Python for Finance: Investment Fundamentals & Data Analytics

BY
Udemy

Learn using data analytic tools for conducting real-world financial analysis with Python for Finance: Investment Fundamentals & Data Analytics course.

Mode

Online

Fees

₹ 999 5900

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

Python for Finance: Investment Fundamentals & Data Analytics online course is designed to help candidates learn concepts and skills of python for finance, investment, tackle financial calculations and portfolio optimization tasks for both beginners who have never even coded before and professional python programmers who want to polish their skills and to help them increase their opportunities to enhance their career prospects.

Python for Finance: Investment Fundamentals & Data Analytics online certification is a short-term course offered by Udemy Inc., a US-based online learning platform. The course content is created by 365 careers, ranking 1 among Udemy’s best-selling providers of business, data science, and finance.

Python for Finance: Investment Fundamentals & Data Analytics syllabus covers major topics, tools, and techniques useful for data analysts and financial analysts such as Rate of return of stocks calculating the risk of stocks, rate of return of stocks, risk of a stock portfolio, covariance, the correlation between stocks, diversifiable and non-diversifiable risk, alpha & beta coefficient, regression analysis, Sharpe ratio, Markowitz efficient frontier calculations. Candidates will also learn about derivatives and their types, using Monte Carlo for options pricing and stock pricing, the Black Scholes formula, and many tools to help learners develop skills at a cost of Rs. 4,999 only.

The highlights

  • Certificate of completion
  • Self-paced course
  • Online course
  • English videos with multi-language subtitles
  • 9 hours of pre-recorded video content
  • 87 downloadable resources
  • 30-day money-back guarantee
  • Unlimited access
  • Accessible on mobile devices and TV

Program offerings

  • Certificate of completion
  • Self-paced course
  • English videos with multi-language subtitles
  • 9 hours of pre-recorded video content
  • 1 article
  • 30-day money-back guarantee
  • Unlimited access
  • Accessible on mobile devices and tv
  • 87 downloadable resources

Course and certificate fees

Fees information
₹ 999  ₹5,900
certificate availability

Yes

certificate providing authority

Udemy

Who it is for

What you will learn

Financial knowledge Knowledge of python Knowledge of numpy

After completing the Python for Finance: Investment Fundamentals & Data Analytics certification course, learners will be able to gain an understanding of the applications of python programming in investment and finance, build programs with python’s conditional operations, use python to resolve practical tasks, analyse the investment and creating an investment portfolio, calculating risk, return on investment, return of individual securities. Learners will also learn about capital asset pricing models, Monte Carlo simulations, applying the Black Scholes formula, using the Sharpe ratio, etc.

The syllabus

Welcome! Course Introduction

  • What Does the Course Cover?
  • Download Useful Resources - Exercises and Solution

Introduction to Programming with Programing

  • Programming Explained in 5 Minutes
  • Programming Explained in 5 Minutes
  • Why Python?
  • Why Python?
  • Why Jupyter?
  • Why Jupyter?
  • Installing Python and Jupyter
  • Jupyter’s Interface – the Dashboard
  • Jupyter’s Interface – Prerequisites for Coding
  • Jupyter’s Interface
  • Python 2 vs Python 3: What's the Difference?

Python Variables and Data Types

  • Variables
  • Variables
  • Numbers and Boolean Values
  • Numbers and Boolean Values
  • Strings
  • Strings

Basic Python Syntax

  • Arithmetic Operators
  • Arithmetic Operators
  • The Double Equality Sign
  • The Double Equality Sign
  • Reassign Values
  • Reassign values
  • Add Comments
  • Add Comments
  • Line Continuation
  • Indexing Elements
  • Indexing Elements
  • Structure Your Code with Indentation
  • Structure Your Code with Indentation

Python Operators Continued

  • Comparison Operators
  • Comparison Operators
  • Logical and Identity Operators
  • Logical and Identity Operators

Conditional statement

  • Introduction to the IF statement
  • Introduction to the IF statement
  • Add an ELSE statement
  • Else if, for Brief - ELIF
  • A Note on Boolean Values
  • A Note on Boolean Values

Python Function

  • Defining a Function in Python
  • Creating a Function with a Parameter
  • Another Way to Define a Function
  • Another Way to Define a Function
  • Using a Function in another Function
  • Combining Conditional Statements and Functions
  • Creating Functions Containing a Few Arguments
  • Notable Built-in Functions in Python
  • Functions

Python Sequences

  • Lists
  • Lists
  • Using Methods
  • Using Methods
  • List Slicing
  • Tuples
  • Dictionaries
  • Dictionaries

Using Iterations in Python

  • For Loops
  • For Loops
  • While Loops and Incrementing
  • Create Lists with the range() Function
  • Create Lists with the range() Function
  • Use Conditional Statements and Loops Together
  • All In – Conditional Statements, Functions, and Loops
  • Iterating over Dictionaries

Advance Python tools

  • Object Oriented Programming
  • Object Oriented Programming - Quiz
  • Modules and Packages
  • Modules - Quiz
  • The Standard Library
  • The Standard Library - Quiz
  • Importing Modules
  • Importing Modules - Quiz
  • Must-have packages for Finance and Data Science
  • Must-have packages - Quiz
  • Working with arrays
  • Generating Random Numbers
  • A Note on Using Financial Data in Python
  • Sources of Financial Data
  • Accessing the Notebook Files
  • Importing and Organizing Data in Python – part I
  • Importing and Organizing Data in Python – part II.A
  • Importing and Organizing Data in Python – part II.B
  • Importing and Organizing Data in Python – part III
  • Changing the Index of Your Time-Series Data
  • Restarting the Jupyter Kernel

