Com5fs112 3 — Syllabus

• Pre-requisites: Understanding of basic spreadsheet functions and familiarity with data entry, basic formulas, and common features in spreadsheet software. Basic knowledge of business concepts is recommended but not mandatory.

Course Summary This course explores application of spreadsheet functions, formulas, Pivot Tables and Pivot Charts, Macros and Automation, Data visualization techniques and Efficient data entry techniques.

• Course Outcomes (CO): CO CO Statement Cognitive Level* Knowledge Category# Evaluation Tools used CO1 Understand the basics of Python programming language U C Instructor created exams/practical assignments CO2 Apply suitable programming constructs and built in data structures to solve a problem Ap P Instructor-created exams / Quiz / Project CO3 Understand the principles and techniques of Python programming for finance applications C P Practical Assignment / Observation of Practical Skills / Examinations CO4 Apply financial models and formulae using python Ap P Practical assignment/Examinations
• - Remember (R), Understand (U), Apply (Ap), Analyse (An), Evaluate (E), Create (C) # - Factual Knowledge(F) Conceptual Knowledge (C) Procedural Knowledge (P) Metacognitive Knowledge (M) Detailed Syllabus:

Module Unit Content Hrs I Introduction to Python Programming 16 1 Introduction to computer programing language, low level and high level languages, algorithm, flowchart, syntax rules, exceptions and debugging, Assembler,

Compilers-Interpreters. 1 Module Unit Content Hrs 2 History and salient features of python, Python IDEs and Code Editors installation of python, Python virtual machine(PVM), Byte code, working of a python code, code portability and platform independence 1 3 Interactive mode and calculator mode, variables, flag variable, statically typed vs dynamically typed variables Python keywords, indentation and block of code, writing and execution of simple python programs. Input and output statements, type conversions(implicit and explicit type conversion), data type checking, Python comments 2 4 Operators and Precedence: Arithmetic operators, Relational operators, Logical operators, Assignment operator, Membership operator, Expressions and Statements , formatted output 2 5 Function definition, built-in functions and user defined functions, Function argument, range function, print vs return statement 3 6 Data types in Python: Numeric (Integer, complex number, float), Sequence (Strings, list, tuple), Set, Dictionary Boolean, Binary Types. 1 7 Functions/operations associated with data structures, String(slicing, indexing, concatenation), List(append, remove, insert, count, sort, index, reverse, pop, slice, min& max, clear concatenate, copy), Set (add, remove, intersection, union, in, not in, difference, symmetric difference) 1 8 Python modules and packages, file handling/instructions in python, reading and writing to a file, pickle module 2 9 Object-oriented Programming (OOPs) approach in Python: Basic concepts 3 II Flow Control in Python 05 1 Concept of flow control statements in python, sequential construct 1 2 Selective construct (simple if..else, nested if, ladder if) 1 3 Iteration construct (for loop and while loop) 2 4 Loop control statements (break, continue and pass) 1 III Data visualization in Python 07 1 Numpy library, arrays, array creation functions, Numpy array attributes, indexing and slicing of array, printing of array, array modification instructions 2 2 Installation of plotting libraries, Data visualization using matplotlib module, graph plotting functions, bar chart, pie chart, histogram 1 3 Analyzing Data Using Pandas, Pandas Data Frame 2 4 Statistical graphics using seaborn 2 IV Python for financial data analysis 08 1 Calculating and Comparing Rates of Return in Python: Calculating a Security’s Rate of Return in Python, Simple Returns and Logarithmic Returns, Calculating a Portfolio of Securities' Rate of Return in Python 2 2 Measuring Investment Risk in Python: Calculating Covariance and Correlation,

Calculating Portfolio Risk, Understanding Systematic vs. Idiosyncratic risk, 2 Module Unit Content Hrs Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio 3 Using regressions for Financial Analysis in Python: The fundamentals of simple Regression Analysis, running a Regression in Python, Computing Alpha, Beta, and R Squared in Python 2 4 Capital Asset Pricing Model (CAPM) in Python: Understanding and Calculating a Security's Beta, The CAPM formula, Calculating the Expected Return of a Stock (CAPM) in Python, Introduction to Sharpe ratio, Obtaining the Sharpe Ratio in Python, Measuring Alpha 2 V Open Ended Module for Practical

• Create a Python calculator capable of performing arithmetic operations like addition, subtraction, multiplication, and division. Achieve this by integrating functions and employing flow control statements. Start the development process by establishing functions for fundamental operations
• Create a Hangman Game using Python in which participants attempt to guess a secret word by proposing letters. The game persists until the player accurately uncovers the entire word or exhausts their allotted attempts
• Generate a graphical representation of item sales in a shop using Python, employing the matplotlib library.
• Create a Python code for managing bank account details in Python, applying object-oriented programming principles. The code should include methods for depositing money, withdrawing funds, and checking the account balance
• Obtain stock data for top companies per industry/sector/ (NIFTY 50/BSE SENSEX Companies) for multiple years in Python
• Calculate stock returns for top companies per industry/sector/ (NIFTY 50/BSE SENSEX Companies) for multiple years in Python
• Calculate the Beta of top listed companies (NIFTY 50/BSE SENSEX) in Python
• Use the yfinance library to obtain financial market data, such as stock prices, beta, earnings per share (EPS), balance sheets, and income statements. 09