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Finance & Accounting
Finance
Finance Fundamentals
Complete 2-in-1 Python for Business and Finance Bootcamp

Complete 2-in-1 Python for Business and Finance Bootcamp

Loại khoá học: Finance

Data Science, Statistics, Hypothesis Tests, Regression, Simulations for Business & Finance: Python Coding AND Theory A-Z

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50.000 VND
1.249.000 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

(Latest course update and full review in May 2023. Now with more than 30 Udemy Online Coding Exercises - NEW Feature!)


Hi and welcome to this Course!

This is the first-ever comprehensive Python Course for Business and Finance Professionals. You will learn and master Python from Zero and the full Python Data Science Stack with real Examples and Projects taken from the Business and Finance world.   

This isn´t just a coding course. You will understand and master all required theoretical concepts behind the projects and the code from scratch.

Important: the quality Benchmark for the theory part is the CFA (Chartered Financial Analyst) Curriculum. The Instructor of this course holds a Master´s Degree in Finance and passed all three CFA Exams. In this course, we leave absolutely no room for wrong/dubious (but frequently promoted) practices like LSTM stock price predictions or using stock prices in linear regressions.         

You will become an expert not only in Python Coding but also in

  • Business & Finance (Time Value of Money, Capital Budgeting, Risk, Return & Correlation, Monte Carlo Simulations, Quality and Risk Management in Production and Finance, Mortgage Loans, Annuities and Retirement Planning, Portfolio Theory, Portfolio Optimization, Asset Pricing & Factor Models, Value-at-Risk)

  • Statistics (descriptive & inferential statistics, Confidence Intervals, Hypothesis Testing, Normal Distribution & Student´s t-distribution, p-value, Bootstrapping Method, Monte Carlo Simulations, Normality of Returns)

  • Regression (Covariance & Correlation, Linear Regression, Multiple Regression and its pitfalls, Hypothesis Testing of Regression Coefficients, Logistic Regression, ANOVA, Dummy Variables, Links to Machine Learning, Fama-French Factor Models)    

This course follows a mutually reinforcing concept: Learning Python and Theory simultaneously: 

  • Learning Python is more effective when having the right context and the right examples (avoid toy examples!).

  • Learning and mastering essential theories and concepts in Business, Finance, Statistics and Regression is way easier and more effective with Python as you can simulate, visualize and dynamically explain the intuition behind theories, math and formulas. 

This course covers in-depth all relevant and commonly used Python Data Science Packages:

  • Python from the very Basics (Standard Library)

  • Numpy and Scipy for Numeric, Scientific, Financial, Statistical Coding and Simulations

  • Pandas to handle, process, clean, aggregate and manipulate Tabular (Financial) Data. You deserve more than just Excel!

  • statsmodels to perform Regression Analysis, Hypothesis Testing and ANOVA

  • Matplotlib and Seaborn for scientific Data Visualization

This course isn´t just videos:

  • Downloadable Jupyter Notebooks with thousands of lines of code

  • Downloadable PDF Files containing hundreds of slides explaining and repeating the most important concepts

  • Downloadable Jupyter Notebook with hundreds of coding exercises incl. hints and solutions

I strictly follow one simple rule in my coding courses: No code without explaining the WHY. You won´t hear comments like "...that´s the Python code, feel free to google for more background information and figure it out yourself". Your boss, your clients, your business partners and your colleges don´t accept that. Why should you ever accept this in a course that builds your career?  Even the best (coding) results have only little value if they can´t be explained and sold to others.

I am Alexander Hagmann, Finance Professional and best-selling Instructor for (Financial) Data Science, Finance with Python and Algorithmic Trading. Students who completed my courses work in the largest and most popular tech and finance companies all over the world. From my own experience and having coached thousands of professionals and companies online and in-person, there is one key finding: Professionals typically start with the wrong parts of the Python Ecosystem, in the wrong context, with the wrong tone and for the wrong career path.

Do it right the first time and save time and nerves! What are you waiting for? There is no risk for you as you have a 30 Days Money Back Guarantee.

Thanks and looking forward to seeing you in the Course!

