Complete 2in1 Python for Business and Finance Bootcamp

Complete 2in1 Python for Business and Finance Bootcamp

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 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

Requirements

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)

Description

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 SimulationsPandas to handle, process, clean, aggregate and manipulate Tabular (Financial) Data. You deserve more than just Excel!statsmodels to perform Regression Analysis, Hypothesis Testing and ANOVAMatplotlib and Seaborn for scientific Data VisualizationThis course isn´t just videos:Downloadable Jupyter Notebooks with thousands of lines of codeDownloadable PDF Files containing hundreds of slides explaining and repeating the most important concepts Downloadable Jupyter Notebook with hundreds of coding exercises incl. hints and solutionsI 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!

Overview

Section 1: Getting Started

Lecture 1 Tips: How to get the most out of this Course (don´t skip!)

Lecture 2 FAQ / Your Questions answered

Lecture 3 How to download and install Anaconda for Python coding

Lecture 4 Jupyter Notebooks – let´s get started

Lecture 5 How to work with Jupyter Notebooks

Section 2: —- PART 1: PYTHON BASICS, TIME VALUE OF MONEY AND CAPITAL BUDGETING —-

Lecture 6 Overview & Download of Course Materials for Part 1

Lecture 7 Coding Projects Part 1 – Overview

Section 3: How to use Python as a Calculator for basic Time Value of Money Problems

Lecture 8 Intro to the Time Value of Money (TVM) Concept (Theory)

Lecture 9 Calculate Future Values (FV) with Python / Compounding

Lecture 10 ***NEW*** Udemy Online Coding Exercises – Intro

Lecture 11 Calculate Present Values (PV) with Python / Discounting

Lecture 12 Interest Rates and Returns (Theory)

Lecture 13 Calculate Interest Rates and Returns with Python

Lecture 14 Introduction to Variables

Lecture 15 Excursus: How to add inline comments

Lecture 16 Variables and Memory (Theory)

Lecture 17 More on Variables and Memory

Lecture 18 Variables – Dos, Don´ts and Conventions

Lecture 19 The print() Function

Lecture 20 Coding Exercise 1

Section 4: How to use Lists and For Loops for TVM Problems with many Cashflows

Lecture 21 TVM Problems with many Cashflows

Lecture 22 Intro to Python Lists

Lecture 23 Zero-based Indexing and negative Indexing in Python (Theory)

Lecture 24 Indexing Lists

Lecture 25 For Loops – Iterating over Lists

Lecture 26 The range Object – another Iterable

Lecture 27 Calculate FV and PV for many Cashflows

Lecture 28 The Net Present Value – NPV (Theory)

Lecture 29 Calculate an Investment Project´s NPV

Lecture 30 Coding Exercise 2

Section 5: 100% Python: Objects, Data Types, Operators & Functional Programming

Lecture 31 Data Types in Action

Lecture 32 The Data Type Hierarchy (Theory)

Lecture 33 Excursus: Dynamic Typing in Python

Lecture 34 Build-in Functions

Lecture 35 Integers

Lecture 36 Floats

Lecture 37 How to round Floats (and Integers) with round()

Lecture 38 More on Lists

Lecture 39 Lists and Element-wise Operations

Lecture 40 Slicing Lists

Lecture 41 Slicing Cheat Sheet

Lecture 42 Changing Elements in Lists

Lecture 43 Sorting and Reversing Lists

Lecture 44 Adding and removing Elements from/to Lists

Lecture 45 Mutable vs. immutable Objects (Part 1)

Lecture 46 Mutable vs. immutable Objects (Part 2)

Lecture 47 Coding Exercise 3

Lecture 48 Tuples

Lecture 49 Dictionaries

Lecture 50 Intro to Strings

Lecture 51 String Replacement

Lecture 52 Booleans

Lecture 53 Operators (Theory)

Lecture 54 Comparison, Logical and Membership Operators in Action

Lecture 55 Coding Exercise 4

Section 6: How to solve for IRR & YTM with While Loops and Conditional Statements

