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DSA-205 A Practical Approach to Data Science 3(3-0-6)
Prerequisite : DSA-105 Introduction to Data Science
Corequisite : No
Introduction to data science and its application to the business, the life cycle of a typical
data science project. Loading data from various formats, exploring and managing data. Modeling
methods, choosing and evaluating models. Single-variable models, basic multiple-variable models
such as decision trees, nearest neighbor, and Naive Bayes. Linear and logistic regression.
Unsupervised methods, documentation and deployment.

DSA-206 Programming for Data Analysis 3(3-0-6)
Prerequisite : DSA-103 Object-Oriented Programming and
DSA-107 Data Structure and Algorithm
Corequisite : No
A scripting language for statistical data manipulation and analysis, Introduction to R and
RStudio, How to run R, introduction to functions, data types such as scalars, vectors, arrays, and
matrices, Vector operations, vector indexing, filtering, List and its operations, data frame, factors
and tables, R programming structures such as control statements, arithmetic and boolean operators,
functions, and recursion, Math and statistical functions, Object-oriented programming, Input /
Output, Graphics package, Debugging, Fundamental packages for statistics and data analysis.

DSA-207 Programming for Data Analysis Laboratory 1(0-3-2)
Prerequisite : DSA-103 Object-Oriented Programming and
DSA-107 Data Structure and Algorithm
Corequisite : No
Introduction to programming for data analysis, for example, running Python with
interactive prompt, command line and IDE, types and operations. Numerics, dynamic typing, list
and dictionaries, tuples and files, expression and statements, functions, modules and packages,
classes and OOP, essential packages for data analysis such as NumPy, pandas and matplotlib.