Bạn sẽ học được gì

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 how to use all relevant and powerful Python Data Science Packages and Libraries

Learn how to use Numpy and Scipy for numerical, financial and scientific computing

Learn how to use Pandas to process Tabular (Financial) Data - cleaning, merging, manipulating

Learn how to use stats (scipy) for Statistics and Hypothesis Testing

Learn how to use statsmodels for Regression Analysis and ANOVA

Learn how to create meaningful Visualizations and Plots with Matplotlib and Seaborn

Learn how to create user-defined functions for Business & Finance applications

Learn how to solve and code real Projects in Business, Finance & Statistics

Learn how to unleash the full power of Python and Numpy with Monte Carlo Simulations

Understand and code Sharpe Ratio, Alpha, Beta, IRR, NPV, Yield-to-Maturity (YTM)

Learn how to code more advanced Finance concepts: Value-at-Risk, Portfolios and (Multi-) Factor Models

Understand the difference between the Normal Distribution and Student´s t-distributions: what to use when

Yêu cầu

  • No (Python) Coding required. This Course starts from complete zero und teaches you everything from scratch.
  • No specific Business/Finance, Statistics & Data Science knowledge needed! The course intuitively explains basic and advanced concepts.
  • A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
  • An internet connection capable of streaming videos.
  • Some high school level math skills would be great (not mandatory, but it helps)

Nội dung khoá học

33 sections

Getting Started

5 lectures
Tips: How to get the most out of this Course (don´t skip!)
05:27
FAQ / Your Questions answered
02:36
How to download and install Anaconda for Python coding
06:15
Jupyter Notebooks - let´s get started
12:24
How to work with Jupyter Notebooks
17:25

---- PART 1: PYTHON BASICS, TIME VALUE OF MONEY AND CAPITAL BUDGETING ----

2 lectures
Overview & Download of Course Materials for Part 1 ***Update May 2023***
05:09
Coding Projects Part 1 - Overview
01:54

How to use Python as a Calculator for basic Time Value of Money Problems

19 lectures
Intro to the Time Value of Money (TVM) Concept (Theory)
06:01
Calculate Future Values (FV) with Python / Compounding
03:29
***NEW*** Udemy Online Coding Exercises - Intro
04:28
Future Value
1 question
Calculate Present Values (PV) with Python / Discounting
02:38
Present Value
1 question
Interest Rates and Returns (Theory)
04:26
Calculate Interest Rates and Returns with Python
03:47
Interest Rates
1 question
Introduction to Variables
05:04
Variables
1 question
Excursus: How to add inline comments
02:50
Variables and Memory (Theory)
01:57
More on Variables and Memory
06:33
Addition Assignment
1 question
Variables - Dos, Don´ts and Conventions
03:49
The print() Function
04:09
print()
1 question
Coding Exercise 1
09:00

How to use Lists and For Loops for TVM Problems with many Cashflows

15 lectures
TVM Problems with many Cashflows
03:21
Intro to Python Lists
02:22
Creating Lists
1 question
Zero-based Indexing and negative Indexing in Python (Theory)
02:47
Indexing Lists
03:10
Indexing Lists
1 question
For Loops - Iterating over Lists
07:48
List Iteration
1 question
The range Object - another Iterable
04:56
Iterating over range objects
1 question
Calculate FV and PV for many Cashflows
07:35
The Net Present Value - NPV (Theory)
07:47
Calculate an Investment Project´s NPV
03:02
Calculating NPV
1 question
Coding Exercise 2
08:41

100% Python: Objects, Data Types, Operators & Functional Programming

37 lectures
Data Types in Action
06:07
Strings
1 question
The Data Type Hierarchy (Theory)
03:30
Excursus: Dynamic Typing in Python
01:38
Build-in Functions
05:52
Functions
1 question
Integers
03:18
Floats
05:58
How to round Floats (and Integers) with round()
05:10
Rounding
1 question
More on Lists
05:15
Lists and Element-wise Operations
04:19
Element-wise Operations
1 question
Slicing Lists
04:33
Slicing Cheat Sheet
00:03
Slicing Lists
1 question
Changing Elements in Lists
02:44
Changing Lists
1 question
Sorting and Reversing Lists
03:48
Sorting Lists
1 question
Adding and removing Elements from/to Lists
09:33
Adding and Removing Elements
1 question
Mutable vs. immutable Objects (Part 1)
09:04
Mutable vs. immutable Objects (Part 2)
05:12
Coding Exercise 3
11:32
Tuples
06:50
Dictionaries
06:22
Dictionary
1 question
Intro to Strings
08:47
Capitalize Strings
1 question
String Replacement
04:10
String Replacement
1 question
Booleans
02:23
Operators (Theory)
04:37
Comparison, Logical and Membership Operators in Action
08:21
Booleans and Operators
1 question
Coding Exercise 4
08:56