Lecture 56 Conditional Statements

Lecture 57 Keywords pass, continue and break

Lecture 58 Calculate a Project´s Payback Period

Lecture 59 While Loops

Lecture 60 The Internal Rate of Return – IRR (Theory)

Lecture 61 Solving for a Project´s IRR

Lecture 62 Bonds and the Yield to Maturity – YTM (Theory)

Lecture 63 Solving for a Bond´s Yield to Maturity (YTM)

Lecture 64 Coding Exercise 5

Section 7: How to create great graphs with Matplotlib – Plotting NPV and IRR

Lecture 65 Intro

Lecture 66 Line Plots

Lecture 67 Scatter Plots

Lecture 68 Customizing Plots (Part 1)

Lecture 69 Customizing Plots (Part 2)

Lecture 70 Plotting NPV & IRR

Lecture 71 Coding Exercise 6

Section 8: The Numpy Package: Working with numbers made easy!

Lecture 72 Modules, Packages and Libraries – No need to reinvent the Wheel

Lecture 73 Numpy Arrays

Lecture 74 Indexing and Slicing Numpy Arrays

Lecture 75 Vectorized Operations with Numpy Arrays

Lecture 76 Changing Elements in Numpy Arrays & Mutability

Lecture 77 View vs. copy – potential Pitfalls when slicing Numpy Arrays

Lecture 78 Numpy Array Methods and Attributes

Lecture 79 Numpy Universal Functions

Lecture 80 Boolean Arrays and Conditional Filtering

Lecture 81 Advanced Filtering & Bitwise Operators

Lecture 82 Determining a Project´s Payback Period with np.where()

Lecture 83 Creating Numpy Arrays from Scratch

Lecture 84 Coding Exercise 7

Section 9: How to solve complex TVM and Capital Budgeting problems with Python and Numpy

Lecture 85 Evaluating Investments with npf.npv() and npf.irr()

Lecture 86 Evaluating Annuities with npf.fv() – Funding Phase

Lecture 87 Evaluating Annuities with npf.fv() – Payout Phase

Lecture 88 How to solve for annuity payments with npf.pmt()

Lecture 89 How to solve for the number of periodic payments with npf.nper()

Lecture 90 How to calculate the required Contract Value with npf.pv()

Lecture 91 Frequency of compounding and the effective annual interest rate

Lecture 92 How to evaluate a Retirement Plan A-Z

Lecture 93 Retirement Plan: Sensitivity Analysis

Lecture 94 Mortgage Loan Analysis – Debt Sizing

Lecture 95 Mortgage Loan Analysis – Interest Payments and Amortization Schedule

Lecture 96 Calculate PV of equal installments with npf.pv() – Valuation of Bonds

Lecture 97 Capital Budgeting – Mutually exclusive Projects (Part 1)

Lecture 98 Capital Budgeting – Mutually exclusive Projects (Part 2)

Lecture 99 Capital Budgeting – Mutually exclusive Projects (Part 3)

Lecture 100 Coding Exercise 8

Section 10: — PART 2: STATISTICS AND HYPOTHESIS TESTING WITH PYTHON, NUMPY AND SCIPY —

Lecture 101 Statistics – Overview, Terms and Vocabulary

Lecture 102 Coding Projects Part 2 – Overview

Lecture 103 Download of Part 2 Course Materials

Section 11: How to perform Descriptive Statistics on Populations and Samples

Lecture 104 Population vs. Sample

Lecture 105 Visualizing Frequency Distributions with plt.hist()

Lecture 106 Relative and Cumulative Frequencies with plt.hist()

Lecture 107 Measures of Central Tendency (Theory)

Lecture 108 Coding Measures of Central Tendency – Mean and Median

Lecture 109 Coding Measures of Central Tendency – Geometric Mean

Lecture 110 Excursus: Why Log Returns are useful

Lecture 111 Variability around the Central Tendency / Dispersion (Theory)

