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Manage Finance Data with Python & Pandas: Unique Masterclass

Manage Finance Data with Python & Pandas: Unique Masterclass

Loại khoá học: Programming Languages

Analyze Stocks with Pandas, Numpy, Seaborn & ChatGPT. Create, analyze & optimize Index & Portfolios (CAPM, Alpha, Beta)

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

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Mô tả

**Fully Updated (Pandas 2.1) in November 2023**

**Now with ChatGPT for Pandas & Data Analytics and Online Coding Exercises!**


The Finance and Investment Industry is experiencing a dramatic change driven by ever-increasing processing power & connectivity and the introduction of powerful Machine Learning tools. The Finance and Investment Industry is more and more shifting from a math/formula-based business to a data-driven business.


What can you do to keep pace?

No matter if you want to dive deep into Machine Learning, or if you simply want to increase productivity at work when handling Financial Data, there is the very first and most important step: Leave Excel behind and manage your Financial Data with Python and Pandas!

Pandas is the Excel for Python and learning Pandas from scratch is almost as easy as learning Excel. Pandas seems to be more complex at a first glance, as it simply offers so much more functionalities. The workflows you are used to do with Excel can be done with Pandas more efficiently. Pandas is a high-level coding library where all the hardcore coding stuff with dozens of coding lines are running automatically in the background. Pandas operations are typically done in one line of code! However, it is important to learn and master Pandas in a way that

  • you understand what is going on

  • you are aware of the pitfalls (Don´ts)

  • you know best practices (Dos)   


MANAGE FINANCE DATA WITH PYTHON & PANDAS best prepares you to master new challenges and to stay ahead of your peers, fellows and competitors! Coding with Python/Pandas is one of the most in-demand skills in Finance.

This course is one of the most practical courses on Udemy with 200 Coding Exercises and a Final Project. You are free to select your individual level of difficulty. If you have no experience with Pandas at all, Part 1 will teach you all the essentials (From Zero to Hero).

Part 2 - The Core of this Course

  • Import Financial Data from Free Web Sources, Excel- and CSV-Files

  • Calculate Risk, Return, and Correlation of Stocks, Indexes and Portfolios

  • Calculate simple Returns, log Returns, and annualized Returns & Risk

  • Create your own customized Financial Index (price-weighted vs. equal-weighted vs. value-weighted)

  • Understand the difference between Price Return and Total Return

  • Create, analyze and optimize Stock Portfolios

  • Calculate Sharpe Ratio, Systematic Risk, Unsystematic Risk, Beta and Alpha for Stocks, Indexes and Portfolios

  • Understand Modern Portfolio Theory, Risk Diversification and the Capital Asset Pricing Model (CAPM)

  • Forward-looking Mean-Variance Optimization (MVO) and its pitfalls

  • Get an exclusive insight into how MVO is used in Real World (and why it is NOT used in many cases) -> get beyond Investments 101 level!

  • Calculate Rolling Statistics (e.g. Simple Moving Averages) and aggregate, visualize and report Financial Performance

  • Create Interactive Charts with Technical Indicators (SMA, Candle Stick, Bollinger Bands etc.)

Part 3 - Capstone Project

Step into the Financial Analyst / Advisor Role and give advice on a Client´s Portfolio (Final Project Challenge).

Apply and master what you have learned before!

Part 4

Some advanced topics on handling Time Series Data with Pandas.

Appendix

Do you struggle with some basic Python / Numpy concepts? Here is all you need to know if you are completely new to Python!


Why you should listen to me...

In my career, I have built an extensive level of expertise and experience in both areas:  Finance and Coding

Finance:

  • 10 years experience in the Finance and Investment Industry...

  • ...where I held various quantitative & strategic positions.

  • MSc in Finance

  • Passed all three CFA Exams (currently no active member of the CFA Institute)

Python & Pandas:

  • I led a company-wide transformation from Excel to Python/Pandas

  • Code, models, and workflows are Real World Project-proven

  • Instructor of the highest-rated and most trending general Course on Pandas


What are you waiting for? Guaranteed Satisfaction: Otherwise, get your money back with a 30-Days-Money-Back-Guarantee.

Looking Forward to seeing you in the Course!

