Classification & Regression Trees
Week9:Supervised Learning Methods Classification & Regression Trees
Week10:Supervised Learning Methods Logistic Regression
Week11:Supervised Learning Methods Logistic Regression Artificial Neural Networks
Week12:Supervised Learning Methods and Wrap Up Artificial Neural Networks Discriminant Analysis Conclusion

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