Lecture 112 Minimum, Maximum and Range with Python/Numpy

Lecture 113 Variance and Standard Deviation with Python/Numpy

Lecture 114 Percentiles with Python/Numpy

Lecture 115 Skew and Kurtosis (Theory)

Lecture 116 How to calculate Skew and Kurtosis with scipy.stats

Lecture 117 Coding Exercise 1

Section 12: Common Probability Distributions and how to construct Confidence Intervals

Lecture 118 How to generate Random Numbers with Numpy

Lecture 119 Reproducibility with np.random.seed()

Lecture 120 Probability Distributions – Overview

Lecture 121 Discrete Uniform Distributions

Lecture 122 Continuous Uniform Distributions

Lecture 123 The Normal Distribution (Theory)

Lecture 124 Creating a normally distributed Random Variable

Lecture 125 Normal Distribution – Probability Density Function (pdf) with scipy.stats

Lecture 126 Normal Distribution – Cumulative Distribution Function (cdf) with scipy.stats

Lecture 127 The Standard Normal Distribution and Z-Values

Lecture 128 Properties of the Standard Normal Distribution (Theory)

Lecture 129 Probabilities and Z-Values with scipy.stats

Lecture 130 Confidence Intervals with scipy.stats

Lecture 131 Coding Exercise 2

Section 13: How to estimate Population parameters with Samples – Sampling and Estimation

Lecture 132 Sample Statistic, Sampling Error and Sampling Distribution (Theory)

Lecture 133 Sampling with np.random.choice()

Lecture 134 Sampling Distribution

Lecture 135 Standard Error

Lecture 136 Central Limit Theorem (Coding Part 1)

Lecture 137 Central Limit Theorem (Coding Part 2)

Lecture 138 Central Limit Theorem (Theory)

Lecture 139 Point Estimates vs. Confidence Interval Estimates (known Population Variance)

Lecture 140 The Student´s t-distribution: What is it and why/when do we use it?

Lecture 141 Unknown Population Variance – the Standard Case (Example 1)

Lecture 142 Unknown Population Variance – the Standard Case (Example 2)

Lecture 143 Student´s t-Distribution vs. Normal Distribution with scipy.stats

Lecture 144 Bootstrapping with Python: an alternative method without Statistics

Lecture 145 Coding Exercise 3

Section 14: How to perform Hypothesis Tests: Z-Tests, t-Tests, Bootstrapping & more

Lecture 146 Hypothesis Testing (Theory)

Lecture 147 Two-tailed Z-Test with known Population Variance

Lecture 148 What is the p-value? (Theory)

Lecture 149 Calculating and interpreting z-statistic and p-value with scipy.stats

Lecture 150 One-tailed Z-Test with known Population Variance

Lecture 151 Two-tailed t-Test (unknown Population Variance)

Lecture 152 One-tailed t-Test (unknown Population Variance)

Lecture 153 Hypothesis Testing with Bootstrapping

Lecture 154 Testing for Normality of Financial Returns with scipy.stats

Lecture 155 Coding Exercise 4

Section 15: — PART 3: ADVANCED PYTHON, MONTE CARLO SIMULATIONS AND VALUE AT RISK (VAR) —

Lecture 156 *Update Notice (June 2021)*

Lecture 157 Overview & Download of Course Materials for Part 3

Lecture 158 Coding Projects Part 3 – Overview

Section 16: n-dimensional Numpy Arrays / How to work with numerical Tabular Data

Lecture 159 How to work with nested Lists

Lecture 160 2-dimensional Numpy Arrays

Lecture 161 How to slice 2-dim Numpy Arrays (Part 1)

Lecture 162 How to slice 2-dim Numpy Arrays (Part 2)