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Nội dung khoá học

25 sections

Getting Started

8 lectures
Course Overview and how to maximize your learning success
10:05
Tips: How to get the most out of this Course (don´t skip!)
05:27
Did you know that...?
03:08
FAQ / Important Information
02:37
Installation of Anaconda
06:15
Opening a Jupyter Notebook
12:24
How to use Jupyter Notebooks
17:25
Downloads (Get all Course Materials here!) **UPD Nov 23**
05:04

-- PART 1: DATA ANALYSIS WITH PYTHON & PANDAS: FROM ZERO TO HERO --

2 lectures
Intro to Tabular Data / Pandas
04:19
Download Course Materials Part 1 (Reminder)
00:06

**NEW** Pandas Coding with your personal assistant - ChatGPT

3 lectures
Introduction
03:22
Coding assistance for Pandas Coding using GPT 3.5 (free)
04:45
Pandas Data Analysis using GPT 4 (Plus Subscription)
03:28

Pandas Basics

32 lectures
Create your very first Pandas DataFrame (from csv)
06:58
Loading a CSV-file into Pandas
1 question
How to read CSV-files from other Locations
03:36
Pandas Display Options and the methods head() & tail()
06:41
First Data Inspection
11:25
Summary Statistics
1 question
Built-in Functions, Attributes and Methods with Pandas
12:06
Make it easy: TAB Completion and Tooltip
08:57
First Steps
3 questions
Explore your own Dataset: Jupyter Coding Exercise 1 (Intro)
03:53
Explore your own Dataset: Jupyter Coding Exercise 1 (Solution)
04:35
Selecting Columns
06:05
Selecting one Column with the "dot notation"
02:16
Selecting Columns
1 question
Zero-based Indexing and Negative Indexing
03:04
Selecting Rows with iloc (position-based indexing)
10:07
Slicing Rows and Columns with iloc (position-based indexing)
04:39
Position-based Indexing Cheat Sheets
00:02
Position-based Indexing 1
1 question
Position-based Indexing 2
1 question
Selecting Rows with loc (label-based indexing)
03:14
Slicing Rows and Columns with loc (label-based indexing)
10:21
Label-based Indexing Cheat Sheets
00:02
Label-based Indexing 1
1 question
Label-based Indexing 2
1 question
Indexing and Slicing with reindex()
05:30
Summary, Best Practices and Outlook
06:30
Indexing and Slicing
6 questions
Jupyter Coding Exercise 2 - Intro
01:03
Jupyter Coding Exercise 2 - Solution
06:31
**NEW** Coding Exercises with ChatGPT
03:14
Advanced Indexing and Slicing (optional)
05:22

Excursus: How to avoid and debug Coding Errors (incl. ChatGPT)

17 lectures
Introduction
02:59
Test your debugging skills!
10:40
Major reasons for Coding Errors
01:12
The most commonly made Errors at a glance
05:37
Omitting cells, changing the sequence and more
06:58
IndexErrors
04:49
Indentation Errors
03:18
Misuse of function names and keywords
02:32
TypeErrors and ValueErrors
03:41
**NEW** Debugging Pandas Errors with ChatGPT
05:46
Getting help on StackOverflow.com
06:23
How to traceback more complex Errors
10:22
Problems with the Python Installation
06:15
External Factors and Issues
04:13
Errors related to the course content (Transcription Errors)
04:11
Summary and Debugging Flow-Chart
07:15
**NEW** The Debugging Flow-Chart with ChatGPT
01:24

Pandas: Intermediate Topics

45 lectures
Intro
00:13
First Steps with Pandas Series
03:53
Analyzing Numerical Series with unique(), nunique() and value_counts()
13:50
Maximum Value in a numerical column
1 question
Most common Value in a numerical Column
1 question
Analyzing non-numerical Series with unique(), nunique(), value_counts()
07:17
Unique Values in a Text Column
1 question
Most common value in a Text Column
1 question
The copy() method
03:57
Sorting of Series and Introduction to the inplace - parameter
08:59
Sorting "inplace"
1 question
Pandas Series
3 questions
Coding Exercise 3 (Intro)
01:30
Coding Exercise 3 (Solution)
04:53
First Steps with Pandas Index Objects
05:57
Selecting Column Labels of a DataFrame
1 question
Changing Row Index with set_index() and reset_index()
10:07
Resetting an Index
1 question
Changing Column Labels
03:20
Renaming Index & Column Labels with rename()
03:51
Renaming Column Labels
1 question
Pandas Index Objects
3 questions
Coding Exercise 4 (Intro)
01:11
Coding Exercise 4 (Solution)
03:42
Sorting DataFrames with sort_index() and sort_values()
09:09
nunique() and nlargest() / nsmallest() with DataFrames
05:30
Filtering DataFrames (one Condition)
10:20
Filtering with one Condition
1 question
Filtering DataFrames by many Conditions (AND)
04:45
Filtering DataFrames by many Conditions (OR)
05:04
Filtering with many Conditions
1 question
Advanced Filtering with between(), isin() and ~
08:35
Advanced Filtering
1 question
any() and all()
04:07
Search with any()
1 question
Sorting and Filtering
2 questions
Coding Exercise 5 (Intro)
01:19
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
08:32
The agg() method
04:22
Coding Exercise 6 (Intro)
01:50
Coding Exercise 6 (Solution)
10:21

