Any data analysis is incomplete without statistics. After getting the data, any statistical analysis starts with descriptive statistics which aims to extract the information hidden inside the data. The tools of descriptive statistics are based on mathematical and statistical functions which are to be evaluated using the software. The statistical software are paid as well as free. Most of the statistical software are paid software. A popular free statistical software is R. What are the basic tools of descriptive statistics and how to use the R software for descriptive statistical analysis is the objective of the course to be taught
INTENDED AUDIENCE
UG students of Science and Engineering. Students of humanities with basic mathematical and statistical background can also do it. Working professionals in analytics can also do it.
PRE-REQUISITES
Mathematics background up to class 12 is needed. Some minor statistics background is desirable
INDUSTRIES SUPPORT
All industries having R & D set up will use this course.
ABOUT THE INSTRUCTOR
Dr. Shalabh is a Professor of Statistics at IIT Kanpur. His research areas of interest are linear models, regression analysis and econometrics. He has more than 23 years of experience in teaching and research. He has developed several web based and MOOC courses in NPTELincluding on regression analysis and has conducted several workshops on statistics for teachers, researchers and practitioners. He has received several national and international awards and fellowships. He has authored more than 75 research papers in national and international journals. He has written four books and one of the book on linear models is co- authored with Prof. C.R. Rao.
Dr. Prashant Jha is an Assistant Professor in the Department of Mathematics at NIT Sikkim. His research areas of interest are nonparametric regression, and shape restricted regression. He has recently completed his Ph.D. from the Department of Mathematics and Statistics, IIT Kanpur. He has worked as a Teaching Assistant in several MOOC courses in NPTEL including Introduction to R software and Descriptive statistics with R software. He has received NBHM Research Award, and CSIR fellowship for Research during his Ph.D.
Additional information
Institute
IITK
Total hours
20
Certification Process
1. Join the course
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COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 2000 + 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 6/8 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
Week 1: Calculations with R Software Week 2: Introduction to Descriptive Statistics, frequency distribution Week 3: Graphics and Plots Week 4: Central Tendency of Data Week 5: Variation in Data Week 6: Moments, Association of Variables Week 7: Association of Variables Week 8: Association of Variables, Fitting of Linear Models
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