Lecture 163 Recap: Changing Elements in a Numpy Array / slice

Lecture 164 How to perform row-wise and column-wise Operations

Lecture 165 Reshaping and Transposing 2-dim Numpy Arrays

Lecture 166 Creating 2-dim Numpy Arrays from Scratch

Lecture 167 Arithmetic & Vectorized Operations with 2-dim Numpy Arrays

Lecture 168 The keepdims parameter

Lecture 169 Adding & Removing Elements

Lecture 170 Merging and Concatenating Numpy Arrays

Lecture 171 Coding Exercise 1

Section 17: How to create your own user-defined Functions

Lecture 172 Defining your first user-defined Function

Lecture 173 What´s the difference between Positional Arguments vs. Keyword Arguments?

Lecture 174 How to work with Default Arguments

Lecture 175 The Default Argument None

Lecture 176 How to unpack Iterables

Lecture 177 Sequences as arguments and *args

Lecture 178 How to return many results

Lecture 179 Scope – easily explained

Lecture 180 How to create Nested Functions

Lecture 181 Putting it all together – Case Study

Lecture 182 Coding Exercise 2

Section 18: Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy

Lecture 183 What is the Value-at-Risk (VaR)? (Theory)

Lecture 184 Analyzing the Data / past Performance

Lecture 185 How to use the Parametric Method to calculate Value-at-Risk (VaR)

Lecture 186 How to use the Historical Method to calculate Value-at-Risk (VaR)

Lecture 187 Monte Carlo Simulations for Value-at-Risk – Parametric (Part 1)

Lecture 188 Monte Carlo Simulations for Value-at-Risk – Parametric (Part 2)

Lecture 189 Monte Carlo Simulations for Value-at-Risk – Parametric (Part 3)

Lecture 190 Monte Carlo Simulations for Value-at-Risk – Bootstrapping (Part 1)

Lecture 191 Monte Carlo Simulations for Value-at-Risk – Bootstrapping (Part 2)

Lecture 192 Conditional Value-at-Risk (CVaR)

Lecture 193 Dynamic & path-dependent Simulations (Part 1)

Lecture 194 Dynamic & path-dependent Simulations (Part 2)

Lecture 195 Dynamic & path-dependent Simulations (Part 3)

Lecture 196 Dynamic & path-dependent Simulations (Part 4)

Lecture 197 Coding Exercise 3

Section 19: — PART 4: MANAGING (FINANCIAL) DATA WITH PANDAS: BEYOND EXCEL —

Lecture 198 Introduction

Lecture 199 Download of Part 4 Course Materials

Lecture 200 Tabular Data and Pandas DataFrames

Section 20: Pandas Basics – Starting from Zero

Lecture 201 First Steps (Inspection of Data, Part 1)

Lecture 202 First Steps (Inspection of Data, Part 2)

Lecture 203 Built-in Functions, Attributes and Methods

Lecture 204 Explore your own Dataset: Coding Exercise 1 (Intro)

Lecture 205 Explore your own Dataset: Coding Exercise 1 (Solution)

Lecture 206 Selecting Columns

Lecture 207 Selecting Rows with Square Brackets (not advisable)

Lecture 208 Selecting Rows with iloc (position-based indexing)

Lecture 209 Slicing Rows and Columns with iloc (position-based indexing)

Lecture 210 Position-based Indexing Cheat Sheets

Lecture 211 Selecting Rows with loc (label-based indexing)

Lecture 212 Slicing Rows and Columns with loc (label-based indexing)

Lecture 213 Label-based Indexing Cheat Sheets

Lecture 214 Summary and Outlook

Lecture 215 Coding Exercise 2 (Intro)

Lecture 216 Coding Exercise 2 (Solution)

Section 21: Pandas Intermediate

Lecture 217 Intro

Lecture 218 First Steps with Pandas Series

Lecture 219 Analyzing Numerical Series with unique(), nunique() and value_counts()

Lecture 220 UPDATE Pandas Version 0.24.0 (Jan 2019)

Lecture 221 EXCURSUS: Updating Pandas / Anaconda

Lecture 222 Analyzing non-numerical Series with unique(), nunique(), value_counts()