Part II- FINANCE: Calculates and Comparing Rates of Return in Python

  • Considering both risk and returna
  • Risk and return - Quiz
  • What are we going to see next?
  • Calculating a security's rate of return
  • Calculating a security's rate of return
  • Calculating a Security’s Rate of Return in Python – Simple Returns – Part I
  • Calculating a Security’s Rate of Return in Python – Simple Returns – Part II
  • Calculating a Security’s Return in Python – Logarithmic Returns
  • What is a portfolio of securities and how to calculate its rate of return
  • What is a portfolio of securities and how to calculate its rate of return - Quiz
  • Calculating a Portfolio of Securities' Rate of Return
  • Popular stock indices that can help us understand financial markets
  • Which of the following is not an index? - Quiz
  • Calculating the Indices' Rate of Return

Part II- FINANCE: Measuring Investment Risk

  • How do we measure a security's risk?
  • Which of the following sentences is true? - Quiz
  • Calculating the Security's Risk In Python
  • The benefits of portfolio diversification
  • Investing in stocks - Quiz
  • Calculating the covariance between securities
  • Covariance - Quiz
  • Measuring the correlation between stocks
  • Correlation - Quiz
  • Calculating Covariance and Correlation
  • Considering the risk of multiple securities in a portfolio
  • Calculating Portfolio Risk
  • Understanding Systematic vs. Idiosyncratic risk
  • Diversifiable Risk - Quiz
  • Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio

Part II- FINANCE: Using Regressions for Financial Analysis

  • The fundamentals of simple regression analysis
  • Regressions - Quiz
  • Running a Regression in Python
  • Are all regressions created equal? Learning how to distinguish good regressions
  • Regressions - Quiz
  • Computing Alpha, Beta, and R Squared in Python

Part II- FINANCE: Markowitz Portfolio Optimization

  • Markowitz Portfolio Theory - One of the main pillars of the modern Finance
  • Markowitz - Quiz
  • Obtaining the Efficient Frontier in Python – Part I
  • Obtaining the Efficient Frontier in Python – Part II
  • Obtaining the Efficient Frontier in Python – Part III

Part II- FINANCE: The Capital Asset Pricing Model

  • The intuition behind the Capital Asset Pricing Model (CAPM)
  • CAPM - Quiz
  • Understanding and calculating a security's Beta
  • Beta - Quiz
  • Calculating the Beta of a Stock
  • The CAPM formula
  • CAPM - Quiz
  • Calculating the Expected Return of a Stock (CAPM)
  • Introducing the Sharpe ratio and how to put it into practice
  • Sharpe ratios - Quiz
  • Obtaining the Sharpe ratio in Python
  • Measuring alpha and verifying how good (or bad) a portfolio manager is doing
  • Alpha - Quiz

Part II- FINANCE: Multivariate regression analysis

  • Multivariate regression analysis - a valuable tool for finance practitioners
  • Multivariate Regressions - Quiz
  • Running a multivariate regression in Python

Part II- FINANCE: Monte Carlo simulation as a decision-making tool

  • The essence of Monte Carlo simulations
  • Monte Carlo - Quiz
  • Monte Carlo applied in a Corporate Finance context
  • Monte Carlo in Corporate Finance - Quiz
  • Monte Carlo: Predicting Gross Profit – Part I
  • Forecasting Stock Prices with a Monte Carlo Simulation
  • Monte Carlo Simulations - Quiz
  • Monte Carlo: Forecasting Stock Prices - Part I
  • Monte Carlo: Forecasting Stock Prices - Part II
  • Monte Carlo: Forecasting Stock Prices - Part III
  • An Introduction to Derivative Contracts
  • Derivatives - Quiz
  • The Black Scholes Formula for Option Pricing
  • Monte Carlo: Black-Scholes-Merton
  • Using Monte Carlo with Black-Scholes-Merton - Quiz
  • Monte Carlo: Euler Discretization - Part I
  • Monte Carlo: Euler Discretization - Part II

APPENDIX- pandas Fundamentals

  • pandas Series - Introduction
  • pandas - Working with Methods - Part I
  • pandas - Working with Methods - Part II
  • pandas - Using Parameters and Arguments
  • pandas Series - .unique() and .nunique()
  • pandas Series - .sort_values()
  • pandas DataFrames - Introduction - Part I
  • pandas DataFrames - Introduction - Part II
  • pandas DataFrames - Common Attributes
  • pandas DataFrames - Data Selection
  • pandas DataFrames - Data Selection with .iloc[]
  • pandas DataFrames - Data Selection with .loc[]

APPENDIX-Technical Analysis

  • Technical Analysis - Principles, Applications, Assumptions
  • Charts Used in Technical Analysis
  • Other Tools Used in Technical Analysis
  • Trend, Support and Resistance Lines
  • Common Chart Patterns
  • Price Indicators
  • Momentum Oscillators
  • Non-price Based Indicators
  • Technical Analysis - Cycles
  • Intermarket Analysis

BONUS LECTURE

  • Bonus Lecture: Next Steps

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