Data Visualization with 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)
01:02
Coding Exercise 7 (Solution)
07:30

Pandas: Advanced Topics

24 lectures
Intro
00:08
Removing Columns
05:18
Removing Rows
07:06
Removing Columns
1 question
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
Creating new DataFrames
1 question
Adding new Rows (Hands-on)
04:16
Adding new Rows to a DataFrame
13:51
Manipulating Elements in a DataFrame
04:42
Coding Exercise 8 (Intro)
00:59
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
GroupBy
4 questions
Coding Exercise 9 (Intro)
00:56
Coding Exercise 9 (Solution)
06:05

----- PART 2: FINANCIAL DATA ANALYSIS ------

2 lectures
Welcome
00:31
Download Course Materials Part 2 (reminder)
00:06

Time Series Data in Pandas: Introduction

14 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)
01:14
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)
01:12
Coding Exercise 11 (Solution)
05:30

Financial Data - Essential Workflows (Risk, Return & Correlation)

13 lectures
Intro
00:25
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
Risk & Return
3 questions
Financial Time Series - Covariance and Correlation
04:32
Coding Exercise 12 (intro)
02:30
Coding Exercise 12 (Solution)
07:28

Financial Data - Advanced Techniques (Rolling Statistics & Reporting)

22 lectures
Intro
02:50
Update: Preps for pd.read_excel()
00:09
Importing Financial Data from Excel
11:25
Simple Moving Averages (SMA) with rolling()
08:44
Momentum Trading Strategies with SMAs
07:08
Trading Strategies
2 questions
S&P 500 Performance Reporting - rolling risk and return
11:25
S&P 500: Investment Horizon and Performance
09:42
Simple Returns vs. Log Returns
09:18
Simple Returns vs. Log Returns
3 questions
The S&P 500 Return Triangle (Part 1)
06:19
The S&P 500 Return Triangle (Part 2)
09:20
Interpreting the Return Triangle
2 questions
The S&P 500 Dollar Triangle
04:08
The S&P 500 "Weather Radar"
04:42
Exponentially-weighted Moving Averages (EWMA)
04:32
Expanding Windows
05:07
Rolling Correlation
07:11
rollling() with fixed-sized time offsets
06:13
Merging / Aligning Financial Time Series (hands-on)
05:02
Coding Exercise 13 (intro)
02:38
Coding Exercise 13 (Solution)
12:31

Create and Analyze Financial Indexes

17 lectures
Financial Indexes - an Overview
09:13
Financial Indexes
3 questions
Getting the Data
03:29
Price-Weighted Index - Theory
08:21
PWI
4 questions
Creating a Price-Weighted Stock Index with Python
08:49
Equal-Weighted Index - Theory
05:37
EWI
4 questions
Creating an Equal-Weighted Stock Index with Python
07:59
Market Value-Weighted Index - Theory
08:41
VWI
4 questions
Creating a Market Value-Weighted Stock Index with Python (Part 1)
08:00
Creating a Market Value-Weighted Stock Index with Python (Part 2)
07:20
Comparison of weighting methods
05:26
Price Index vs. Performance/Total Return Index
06:13
Coding Exercise 14 (intro)
01:34
Coding Exercise 14 (Solution)
07:37

Create, Analyze and Optimize Financial Portfolios

13 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
Sharpe Ratio and Risk Free Asset
3 questions
Finding the Optimal Portfolio
06:51
Excursus: Portfolio Optimization with scipy
00:03
Sharpe Ratio - visualized and explained
05:07
Portfolios
3 questions
Coding Exercise 15 (Intro)
02:45
Coding Exercise 15 (Solution)
10:27

Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)

14 lectures
Intro
01:46
Capital Market Line (CML) & Two-Fund-Theorem
03:07
Two-Fund-Theorem
2 questions
The Portfolio Diversification Effect
12:56
Systematic vs. unsystematic Risk
12:22
Risk Diversification
5 questions
Capital Asset Pricing Model (CAPM) & Security Market Line (SLM)
07:47
CAPM
3 questions
Beta and Alpha
07:21
Redefining the Market Portfolio
07:32
Cyclical vs. non-cyclical Stocks - another Intuition on Beta
06:17
Beta and Alpha
3 questions
Coding Exercise 16 (Intro)
02:34
Coding Exercise 16 (Solution)
09:01

Forward-looking Mean-Variance Optimization & Asset Allocation

5 lectures
Intro
04:38
Mean-Variance Optimization (MVO)
08:51
It´s not that simple - Part 1 (Investments 101 vs. Real World)
06:58
Changing Expected Returns
06:25
It´s not that simple - Part 2 (Investments 101 vs. Real World)
10:37

