![CSST 111 :Descriptive Statistics
UNIT1: Data Condensation and Presentation of Data (9L)
1.Definition, importance, scope and limitations of statistics.
2.Graphical Representation: Histogram,Ogive Curves, Steam and leaf chart. [Note: Theory
paper will contain only procedures. Problems to be included in practical]
3.Numerical problems related to real life situations.
4.Data Condensation: Types of data (Primary and secondary), Attributes and variables,
discrete and Continuous variables.
UNIT2: Descriptive Statistics (14L)
1.Measures of central tendency: Concept of central tendency, requisites of good measures
of central tendency.
2.Arithmetic mean: Definition, computation for ungrouped and grouped data, properties of
arithmetic mean (without proof) combined mean, weighted mean, merits and demerits.
3.Median and Mode: Definition, formula for computation for ungrouped and grouped data,
graphical method, merits and demerits. Empirical relation between mean, median and
mode (without proof)
4.Partition Values: Quartiles, Box Plot.](https://image.slidesharecdn.com/ds-intro-230404034106-e33d3809/85/DS-Intro-pptx-8-320.jpg)
DS-Intro.pptx
- 1. Department Of Statistics Course Name - F.Y. B.Sc. (Computer Science) Name of the Faculty Designation Prof. Shriram Kargaonkar Asst. Prof. & HOD, Statistics, NSS Program Officer & UBA Coordinator
- 2. Title of the Course: B. Sc. (Computer Science) STATISTICS Choice Based Credit System Syllabus To be implemented from Academic Year 2019-2020 Preamble of the Syllabus Statistics is a branch of science that can be applied practically in every walk of life. Statistics deals with any decision making activity in which there is certain degree of uncertainty and Statistics helps in taking decisions in an objective and rational way. The student of Statistics can study it purely theoretically which is usually done in research activity or it can be studied as a systematic collection of tools and techniques to be applied in solving a problem in real life.
- 3. •F.Y.B.Sc. (CS) •Computer Science • Programming Skills •Mathematics • Logic •Statistics •Analysis •Electronics • Hardware FOUR PILLERS OF F.Y.B.Sc. (COMPUTER SCIENCE)
- 4. Extensive Use of Statistics in fields like ……… Following are just few applications to name where Statistics can be extensively used. ⮚ Data Mining and Warehousing, ⮚ Big Data Analytics, ⮚ Theoretical Computer Science, ⮚ Reliability of a computer Program or Software, ⮚ Machine Learning, ⮚ Artificial Intelligence, ⮚ Pattern Recognition, ⮚ Digital Image Processing, ⮚ Embedded Systems
- 7. Structure of F. Y. B. Sc. (Computer Science) Statistics Sem-I Semester Paper code Paper Paper title credits Marks CIA ESE Total 1 CSST 111 I Descriptive Statistics I 2 15 35 50 CSST 112 II Methods of Applied Statistics 2 15 35 50 CSST113 III Statistics Practical Paper I 1.5 15 35 50
- 8. CSST 111 :Descriptive Statistics UNIT1: Data Condensation and Presentation of Data (9L) 1.Definition, importance, scope and limitations of statistics. 2.Graphical Representation: Histogram,Ogive Curves, Steam and leaf chart. [Note: Theory paper will contain only procedures. Problems to be included in practical] 3.Numerical problems related to real life situations. 4.Data Condensation: Types of data (Primary and secondary), Attributes and variables, discrete and Continuous variables. UNIT2: Descriptive Statistics (14L) 1.Measures of central tendency: Concept of central tendency, requisites of good measures of central tendency. 2.Arithmetic mean: Definition, computation for ungrouped and grouped data, properties of arithmetic mean (without proof) combined mean, weighted mean, merits and demerits. 3.Median and Mode: Definition, formula for computation for ungrouped and grouped data, graphical method, merits and demerits. Empirical relation between mean, median and mode (without proof) 4.Partition Values: Quartiles, Box Plot.
- 9. 1.Concept of dispersion, requisites of good measures of dispersion, absolute and relative measures of dispersion. 2.Measures of dispersion : Range and Quartile Deviation definition for ungrouped and grouped data and their coefficients, merits and demerits, Variance and Standard deviation: definition for ungrouped and grouped data, coefficient of variation, combined variance & standard deviation, merits and demerits. 1.Numerical problems related to real life situations. UNIT3: Moments, Skewness and Kurtosis(10L) 1.Concept of Raw and central moments: Formulae for ungrouped and grouped data (only first four moments), relation between central and raw moments upto fourth order. (without proof) 2.Measures of Skewness: Types of skewness, Pearson’s and Bowley’s coefficient of skewness, Measure of skewness based on moments. 3.Measure of Kurtosis: Types of kurtosis, Measure of kurtosis based on moments. Numerical problems related to real life situations
- 10. UNIT4: Theory of Attributes (7L) 4.1 Attributes: Concept of a Likert scale, classification, notion of manifold classification, dichotomy, class- frequency, order of a class, positive classfrequency, negative class frequency, ultimate class frequency, relationship among different class frequencies (up to two attributes), 4.2 Consistency of data upto 2 attributes. Concepts of independence and association of two attributes. Yule’s coefficient of association (Q), −1 ≤ Q ≤ 1, interpretation. References: Statistical Methods, George W. Snedecor, William G, Cochran, John Wiley &sons Programmed Statistics, B.L. Agarwal, New Age International Publishers. Modern Elementary Statistics,Freund J.E. 2005, PearsonPublication Fundamentals of Applied Statistics(3rd Edition), Gupta and Kapoor, S.Chand and Sons, New Delhi, 1987. An Introductory Statistics ,Kennedy and Gentle Fundamentals of Statistics, Vol. 1,Sixth Revised Edition,Goon, A. M., Gupta, M. K. and Dasgupta, B. (1983). The World Press Pvt. Ltd., Calcutta