Course curriculum

1
Course Overview
 VID20200716WA0053

2
Module1 : (Probability Theory)
 Concept of Random Experiment
 Events
 Special types of events
 Event/Sample Space
 Complementary Event
 Sum of two or more events
 Product of two or more events
 Classical definition of Probability
 Some important rules
 Examples on classical definition of probability
 Axioms of Mathematical Probability
 Examples in probability
 General addition rule of Probability
 Examples on addition rule of probability
 Examples on addition rule of probability(Contd.)
 Conditional Probability
 Examples on Conditional probability
 Examples on Conditional probability(Contd.)
 Bayes Theorem
 Application of Bayes Theorem
 Independent Events
 Example on independent events
 Basic_course_on_Data_science_using_statistics_Probability_Theory_QUIZ_1
 Basic_course_on_Data_science_using_statistics_Probability_Theory_QUIZ_1,Answer Booklet

3
Module2(Basic Statistics)
 Introduction
 Basic Data Types
 Representation of Data: Ungrouped Frequency Distribution Table
 Representation of Data: Grouped Frequency Distribution Table
 Representation of Data: Grouped Frequency Distribution Table continued
 Representation of Data: Histogram, Frequency polygon
 Representation of Data: Ogive
 Introduction to measures of central tendency
 Method to calculate mean
 Properties of mean
 Method to calculate median
 Properties of median
 Method to calulate mode
 Properties of mode
 Relationship among mean,median and mode
 Partition values in a data set
 Introduction to measures of dispersion
 Graphical and algebraic measures of dispersion
 Range
 Quartile deviation
 Mean deviation
 standard deviation
 Co efficient of range,quartile deviation and mean deviation
 Coefficient of variation
 QUIZ_Measures of central tendency and dispersionconverted
 Measures of central tendency and dispersion,quiz
 Moments
 Skewness & Kurtosis
 Correlation: Intro
 Correlation: Understanding from Examples
 Correlation coefficient
 Correlation: Worked out example
 Rank correlation
 Regression
 Quiz_4_Regression
 Quiz_4_Regression Answer Booklet

4
Module 3 (Introduction to Datascience)
 Types of Data and Dataset
 Tasks in Data Science
 Data Exploration
 Data cleaning
 Dimensionality Reduction
 Basic Course on Data Science using Statistics_XX_QUIZ_1
 Basic Course on Data Science using Statistics_XX_QUIZ_1 Answer Booklet

5
Module 4 (Introduction to Data Mining)
 Tasks in Data Mining
 Machine Learning for Data Modelling
 Implementing Data Mining Tasks using Machine Leaning
 Nearest Neighbour classifier
 Naive Baye's classifier
 Basic Course on Data Science using Statistics_XX_QUIZ_2
 Basic Course on Data Science using Statistics_XX_QUIZ_ Answer Booklet
Image & text (with CTA)
The course focuses on the application of statistics on various tasks associated
with Data Science. Moreover, the course also introduces Machine Leaning as a
tool for building models for data mining tasks.
Course outcome
Instructor(s)
Senior Assistant Professor
Biswadip Basu Mallik
Associate professor
Krishanu Deyasi, PhD
Assistant Professor
Moumita Basu