Lecture 223 The copy() method

Lecture 224 Sorting of Series and Introduction to the inplace – parameter

Lecture 225 Coding Exercise 3 (Intro)

Lecture 226 Coding Exercise 3 (Solution)

Lecture 227 First Steps with Pandas Index Objects

Lecture 228 Changing Row Index with set_index() and reset_index()

Lecture 229 Changing Column Labels

Lecture 230 Renaming Index & Column Labels with rename()

Lecture 231 Coding Exercise 4 (Intro)

Lecture 232 Coding Exercise 4 (Solution)

Lecture 233 Sorting DataFrames with sort_index() and sort_values()

Lecture 234 nunique() and nlargest() / nsmallest() with DataFrames

Lecture 235 Filtering DataFrames (one Condition)

Lecture 236 Filtering DataFrames by many Conditions (AND)

Lecture 237 Filtering DataFrames by many Conditions (OR)

Lecture 238 Advanced Filtering with between(), isin() and ~

Lecture 239 any() and all()

Lecture 240 Coding Exercise 5 (Intro)

Lecture 241 Coding Exercise 5 (Solution)

Lecture 242 Intro to NA Values / missing Values

Lecture 243 Handling NA Values / missing Values

Lecture 244 Exporting DataFrames to csv

Lecture 245 Summary Statistics and Accumulations

Lecture 246 The agg() method

Lecture 247 Coding Exercise 6 (Intro)

Lecture 248 Coding Exercise 6 (Solution)

Section 22: Data Visualization with Pandas, Matplotlib and Seaborn

Lecture 249 Intro

Lecture 250 Visualization with Matplotlib (Intro)

Lecture 251 Customization of Plots

Lecture 252 Histogramms (Part 1)

Lecture 253 Histogramms (Part 2)

Lecture 254 Scatterplots

Lecture 255 First Steps with Seaborn

Lecture 256 Categorical Seaborn Plots

Lecture 257 Seaborn Regression Plots

Lecture 258 Seaborn Heatmaps

Lecture 259 Coding Exercise 7 (Intro)

Lecture 260 Coding Exercise 7 (Solution)

Section 23: Pandas Advanced

Lecture 261 Intro

Lecture 262 Removing Columns

Lecture 263 Removing Rows

Lecture 264 Adding new Columns to a DataFrame

Lecture 265 Arithmetic Operations (Part 1)

Lecture 266 Arithmetic Operations (Part 2)

Lecture 267 Creating DataFrames from Scratch with pd.DataFrame()

Lecture 268 Adding new Rows (Hands-on)

Lecture 269 Adding new Rows to a DataFrame

Lecture 270 Manipulating Elements in a DataFrame

Lecture 271 Coding Exercise 8 (Intro)

Lecture 272 Coding Exercise 8 (Solution)

Lecture 273 Introduction to GroupBy Operations

Lecture 274 Understanding the GroupBy Object

Lecture 275 Splitting with many Keys

Lecture 276 split-apply-combine

Lecture 277 split-apply-combine applied

Lecture 278 Hierarchical Indexing with Groupby

Lecture 279 stack() and unstack()

Lecture 280 Coding Exercise 9 (Intro)

Lecture 281 Coding Exercise 9 (Solution)

Section 24: Managing Time Series and Financial Data with Pandas

Lecture 282 Importing Time Series Data from csv-files

Lecture 283 Converting strings to datetime objects with pd.to_datetime()

Lecture 284 Initial Analysis / Visualization of Time Series

Lecture 285 Indexing and Slicing Time Series

Lecture 286 Creating a customized DatetimeIndex with pd.date_range()

Lecture 287 More on pd.date_range()

Lecture 288 Coding Exercise 10 (intro)

Lecture 289 Coding Exercise 10 (Solution)

Lecture 290 Downsampling Time Series with resample() (Part 1)

Lecture 291 Downsampling Time Series with resample (Part 2)

Lecture 292 The PeriodIndex object

Lecture 293 Advanced Indexing with reindex()

Lecture 294 Coding Exercise 11 (intro)

Lecture 295 Coding Exercise 11 (Solution)

Lecture 296 Getting Ready (Installing required library)

Lecture 297 Importing Stock Price Data from Yahoo Finance (it still works!)

