Any data analysis is incomplete without statistics. After getting the data, the statistical tools aims to extract the information hidden inside the data. The main objective of statistics is to work on a small sample of data but provide conclusions for the whole population. Such results cannot be obtained without learning the concepts and tools of theory of probability and statistical inference. With the advent of data science, it has become important to learn those tools from computational and data based aspects. Without learning the basic fundamentals of probability theory and statistical inference, it is difficult to implement them correctly on the data and draw correct statistical conclusions. Such fundamental topics have enormous applicability in data science and are to be learnt from data based computational perspectives through software. How to use them with the popular and freely available R statistical software and how to understand the correct statistical inferences is the objective of the course to be taught.
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.
“Introduction to R Course” is preferred. Mathematics background up to class 12 is needed. Some minor statistics background is desirable.
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.
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.
Week 1:Introduction to data science, basic calculations with R Software and probability theory
Week 2:Probability theory and random variables
Week 3: Random variables and Discrete probability distributions
Week 4:Continuous probability distributions
Week 5:Sampling distributions and Functions of random variables
Week 6:Convergence of random variables, Central limit theorems and Law of large numbers
Week 7: Statistical inference and point estimation
Week 8:Methods of point estimation of parameters
Week 9:Point and confidence interval estimation
Week 10:Confidence interval estimation and test of hypothesis
Week 11: Test of hypothesis
Week 12:Test of hypothesis for attributes and other tests