Course curriculum

  • 1

    Course Overview

    • VID-20200716-WA0053
  • 2

    Module-1 : (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

    Module-2(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 dispersion-converted
    • 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

 Participants would be able to understand different task associated with data science.  Participants would be able to understand the importance of data pre-processing in data science  Participants would be able to understand the fundamental concepts associated with machine learning  Participants would be able to understand how Machine Learning models are used in solving data mining tasks  Participants would be able to understand and implement two popular classification algorithms widely used in Machine Learning


Biswadip  Basu Mallik

Senior Assistant Professor

Biswadip Basu Mallik

Biswadip Basu Mallik is presently a Senior Assistant Professor of Mathematics in the Department of Basic Sciences & Humanities at Institute of Engineering &Management, Kolkata. He has been involved in teaching and research for more than 18 years and has published several research papers in various International and National journals.He has authored four books at undergraduate levels in the areas of Engineering Mathematics and Quantitative Methods.He has taught numerous courses like Engineering Mathematics, Probability and Statistics, Complex analysis,Graph theory, Numerical methods and programming, Discrete Mathematical Structures, Operations Research and Quantitative Methods at theundergraduate & postgraduate levels. His fields of research work are Computational fluid Dynamics, Mathematical modeling and Biomechanics.Prof. Basu Mallik is a managing editor of Journal of Mathematical Sciences & Computational Mathematics (JMSCM), USA. He is a senior life member of Operational Research Society of India (ORSI), a life member of Calcutta Mathematical Society (CMS), Indian Statistical Institute (ISI), Indian Science Congress Association (ISCA), International Association of Engineers (IAENG) and an academic professional member of Society for Data Science (S4DS).
Krishanu Deyasi, PhD

Associate professor

Krishanu Deyasi, PhD

Dr.KrishanuDeyasi is currently Associate Professor in the Department of Basic Sciences & Humanities, Institute of Engineering & Management Kolkata. He obtained M.Sc. in Mathematics from Indian Institute of Technology (IIT) Kharagpur. Then he joined as a Research Scholar at Indian Institute of Science Education and Research (IISER) Kolkataupon qualifying CSIR-JRF. Prior to joining at IEM Kolkata, he worked more than one year at The Institute of Mathematical Sciences (IMSc) Chennai as a Research Associate. He has many publications in National and International Journal. Dr.Deyasi delivered many talks in several conferences. He is co-author of 3 Engineering Mathematics books for B. Tech engineering student in India. All of these books are published from Cengage India Pvt. Ltd. Dr. Deyasi is serving as an editor in the journal named Expert Opinion on Astronomy and Astrophysics from 2017. He is associated with the Journal of Mathematical Sciences & Computational Mathematics (JMSCM). He is also a member of International Association of Engineers (IAENG), Society of Data Science (S4DS).
Moumita  Basu

Assistant Professor

Moumita Basu

Moumita Basu is an Assistant Professor at the Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India. She has more than 15 years of teaching experience. She is currently pursuing Ph.D. at the Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology Shibpur, India. Her research interests include natural language processing and information retrieval in the area of disaster management.