Lecture 298 Initial Inspection and Visualization

Lecture 299 Normalizing Time Series to a Base Value (100)

Lecture 300 The shift() method

Lecture 301 The methods diff() and pct_change()

Lecture 302 Measuring Stock Performance with MEAN Returns and STD of Returns

Lecture 303 Financial Time Series – Return and Risk

Lecture 304 Financial Time Series – Covariance and Correlation

Lecture 305 Importing Financial Data from Excel

Lecture 306 Merging / Aligning Financial Time Series (hands-on)

Lecture 307 Coding Exercise 12 (intro)

Lecture 308 Coding Exercise 12 (Solution)

Section 25: Creating, analyzing and optimizing Financial Portfolios with Python

Lecture 309 Intro

Lecture 310 Getting the Data

Lecture 311 Creating the equally-weighted Portfolio

Lecture 312 Creating many random Portfolios with Python

Lecture 313 What is the Sharpe Ratio and a Risk Free Asset?

Lecture 314 Portfolio Analysis and the Sharpe Ratio with Python

Lecture 315 Finding the Optimal Portfolio

Lecture 316 Excursus: Portfolio Optimization with scipy

Lecture 317 Sharpe Ratio – visualized and explained

Lecture 318 Coding Exercise 13 (Intro)

Lecture 319 Coding Exercise 13 (Solution)

Lecture 320 Intro CAPM

Lecture 321 Capital Market Line (CML) & Two-Fund-Theorem

Lecture 322 The Portfolio Diversification Effect

Lecture 323 Systematic vs. unsystematic Risk

Lecture 324 Capital Asset Pricing Model (CAPM) & Security Market Line (SLM)

Lecture 325 Beta and Alpha

Lecture 326 Redefining the Market Portfolio

Lecture 327 Cyclical vs. non-cyclical Stocks – another Intuition on Beta

Lecture 328 Coding Exercise 14 (Intro)

Lecture 329 Coding Exercise 14 (Solution)

Section 26: — PART 5: REGRESSION ANALYSIS (A MUST-HAVE FOR MACHINE LEARNING) —

Lecture 330 Introduction to Regression Analysis

Lecture 331 Coding Projects Part 5 – Overview

Lecture 332 Download of Part 5 Course Materials

Section 27: Correlation and Regression

Lecture 333 Cleaning and preparing the Data – Movies Database (Part 1)

Lecture 334 Cleaning and preparing the Data – Movies Database (Part 2)

Lecture 335 Covariance and Correlation Coefficient (Theory)

Lecture 336 How to calculate Covariance and Correlation in Python

Lecture 337 Correlation and Scatterplots – visual Interpretation

Lecture 338 Creating a Confidence Interval for the Correlation Coefficient (Bootstrapping)

Lecture 339 Testing for Correlation (t-Test)

Lecture 340 What is Linear Regression? (Theory)

Lecture 341 A simple Linear Regression Model with numpy & Scipy

Lecture 342 How to interpret Intercept and Slope Coefficient

Lecture 343 Case Study (Part 1): The Market Model (Single Factor Model)

Lecture 344 Case Study (Part 2): The Market Model (Single Factor Model)

Lecture 345 Coding Exercise 1

Section 28: OLS Regression, ANOVA and Hypothesis Testing

Lecture 346 OLS (Ordinary Least Squares) Regression (Theory)

Lecture 347 OLS Regression with statsmodels – Intro

Lecture 348 OLS Regression – ANOVA (Theory)

Lecture 349 OLS Regression with Statsmodels – ANOVA

Lecture 350 Coefficient of Determination (R squared)

