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Overview
Data Science, Statistics, Hypothesis Tests, Regression, Simulations for Business & Finance: Python Coding AND Theory A-Z What you'll learn: Learn Python coding from Zero in a Business, Finance & Data Science context (real Examples)Learn Business & Finance (Time Value of Money, Capital Budgeting, Risk, Return & Correlation)Learn Statistics (descriptive & inferential, Probability Distributions, Confidence Intervals, Hypothesis Testing)Learn how to use the Bootstrapping method to perform hands-on statistical analyses and simulations Learn Regression (Covariance & Correlation, Linear Regression, Multiple Regression, ANOVA)Learn...
Syllabus
- Getting Started
- ---- PART 1: PYTHON BASICS, TIME VALUE OF MONEY AND CAPITAL BUDGETING ----
- How to use Python as a Calculator for basic Time Value of Money Problems
- How to use Lists and For Loops for TVM Problems with many Cashflows
- 100% Python: Objects, Data Types, Operators & Functional Programming
- How to solve for IRR & YTM with While Loops and Conditional Statements
- How to create great graphs with Matplotlib - Plotting NPV and IRR
- The Numpy Package: Working with numbers made easy!
- How to solve complex TVM and Capital Budgeting problems with Python and Numpy
- --- PART 2: STATISTICS AND HYPOTHESIS TESTING WITH PYTHON, NUMPY AND SCIPY ---
- How to perform Descriptive Statistics on Populations and Samples
- Common Probability Distributions and how to construct Confidence Intervals
- How to estimate Population parameters with Samples - Sampling and Estimation
- How to perform Hypothesis Tests: Z-Tests, t-Tests, Bootstrapping & more
- -- PART 3: ADVANCED PYTHON, MONTE CARLO SIMULATIONS AND VALUE AT RISK (VAR) ---
- n-dimensional Numpy Arrays / How to work with numerical Tabular Data
- How to create your own user-defined Functions
- Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy
- --- PART 4: MANAGING (FINANCIAL) DATA WITH PANDAS: BEYOND EXCEL ---
- Pandas Basics - Starting from Zero
- Pandas Intermediate
- Data Visualization with Pandas, Matplotlib and Seaborn
- Pandas Advanced
- Managing Time Series and Financial Data with Pandas
- Creating, analyzing and optimizing Financial Portfolios with Python
- --- PART 5: REGRESSION ANALYSIS (A MUST-HAVE FOR MACHINE LEARNING) ---
- Correlation and Regression
- OLS Regression, ANOVA and Hypothesis Testing
- Multiple Regression Models
- Case Study: Multi-Factor Models (Fama-French)
- Issues in Linear Regression Analysis and Logistic Regression
- Extra Section: Introduction to Object Oriented Programming (OOP)
- WhatÂŽs next? (outlook and additional resources)
Instructor
Alexander Hagmann
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