How to solve for IRR & YTM with While Loops and Conditional Statements

12 lectures
Conditional Statements
09:04
Conditionals
1 question
Keywords pass, continue and break
09:37
Keywords
1 question
Calculate a Project´s Payback Period
04:35
While Loops
07:58
While Loop
1 question
The Internal Rate of Return - IRR (Theory)
06:10
Solving for a Project´s IRR
11:26
Bonds and the Yield to Maturity - YTM (Theory)
10:01
Solving for a Bond´s Yield to Maturity (YTM)
02:42
Coding Exercise 5
10:41

How to create great graphs with Matplotlib - Plotting NPV and IRR

7 lectures
Intro
01:23
Line Plots
05:38
Scatter Plots
01:59
Customizing Plots (Part 1)
06:29
Customizing Plots (Part 2)
11:54
Plotting NPV & IRR
09:08
Coding Exercise 6
00:04

The Numpy Package: Working with numbers made easy!

22 lectures
Modules, Packages and Libraries - No need to reinvent the Wheel
07:52
Numpy Arrays
08:23
Numpy Arrays
1 question
Indexing and Slicing Numpy Arrays
03:13
Indexing and Slicing
1 question
Vectorized Operations with Numpy Arrays
03:56
PV with vectorized Numpy Code
1 question
Changing Elements in Numpy Arrays & Mutability
05:50
View vs. copy - potential Pitfalls when slicing Numpy Arrays
04:45
Changing elements in Arrays (and Copies)
1 question
Numpy Array Methods and Attributes
05:13
Methods
1 question
Numpy Universal Functions
03:59
Universal Functions
1 question
Boolean Arrays and Conditional Filtering
04:39
Conditional Filtering
1 question
Advanced Filtering & Bitwise Operators
06:11
Advanced Filtering
1 question
Determining a Project´s Payback Period with np.where()
04:50
Creating Numpy Arrays from Scratch
05:56
Numpy Arrays from Scratch
1 question
Coding Exercise 7
12:34

How to solve complex TVM and Capital Budgeting problems with Python and Numpy

16 lectures
Evaluating Investments with npf.npv() and npf.irr()
08:18
Evaluating Annuities with npf.fv() - Funding Phase
07:19
Evaluating Annuities with npf.fv() - Payout Phase
05:37
How to solve for annuity payments with npf.pmt()
03:26
How to solve for the number of periodic payments with npf.nper()
02:44
How to calculate the required Contract Value with npf.pv()
03:11
Frequency of compounding and the effective annual interest rate
05:41
How to evaluate a Retirement Plan A-Z
07:04
Retirement Plan: Sensitivity Analysis
06:39
Mortgage Loan Analysis - Debt Sizing
07:35
Mortgage Loan Analysis - Interest Payments and Amortization Schedule
12:45
Calculate PV of equal installments with npf.pv() - Valuation of Bonds
02:39
Capital Budgeting - Mutually exclusive Projects (Part 1)
03:25
Capital Budgeting - Mutually exclusive Projects (Part 2)
06:21
Capital Budgeting - Mutually exclusive Projects (Part 3)
03:37
Coding Exercise 8
00:04

--- PART 2: STATISTICS AND HYPOTHESIS TESTING WITH PYTHON, NUMPY AND SCIPY ---

3 lectures
Statistics - Overview, Terms and Vocabulary
11:48
Coding Projects Part 2 - Overview
02:27
Download of Part 2 Course Materials ***Update May 2023***
04:51

