JNTUA B.TECH R 20 2-4 Syllabus For Statistical methods for data science PDF 2022
February 10, 2022 2022-02-10 17:07JNTUA B.TECH R 20 2-4 Syllabus For Statistical methods for data science PDF 2022
JNTUA B.TECH R 20 2-4 Syllabus For Statistical methods for data science PDF 2022
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You will be able to find information about Statistical methods for data science along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Statistical methods for data science after reading this blog. Statistical methods for data science has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Statistical methods for data science can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.
All of the Topic and subtopics related to Statistical methods for data science are mentioned below in detail. If you are having a hard time understanding Statistical methods for data science or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.
Statistical methods for data science Unit One
Basic Concepts
Population, sample, parameter and statistic; characteristics of a good estimator; Consistency –Invariance property of Consistent estimator, Sufficient condition for consistency; Unbiasedness; Sufficiency –Factorization Theorem –Minimal sufficiency; Efficiency –Most efficient estimator,likelihood equivalence, Uniformly minimum variance unbiased estimator,applications of Lehmann-Scheffe’s Theorem, Rao -Blackwell Theorem and applications
Statistical methods for data science Unit Two
Point Estimation
Point Estimation-Estimator, Estimate, Methods of point estimation –Maximum likelihood method (the asymptotic properties of ML estimators are not included), Large sample properties of ML estimator(without proof)-applications , Method of moments, method of least squares, method of minimum chi-square and modified minimum chi-square-Asymptotic Maximum Likelihood Estimation and applications.
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Statistical methods for data science Unit Three
Interval Estimation
Confidence limits and confidence coefficient; Duality between acceptance region of a test and a confidence interval; Construction of confidence intervals for population proportion (small and large samples) and between two population proportions(large samples); Confidence intervals for mean and variance of a normal population; Difference between the mean and ratio of two normal populations.
Statistical methods for data science Unit Four
Testing of hypotheses
Types of errors, power of a test, most powerful tests; Neyman-Pearson Fundamental Lemma and its applications; Notion of Uniformly most powerful tests; Likelihood Ratio tests: Description and property of LR tests -Application to standard distributions.
Statistical methods for data science Unit Five
Small sample tests
Student’s t-test, test for a population mean, equality of two population means, paired t-test, F-test for equality of two population variances,CRD,RBD,LSD; Chi-square test for goodness of fit and test for independence of attributes, χ2 test for testing variance of a normal distributionSign test, Signed rank test, Median test, Mann-Whitney test, Run test and One sample Kolmogorov –Smirnov test ,Kruskal –Wallis H test(Description, properties and applications only).
Statistical methods for data science Course Objectives
This course aims at providing knowledge on basic concepts of Statistics, Estimation and testing of hypotheses for large and small samples.
Statistical methods for data science Course Outcomes
•Understand the basic concepts of Statistics
•Analyze data and draw conclusion about collection of data under study using Point estimation
•Analyze data and draw conclusion about collection of data under study using Interval estimation.
•Analyzing the tests and types of errors for large samples
•Apply testing of hypothesis for small samples.
Statistical methods for data science Text Books
1.Manoj Kumar Srivastava and Namita Srivastava, Statistical Inference –Testing ofHypotheses, Prentice Hall of India, 2014.
2.Robert V Hogg, Elliot A Tannis and Dale L.Zimmerman, Probability and Statistical Inference,9th edition,Pearson publishers,2013.
Statistical methods for data science Reference Books
1.S.P.Gupta, Statistical Methods, 33rd Edition, Sultan Chand & Sons.
2.Miller and John E Freund, Probability and Statistics for Engineers, 5th Edition.
Scoring Marks in Statistical methods for data science
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