Lecture 351 OLS Regression with statsmodels and DataFrames

Lecture 352 Confidence Intervals for Regression Coefficients – Bootstrapping

Lecture 353 Hypothesis Testing of Regression Coefficients (Theory)

Lecture 354 Hypothesis Testing of Regression Coefficients with statsmodels

Lecture 355 Regression Analysis with statsmodels – the Summary Table

Lecture 356 Case Study (Part 3): The Market Model (Single Factor Model)

Lecture 357 Coding Exercise 2

Section 29: Multiple Regression Models

Lecture 358 Multiple Regression (Theory)

Lecture 359 Movies Dataset – Preparing the Data

Lecture 360 Multiple Regression Analysis with statsmodels

Lecture 361 Coefficient of Determination (Adjusted R squared)

Lecture 362 Regression Coefficients, Hypothesis Testing & Model Specification

Lecture 363 How to test the Significance of the Model as a whole (F-Test)

Lecture 364 Creating and working with Dummy Variables (Part 1)

Lecture 365 Creating and working with Dummy Variables (Part 2)

Lecture 366 Coding Exercise 3

Section 30: Case Study: Multi-Factor Models (Fama-French)

Lecture 367 Fama-French: An Introduction

Lecture 368 Single-Factor Models with the Fama-French Market Portfolio (Part 1)

Lecture 369 Single-Factor Models with the Fama-French Market Portfolio (Part 2)

Lecture 370 The Factors Size & Value

Lecture 371 How to create a Fama-French Three-Factor Model

Lecture 372 The Factors Profitability and Investment

Lecture 373 How to create a Fama-French Five-Factor Model

Lecture 374 Coding Exercise 4

Section 31: Issues in Linear Regression Analysis and Logistic Regression

Lecture 375 Linear Regression – not that easy!

Lecture 376 Detecting and Handling Outliers (Part 1)

Lecture 377 Detecting and Handling Outliers (Part 2)

Lecture 378 Non-Linear Relationships – Feature Transformation

Lecture 379 Detecting and Handling Multicollinearity

Lecture 380 Detecting and Correcting Heteroskedasticity

Lecture 381 Detecting and Handling Serial Correlation (Autocorrelation)

Lecture 382 Logistic Regression (Theory)

Lecture 383 Logistic Regression with statsmodels (Part 1)

Lecture 384 Logistic Regression with statsmodels (Part 2)

Section 32: Extra Section: Introduction to Object Oriented Programming (OOP)

Lecture 385 Downloads for this Section

Lecture 386 Introduction to OOP and examples for Classes

Lecture 387 The FinancialInstrument Class live in action (Part 1)

Lecture 388 The FinancialInstrument Class live in action (Part 2)

Lecture 389 The special method __init__()

Lecture 390 The method get_data()

Lecture 391 The method log_returns()

Lecture 392 String representation and the special method __repr__()

Lecture 393 The methods plot_prices() and plot_returns()

Lecture 394 Encapsulation and protected Attributes

Lecture 395 The method set_ticker()

Lecture 396 Adding more methods and performance metrics

Lecture 397 Inheritance

Lecture 398 Inheritance and the super() Function

Lecture 399 Adding meaningful Docstrings

Lecture 400 Creating and Importing Python Modules (.py)

Lecture 401 Coding Exercise: Create your own Class

Section 33: What´s next? (outlook and additional resources)

Lecture 402 Bonus Lecture

All Business and Finance Professionals (Python is the future),Python Developers / Computer Scientists who want to step into Business, Finance & Data Science Roles,Researchers who need to analyze large data sets and perform statistical & regression analysis,Everyone who want to complement/replace Excel at work to increase productivity,Everyone who want to get the full picture: Coding and underlying Theory (Statistics, Regression, Finance)

Course Information:

Udemy | English | 37h 30m | 13.15 GB
Created by: Alexander Hagmann

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