How to perform Descriptive Statistics on Populations and Samples

14 lectures
Population vs. Sample
06:52
Visualizing Frequency Distributions with plt.hist()
03:51
Relative and Cumulative Frequencies with plt.hist()
05:17
Measures of Central Tendency (Theory)
04:41
Coding Measures of Central Tendency - Mean and Median
03:42
Coding Measures of Central Tendency - Geometric Mean
03:50
Excursus: Why Log Returns are useful
02:35
Variability around the Central Tendency / Dispersion (Theory)
06:05
Minimum, Maximum and Range with Python/Numpy
02:05
Variance and Standard Deviation with Python/Numpy
03:14
Percentiles with Python/Numpy
03:35
Skew and Kurtosis (Theory)
03:48
How to calculate Skew and Kurtosis with scipy.stats
05:34
Coding Exercise 1
00:04

Common Probability Distributions and how to construct Confidence Intervals

14 lectures
How to generate Random Numbers with Numpy
04:33
Reproducibility with np.random.seed()
03:38
Probability Distributions - Overview
06:33
Discrete Uniform Distributions
05:57
Continuous Uniform Distributions
04:11
The Normal Distribution (Theory)
05:21
Creating a normally distributed Random Variable
05:22
Normal Distribution - Probability Density Function (pdf) with scipy.stats
03:54
Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats
02:53
The Standard Normal Distribution and Z-Values
06:40
Properties of the Standard Normal Distribution (Theory)
02:52
Probabilities and Z-Values with scipy.stats
11:02
Confidence Intervals with scipy.stats
07:16
Coding Exercise 2
00:04

How to estimate Population parameters with Samples - Sampling and Estimation

14 lectures
Sample Statistic, Sampling Error and Sampling Distribution (Theory)
05:10
Sampling with np.random.choice()
04:00
Sampling Distribution
04:00
Standard Error
02:23
Central Limit Theorem (Coding Part 1)
04:15
Central Limit Theorem (Coding Part 2)
05:31
Central Limit Theorem (Theory)
03:38
Point Estimates vs. Confidence Interval Estimates (known Population Variance)
04:38
The Student´s t-distribution: What is it and why/when do we use it?
04:20
Unknown Population Variance - the Standard Case (Example 1)
04:33
Unknown Population Variance - the Standard Case (Example 2)
02:56
Student´s t-Distribution vs. Normal Distribution with scipy.stats
05:16
Bootstrapping with Python: an alternative method without Statistics
05:20
Coding Exercise 3
00:04

How to perform Hypothesis Tests: Z-Tests, t-Tests, Bootstrapping & more

10 lectures
Hypothesis Testing (Theory)
10:34
Two-tailed Z-Test with known Population Variance
09:02
What is the p-value? (Theory)
03:19
Calculating and interpreting z-statistic and p-value with scipy.stats
03:53
One-tailed Z-Test with known Population Variance
05:54
Two-tailed t-Test (unknown Population Variance)
07:07
One-tailed t-Test (unknown Population Variance)
03:00
Hypothesis Testing with Bootstrapping
05:49
Testing for Normality of Financial Returns with scipy.stats
09:54
Coding Exercise 4
00:04

-- PART 3: ADVANCED PYTHON, MONTE CARLO SIMULATIONS AND VALUE AT RISK (VAR) ---

3 lectures
*Update Notice (June 2021)*
00:22
Overview & Download of Course Materials for Part 3 ***Update May 2023***
02:11
Coding Projects Part 3 - Overview
02:37

n-dimensional Numpy Arrays / How to work with numerical Tabular Data

13 lectures
How to work with nested Lists
04:21
2-dimensional Numpy Arrays
03:51
How to slice 2-dim Numpy Arrays (Part 1)
05:36
How to slice 2-dim Numpy Arrays (Part 2)
02:03
Recap: Changing Elements in a Numpy Array / slice
03:39
How to perform row-wise and column-wise Operations
04:33
Reshaping and Transposing 2-dim Numpy Arrays
04:47
Creating 2-dim Numpy Arrays from Scratch
03:40
Arithmetic & Vectorized Operations with 2-dim Numpy Arrays
05:19
The keepdims parameter
03:52
Adding & Removing Elements
03:53
Merging and Concatenating Numpy Arrays
03:55
Coding Exercise 1
00:04

How to create your own user-defined Functions

11 lectures
Defining your first user-defined Function
06:07
What´s the difference between Positional Arguments vs. Keyword Arguments?
05:35
How to work with Default Arguments
05:27
The Default Argument None
06:17
How to unpack Iterables
04:40
Sequences as arguments and *args
05:05
How to return many results
02:42
Scope - easily explained
08:16
How to create Nested Functions
05:18
Putting it all together - Case Study
11:30
Coding Exercise 2
00:04

Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy

15 lectures
What is the Value-at-Risk (VaR)? (Theory)
05:32
Analyzing the Data / past Performance
05:03
How to use the Parametric Method to calculate Value-at-Risk (VaR)
04:34
How to use the Historical Method to calculate Value-at-Risk (VaR)
02:41
Monte Carlo Simulations for Value-at-Risk - Parametric (Part 1)
05:12
Monte Carlo Simulations for Value-at-Risk - Parametric (Part 2)
06:36
Monte Carlo Simulations for Value-at-Risk - Parametric (Part 3)
10:03
Monte Carlo Simulations for Value-at-Risk - Bootstrapping (Part 1)
06:45
Monte Carlo Simulations for Value-at-Risk - Bootstrapping (Part 2)
06:44
Conditional Value-at-Risk (CVaR)
04:00
Dynamic & path-dependent Simulations (Part 1)
07:46
Dynamic & path-dependent Simulations (Part 2)
11:18
Dynamic & path-dependent Simulations (Part 3)
02:53
Dynamic & path-dependent Simulations (Part 4)
09:53
Coding Exercise 3
00:04

--- PART 4: MANAGING (FINANCIAL) DATA WITH PANDAS: BEYOND EXCEL ---

3 lectures
Introduction
01:34
Download of Part 4 Course Materials ***Updated May 2023***
10:57
Tabular Data and Pandas DataFrames
05:02

Pandas Basics - Starting from Zero

16 lectures
First Steps (Inspection of Data, Part 1)
10:07
First Steps (Inspection of Data, Part 2)
08:45
Built-in Functions, Attributes and Methods
08:26
Explore your own Dataset: Coding Exercise 1 (Intro)
00:04
Explore your own Dataset: Coding Exercise 1 (Solution)
04:35
Selecting Columns
08:01
Selecting Rows with Square Brackets (not advisable)
04:02
Selecting Rows with iloc (position-based indexing)
07:42
Slicing Rows and Columns with iloc (position-based indexing)
05:11
Position-based Indexing Cheat Sheets
00:04
Selecting Rows with loc (label-based indexing)
05:11
Slicing Rows and Columns with loc (label-based indexing)
11:28
Label-based Indexing Cheat Sheets
00:04
Summary and Outlook
09:38
Coding Exercise 2 (Intro)
00:04
Coding Exercise 2 (Solution)
06:31

Pandas Intermediate

32 lectures
Intro
00:12
First Steps with Pandas Series
06:44
Analyzing Numerical Series with unique(), nunique() and value_counts()
13:10
UPDATE Pandas Version 0.24.0 (Jan 2019)
00:08
EXCURSUS: Updating Pandas / Anaconda
06:34
Analyzing non-numerical Series with unique(), nunique(), value_counts()
07:17
The copy() method
03:57
Sorting of Series and Introduction to the inplace - parameter
08:59
Coding Exercise 3 (Intro)
00:04
Coding Exercise 3 (Solution)
04:53
First Steps with Pandas Index Objects
05:57
Changing Row Index with set_index() and reset_index()
10:07
Changing Column Labels
03:20
Renaming Index & Column Labels with rename()
03:51
Coding Exercise 4 (Intro)
00:04
Coding Exercise 4 (Solution)
03:42
Sorting DataFrames with sort_index() and sort_values()
09:01
nunique() and nlargest() / nsmallest() with DataFrames
05:30
Filtering DataFrames (one Condition)
10:20
Filtering DataFrames by many Conditions (AND)
04:45
Filtering DataFrames by many Conditions (OR)
05:04
Advanced Filtering with between(), isin() and ~
08:35
any() and all()
04:07
Coding Exercise 5 (Intro)
00:04
Coding Exercise 5 (Solution)
08:13
Intro to NA Values / missing Values
08:52
Handling NA Values / missing Values
10:51
Exporting DataFrames to csv
02:14
Summary Statistics and Accumulations
10:26
The agg() method
03:27
Coding Exercise 6 (Intro)
00:04
Coding Exercise 6 (Solution)
10:21

