Syllabus - COMPUTATIONAL STATISTICS (CB304)

Computer Science and Business System (CSBS)

COMPUTATIONAL STATISTICS (CB304)

III

Multivariate Normal Distribution

Multivariate Normal Distribution Functions, Conditional Distribution and its relation to regression model, Estimation of parameters.

Discriminant Analysis

Statistical background, linear discriminant function analysis, Estimating linear discriminant functions and their properties.

Principal Component Analysis

Principal components, Algorithm for conducting principal component analysis, deciding on how many principal components to retain, H-plot.

Factor Analysis

Factor analysis model, Extracting common factors, determining number of factors, Transformation of factor analysis solutions, Factor scores.

Clustering

Introduction, Types of clustering, Correlations and distances, clustering by partitioning methods, hierarchical clustering, overlapping clustering, K-Means Clustering- Profiling and Interpreting Clusters

Python Concepts, Data Structures, Classes

Interpreter, Program Execution, Statements, Expressions, Flow Controls, Functions, Numeric Types, Sequences and Class Definition, Constructors, Text & Binary Files - Reading and Writing

Data Wrangling

Combining and Merging Datasets, Reshaping and Pivoting, Data Transformation, String Manipulation, Regular Expressions

Data Aggregation, Group Operations, Time series

GoupBy Mechanics, Data Aggregation, Groupwise Operations and Transformations, Pivot Tables and Cross Tabulations, Time Series Basics, Data Ranges, Frequencies and Shifting

Visualization in Python

Matplotlib package, Plotting Graphs, Controlling Graph, Adding Text, More Graph Types, Getting and setting values, Patches

Practicals

  • Laboratory

    Python Concepts, Data Structures, Classes: Interpreter, Program Execution, Statements, Expressions, Flow Controls, Functions, Numeric Types, Sequences and Class Definition, Constructors, Text & Binary Files - Reading and Writing Data Wrangling: Combining and Merging Datasets, Reshaping and Pivoting, Data Transformation, String Manipulation, Regular Expressions Data Aggregation, Group Operations, Time series:GoupBy Mechanics, Data Aggregation, Groupwise Operations and Transformations, Pivot Tables and Cross Tabulations, Time Series Basics, Data Ranges, Frequencies and Shifting Visualization in Python: Matplotlib package, Plotting Graphs, Controlling Graph, Adding Text, More Graph Types, Getting and setting values, Patches

Reference Books

  • An Introduction to Multivariate Statistical Analysis, T.W. Anderson

  • Applied Multivariate Data Analysis, Vol I & II, J.D. Jobson

  • Statistical Tests for Multivariate Analysis, H. Kris

  • Programming Python, Mark Lutz

  • Python 3 for Absolute Beginners, Tim Hall and J-P Stacey

  • Beginning Python: From Novice to Professional, Magnus Lie Hetland. Edition, 2005

  • Regression Diagnostics , Identifying Influential Data and Sources of Collinearety, D.A. Belsey, E. Kuh and R.E. Welsch

  • Applied Linear Regression Models, J. Neter, W. Wasserman and M.H. Kutner

  • The Foundations of Factor Analysis, A.S. Mulaik

  • Introduction to Linear Regression Analysis, D.C. Montgomery and E.A. Peck

  • Cluster Analysis for Applications, M.R. Anderberg

  • Multivariate Statistical Analysis, D.F. Morrison

  • Python for Data Analysis, Wes Mc Kinney