Interactive Financial Charts with Plotly and Cufflinks

10 lectures
Intro
01:34
Getting Ready (Installing required libraries)
01:53
Creating Offline Graphs in Jupyter Notebooks
07:36
Interactive Price Charts with Plotly
05:07
Customizing Plotly Charts
05:15
Interactive Histograms with Plotly
06:02
Candle-Stick and OHLC Charts with Plotly
04:22
SMA and Bollinger Bands with Plotly
06:05
More Technical Indicators with Plotly (Volume, MACD, DMI)
01:53
Coding Exercise 17
00:10

----- PART 3: THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----

11 lectures
Download Course Materials Part 3 (reminder)
00:06
Financial Analyst Challenge (Intro)
01:22
Financial Analyst Challenge (Instruction & Hints)
03:26
Financial Analyst Challenge (Solution Part 1)
02:00
Financial Analyst Challenge (Solution Part 2)
07:07
Financial Analyst Challenge (Solution Part 3)
06:08
Financial Analyst Challenge (Solution Part 4)
04:53
Financial Analyst Challenge (Solution Part 5)
04:53
Financial Analyst Challenge (Solution Part 6)
07:31
Financial Analyst Challenge (Solution Part 7)
07:36
Additional Bonus Question
00:32

---------- PART 4: ADVANCED TOPICS ----------------

11 lectures
Download of Course Materials Part 4 (reminder)
00:06
Helpful DatetimeIndex Attributes and Methods
06:24
Filling NA Values with bfill, ffill and interpolation
10:07
resample() and agg()
04:48
resample() and OHLC()
01:17
Upsampling with resample()
06:10
Timezones and Converting (Part 1)
04:36
Timezones and Converting (Part 2)
04:48
Shifting Dates with pd.DateOffset()
04:34
Advanced Date Shifting
03:37
The Timedelta Object
07:27

+++ WHAT´S NEW IN PANDAS VERSION 1.0? - A HANDS-ON GUIDE +++

13 lectures
Intro and Overview
02:08
How to update Pandas to Version 1.0
00:09
Downloads for this Section (reminder)
00:06
Important Recap: Pandas Display Options (Changed in Version 0.25)
05:38
Info() method - new and extended output
01:41
NEW Extension dtypes ("nullable" dtypes): Why do we need them?
04:31
Creating the NEW extension dtypes with convert_dtypes()
03:56
NEW pd.NA value for missing values
06:08
The NEW "nullable" Int64Dtype
03:32
The NEW StringDtype
05:18
The NEW "nullable" BooleanDtype
04:11
Addition of the ignore_index parameter
03:55
Removal of prior Version Deprecations
06:01

------------------ APPENDIX -------------------

1 lectures
Welcome to the Appendix & Download
00:09

Appendix 1: Python Crash Course (optional)

20 lectures
Intro
02:06
First Steps
08:57
Variables
08:22
Data Types: Integers & Floats
08:05
Data Types: Strings
09:59
Data Types: Lists (Part 1)
08:04
Data Types: Lists (Part 2)
18:24
Data Types: Tuples
06:29
Data Types: Sets
03:35
Operators & Booleans
09:39
Conditional Statements (if, elif, else, while)
12:01
For Loops
09:12
Key words break, pass, continue
05:31
Generating Random Numbers
06:19
User Defined Functions (Part 1)
09:21
User Defined Functions (Part 2)
07:16
User Defined Functions (Part 3)
08:17
Visualization with Matplotlib
15:20
Python Basics
15 questions
Quiz Solution
12:46

Appendix 2: Numpy Crash Course (optional)

13 lectures
Introduction to Numpy Arrays
07:03
Numpy Arrays: Vectorization
08:49
Numpy Arrays: Indexing and Slicing
06:49
Numpy Arrays: Shape and Dimensions
05:33
Numpy Arrays: Indexing and Slicing of multi-dimensional Arrays
10:36
Numpy Arrays: Boolean Indexing
05:36
Generating Random Numbers
08:06
Performance Issues
06:17
Case Study: Numpy vs. Python Standard Library
06:49
Summary Statistics
06:38
Visualization and (Linear) Regression
12:00
Numpy
15 questions
Numpy Quiz: Solution
14:47

**NEW** ChatGPT Introduction

9 lectures
Diving into ChatGPT for Finance: The Power of Prompts
07:38
What is ChatGPT and how does it work?
03:56
ChatGPT vs. Search Engines
05:23
Artificial Intelligence vs. Human Intelligence
04:45
Creating a ChatGPT account and getting started
07:44
Design Updates (Nov 23)
01:47
Features, Options and Products around GPT models
06:41
Navigating the OpenAI Website
07:38
What is a Token and how do Tokens work?
07:33

What´s next? (outlook and additional resources)

1 lectures
Bonus Lecture
05:46

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