Data Visualization with Pandas, Matplotlib and Seaborn

12 lectures
Intro
00:27
Visualization with Matplotlib (Intro)
08:48
Customization of Plots
12:56
Histogramms (Part 1)
04:34
Histogramms (Part 2)
06:28
Scatterplots
07:18
First Steps with Seaborn
05:24
Categorical Seaborn Plots
13:33
Seaborn Regression Plots
12:21
Seaborn Heatmaps
08:17
Coding Exercise 7 (Intro)
00:04
Coding Exercise 7 (Solution)
07:30

Pandas Advanced

21 lectures
Intro
00:08
Removing Columns
05:18
Removing Rows
07:06
Adding new Columns to a DataFrame
03:27
Arithmetic Operations (Part 1)
11:59
Arithmetic Operations (Part 2)
10:55
Creating DataFrames from Scratch with pd.DataFrame()
07:43
Adding new Rows (Hands-on)
02:55
Adding new Rows to a DataFrame
13:51
Manipulating Elements in a DataFrame
04:42
Coding Exercise 8 (Intro)
00:04
Coding Exercise 8 (Solution)
06:11
Introduction to GroupBy Operations
02:02
Understanding the GroupBy Object
08:05
Splitting with many Keys
06:49
split-apply-combine
09:36
split-apply-combine applied
11:59
Hierarchical Indexing with Groupby
06:18
stack() and unstack()
13:31
Coding Exercise 9 (Intro)
00:04
Coding Exercise 9 (Solution)
06:05

Managing Time Series and Financial Data with Pandas

27 lectures
Importing Time Series Data from csv-files
08:16
Converting strings to datetime objects with pd.to_datetime()
08:53
Initial Analysis / Visualization of Time Series
05:41
Indexing and Slicing Time Series
07:25
Creating a customized DatetimeIndex with pd.date_range()
15:33
More on pd.date_range()
03:01
Coding Exercise 10 (intro)
00:04
Coding Exercise 10 (Solution)
05:25
Downsampling Time Series with resample() (Part 1)
14:20
Downsampling Time Series with resample (Part 2)
08:26
The PeriodIndex object
06:03
Advanced Indexing with reindex()
08:48
Coding Exercise 11 (intro)
00:04
Coding Exercise 11 (Solution)
05:30
Getting Ready (Installing required library)
02:42
Importing Stock Price Data from Yahoo Finance (it still works!)
09:29
Initial Inspection and Visualization
05:32
Normalizing Time Series to a Base Value (100)
06:31
The shift() method
06:51
The methods diff() and pct_change()
06:41
Measuring Stock Performance with MEAN Returns and STD of Returns
08:49
Financial Time Series - Return and Risk
08:30
Financial Time Series - Covariance and Correlation
04:32
Importing Financial Data from Excel
10:45
Merging / Aligning Financial Time Series (hands-on)
05:02
Coding Exercise 12 (intro)
00:04
Coding Exercise 12 (Solution)
07:28

Creating, analyzing and optimizing Financial Portfolios with Python

21 lectures
Intro
03:43
Getting the Data
02:07
Creating the equally-weighted Portfolio
08:11
Creating many random Portfolios with Python
12:09
What is the Sharpe Ratio and a Risk Free Asset?
04:33
Portfolio Analysis and the Sharpe Ratio with Python
07:51
Finding the Optimal Portfolio
06:51
Excursus: Portfolio Optimization with scipy
00:03
Sharpe Ratio - visualized and explained
05:07
Coding Exercise 13 (Intro)
00:04
Coding Exercise 13 (Solution)
10:27
Intro CAPM
01:46
Capital Market Line (CML) & Two-Fund-Theorem
03:07
The Portfolio Diversification Effect
12:56
Systematic vs. unsystematic Risk
12:22
Capital Asset Pricing Model (CAPM) & Security Market Line (SLM)
07:47
Beta and Alpha
07:21
Redefining the Market Portfolio
07:29
Cyclical vs. non-cyclical Stocks - another Intuition on Beta
06:17
Coding Exercise 14 (Intro)
00:04
Coding Exercise 14 (Solution)
09:01

--- PART 5: REGRESSION ANALYSIS (A MUST-HAVE FOR MACHINE LEARNING) ---

3 lectures
Introduction to Regression Analysis
04:34
Coding Projects Part 5 - Overview
02:15
Download of Part 5 Course Materials ***Updated May 2023***
00:04

