Hands-on Python for Finance

Hands-on Python for Finance, available at $44.99, has an average rating of 4.05, with 36 lectures, based on 65 reviews, and has 369 subscribers.

You will learn about General programing skills in Python and working with common Python interfaces Using Numpy, Pandas and matplotlib to manipulate, analyze and visualize data Understand the Time value of money applications and project selection Getting and with working data, time series forecasting methods and linear models Understand Correlation and portfolio construction Be comfortable with Monte Carlo Simulation, Value at Risk and Options Valuation This course is ideal for individuals who are This course is for developers and analysts with some background in programming language and are interested in a concrete framework for using Python to augment or replace spreadsheet applications for financial tasks. It is particularly useful for This course is for developers and analysts with some background in programming language and are interested in a concrete framework for using Python to augment or replace spreadsheet applications for financial tasks.

Enroll now: Hands-on Python for Finance

Summary

Title: Hands-on Python for Finance

Price: $44.99

Average Rating: 4.05

Number of Lectures: 36

Number of Published Lectures: 36

Number of Curriculum Items: 36

Number of Published Curriculum Objects: 36

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

General programing skills in Python and working with common Python interfaces

Using Numpy, Pandas and matplotlib to manipulate, analyze and visualize data

Understand the Time value of money applications and project selection

Getting and with working data, time series forecasting methods and linear models

Understand Correlation and portfolio construction

Be comfortable with Monte Carlo Simulation, Value at Risk and Options Valuation

Who Should Attend

This course is for developers and analysts with some background in programming language and are interested in a concrete framework for using Python to augment or replace spreadsheet applications for financial tasks.

Target Audiences

This course is for developers and analysts with some background in programming language and are interested in a concrete framework for using Python to augment or replace spreadsheet applications for financial tasks.

Did you know Python is the one of the best solution to quantitatively analyse your finances by taking an overview of your timeline? This hands-on course helps both developers and quantitative analysts to get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

You will begin with a primer to Python and its various data structures.Then you will dive into third party libraries. You will work with Python libraries and tools designed specifically for analytical and visualization purposes. Then you will get an overview of cash flow across the timeline. You will also learn concepts like Time Series Evaluation, Forecasting, Linear Regression and also look at crucial aspects like Linear Models, Correlation and portfolio construction. Finally, you will compute Value at Risk (VaR) and simulate portfolio values using Monte Carlo Simulation which is a broader class of computational algorithms.

With numerous practical examples through the course, you will develop a full-fledged framework for Monte Carlo, which is a class of computational algorithms and simulation-based derivatives and risk analytics.

About the Author

Matthew Macarty has taught graduate and undergraduate business school students for over 15 years and currently teaches at Bentley University. He has taught courses in statistics, quantitative methods, information systems and database design.

Course Curriculum

Chapter 1: Python Programming Primer

Lecture 1: The Course Overview

Lecture 2: Installing the Anaconda Platform

Lecture 3: Launching the Python Environment

Lecture 4: String and Number Objects

Lecture 5: Python Lists

Lecture 6: Python Dictionaries (Dicts)

Lecture 7: Repetition in Python (For Loops)

Lecture 8: Branching Logic in Python (If Blocks)

Lecture 9: Introduction to Functions in Python

Chapter 2: The Python Data Environment

Lecture 1: Introduction to NumPy Arrays

Lecture 2: NumPy – A Deeper Dive

Lecture 3: Pandas – Part I

Lecture 4: Pandas – Part II

Lecture 5: Introduction to Scipy.stats

Lecture 6: Matplotlib – Part I

Lecture 7: Matplotlib – Part II

Chapter 3: Time Value of Money

Lecture 1: Present Value of a Stream of Cash Flows

Lecture 2: Future Value of Single and Multiple Cash Flows

Lecture 3: Net Present Value of a Project

Lecture 4: Internal Rate of Return

Lecture 5: Introduction to Amortization

Lecture 6: Creating an Amortization Application

Chapter 4: Time Series Evaluation and Forecasting

Lecture 1: Opening and Reading a .CSV File

Lecture 2: Getting and Evaluating Data

Lecture 3: Moving Average Forecasting

Lecture 4: Forecasting with Single Exponential Smoothing

Lecture 5: Creating and Testing a Simple Trading System

Chapter 5: Linear Models, Correlation, and Valuation

Lecture 1: Valuing Securities with Pricing Models

Lecture 2: Finding Correlations Between Securities

Lecture 3: Linear Regression

Lecture 4: Calculating Beta and Expected Return

Lecture 5: Constructing Portfolios Along the Efficient Frontier

Chapter 6: Build a Monte Carlo Simulation App

Lecture 1: Introduction to Monte Carlo

Lecture 2: Monte Carlo Simulation

Lecture 3: Using Monte Carlo Technique to Calculate Value at Risk

Lecture 4: Putting It All Together – Monte Simulation Application

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 5 votes

2 stars: 2 votes

3 stars: 10 votes

4 stars: 24 votes

5 stars: 24 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!