Correlation and Regression

13 lectures
Cleaning and preparing the Data - Movies Database (Part 1)
06:42
Cleaning and preparing the Data - Movies Database (Part 2)
05:42
Covariance and Correlation Coefficient (Theory)
06:50
How to calculate Covariance and Correlation in Python
05:02
Correlation and Scatterplots – visual Interpretation
04:59
Creating a Confidence Interval for the Correlation Coefficient (Bootstrapping)
07:54
Testing for Correlation (t-Test)
03:27
What is Linear Regression? (Theory)
02:47
A simple Linear Regression Model with numpy & Scipy
06:48
How to interpret Intercept and Slope Coefficient
02:44
Case Study (Part 1): The Market Model (Single Factor Model)
05:11
Case Study (Part 2): The Market Model (Single Factor Model)
02:22
Coding Exercise 1
00:04

OLS Regression, ANOVA and Hypothesis Testing

12 lectures
OLS (Ordinary Least Squares) Regression (Theory)
02:15
OLS Regression with statsmodels - Intro
09:58
OLS Regression - ANOVA (Theory)
08:58
OLS Regression with Statsmodels - ANOVA
03:18
Coefficient of Determination (R squared)
01:48
OLS Regression with statsmodels and DataFrames
04:17
Confidence Intervals for Regression Coefficients - Bootstrapping
10:42
Hypothesis Testing of Regression Coefficients (Theory)
03:50
Hypothesis Testing of Regression Coefficients with statsmodels
04:09
Regression Analysis with statsmodels - the Summary Table
03:48
Case Study (Part 3): The Market Model (Single Factor Model)
05:13
Coding Exercise 2
00:04

Multiple Regression Models

9 lectures
Multiple Regression (Theory)
06:15
Movies Dataset - Preparing the Data
07:42
Multiple Regression Analysis with statsmodels
05:05
Coefficient of Determination (Adjusted R squared)
02:52
Regression Coefficients, Hypothesis Testing & Model Specification
08:37
How to test the Significance of the Model as a whole (F-Test)
04:57
Creating and working with Dummy Variables (Part 1)
08:19
Creating and working with Dummy Variables (Part 2)
06:56
Coding Exercise 3
00:04

Case Study: Multi-Factor Models (Fama-French)

8 lectures
Fama-French: An Introduction
13:12
Single-Factor Models with the Fama-French Market Portfolio (Part 1)
08:39
Single-Factor Models with the Fama-French Market Portfolio (Part 2)
06:16
The Factors Size & Value
09:00
How to create a Fama-French Three-Factor Model
07:29
The Factors Profitability and Investment
04:57
How to create a Fama-French Five-Factor Model
06:01
Coding Exercise 4
00:04

Issues in Linear Regression Analysis and Logistic Regression

10 lectures
Linear Regression - not that easy!
04:49
Detecting and Handling Outliers (Part 1)
09:32
Detecting and Handling Outliers (Part 2)
02:41
Non-Linear Relationships - Feature Transformation
04:32
Detecting and Handling Multicollinearity
06:53
Detecting and Correcting Heteroskedasticity
08:42
Detecting and Handling Serial Correlation (Autocorrelation)
11:39
Logistic Regression (Theory)
03:49
Logistic Regression with statsmodels (Part 1)
05:10
Logistic Regression with statsmodels (Part 2)
06:37

Extra Section: Introduction to Object Oriented Programming (OOP)

17 lectures
Downloads for this Section ***Updated May 2023***
00:04
Introduction to OOP and examples for Classes
10:58
The FinancialInstrument Class live in action (Part 1)
04:58
The FinancialInstrument Class live in action (Part 2)
03:42
The special method __init__()
08:28
The method get_data()
06:49
The method log_returns()
03:21
String representation and the special method __repr__()
03:41
The methods plot_prices() and plot_returns()
05:21
Encapsulation and protected Attributes
04:02
The method set_ticker()
03:18
Adding more methods and performance metrics
05:51
Inheritance
09:01
Inheritance and the super() Function
06:47
Adding meaningful Docstrings
06:24
Creating and Importing Python Modules (.py)
04:19
Coding Exercise: Create your own Class
07:13

What´s next? (outlook and additional resources)

1 lectures
Bonus Lecture
05:52

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