4.6 out of 5
4.6
433 reviews on Udemy

Manage Finance Data with Python & Pandas: Unique Masterclass

Analyze Stocks with Pandas, Numpy, Seaborn & Plotly. Create, analyze & optimize Index & Portfolios (CAPM, Alpha, Beta)
Instructor:
Alexander Hagmann
5,302 students enrolled
English [Auto]
Step into the Financial Analyst role and give advice on a client´s financial Portfolio (Final Project)
Import large Financial Datasets / historical Prices from Web Sources and analyze, aggregate and visualize them
Calculate Return, Risk, Correlation and Rolling Statistics for Stocks, Indexes and Portfolios
Create, analyze and optimize financial Portfolios and understand the use of the Sharpe Ratio
Intuitively understand Modern Portfolio Theory (CAPM, Beta, Alpha, CML, SML, Risk Diversification) with Real Data examples
Create Interactive Price Charts with Technical Indicators (Volume, OHLC, Candlestick, SMA etc.)
Create Financial Indexes (price-, equal- and value- weighted) and understand the difference between Price Return and Total Return
Easily switch between daily, weekly, monthly and annual returns and understand the benefits of log returns
Start from Zero and learn all the Basics of the powerful Pandas Library

+++++Recently Updated: Pandas Version 1.0: Including a guide on how to best transition from old versions 0.x to version 1.0!+++++

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 more and more shifts 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 the 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 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 exclusive insight 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

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:

  • 7 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 30-Days-Money-Back-Guarantee.

Looking Forward to seeing you in the Course!

Getting Started

1
Course Overview and how to maximize your learning success
2
Tips: How to get the most out of this Course (don´t skip!)
3
Did you know that...?
4
FAQ / Important Information
5
Installation of Anaconda
6
Opening a Jupyter Notebook
7
How to use Jupyter Notebooks

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

1
Welcome to Part 1: Intro

Pandas: Basics

1
Intro to Tabular Data / Pandas
2
Tabular Data Cheat Sheets
3
First Steps (Inspection of Data, Part 1)
4
First Steps (Inspection of Data, Part 2)
5
Coding Exercise 0: Coding the Video Lectures
6
Built-in Functions, Attributes and Methods
7
Make it easy: TAB Completion and Tooltip
8
First Steps
9
Explore your own Dataset: Coding Exercise 1 (Intro)
10
Explore your own Dataset: Coding Exercise 1 (Solution)
11
Selecting Columns
12
Selecting Rows with Square Brackets (not advisable)
13
Selecting Rows with iloc (position-based indexing)
14
Slicing Rows and Columns with iloc (position-based indexing)
15
Position-based Indexing Cheat Sheets
16
Selecting Rows with loc (label-based indexing)
17
Slicing Rows and Columns with loc (label-based indexing)
18
Label-based Indexing Cheat Sheets
19
Summary and Outlook
20
Indexing and Slicing
21
Coding Exercise 2 (Intro)
22
Coding Exercise 2 (Solution)

Pandas: Intermediate Topics

1
Intro
2
First Steps with Pandas Series
3
Analyzing Numerical Series with unique(), nunique() and value_counts()
4
UPDATE Pandas Version 0.24.0 (Jan 2019)
5
EXCURSUS: Updating Pandas / Anaconda
6
Analyzing non-numerical Series with unique(), nunique(), value_counts()
7
The copy() method
8
Sorting of Series and Introduction to the inplace - parameter
9
Pandas Series
10
Coding Exercise 3 (Intro)
11
Coding Exercise 3 (Solution)
12
First Steps with Pandas Index Objects
13
Changing Row Index with set_index() and reset_index()
14
Changing Column Labels
15
Renaming Index & Column Labels with rename()
16
Pandas Index Objects
17
Coding Exercise 4 (Intro)
18
Coding Exercise 4 (Solution)
19
Sorting DataFrames with sort_index() and sort_values()
20
nunique() and nlargest() / nsmallest() with DataFrames
21
Filtering DataFrames (one Condition)
22
Filtering DataFrames by many Conditions (AND)
23
Filtering DataFrames by many Conditions (OR)
24
Advanced Filtering with between(), isin() and ~
25
any() and all()
26
Sorting and Filtering
27
Coding Exercise 5 (Intro)
28
Coding Exercise 5 (Solution)
29
Intro to NA Values / missing Values
30
Handling NA Values / missing Values
31
Exporting DataFrames to csv
32
Summary Statistics and Accumulations
33
The agg() method
34
Coding Exercise 6 (Intro)
35
Coding Exercise 6 (Solution)

Data Visualization with Matplotlib and Seaborn

1
Intro
2
Visualization with Matplotlib (Intro)
3
Customization of Plots
4
Histogramms (Part 1)
5
Histogramms (Part 2)
6
Scatterplots
7
First Steps with Seaborn
8
Categorical Seaborn Plots
9
Seaborn Regression Plots
10
Seaborn Heatmaps
11
Coding Exercise 7 (Intro)
12
Coding Exercise 7 (Solution)

Pandas: Advanced Topics

1
Intro
2
Removing Columns
3
Removing Rows
4
Adding new Columns to a DataFrame
5
Arithmetic Operations (Part 1)
6
Arithmetic Operations (Part 2)
7
Creating DataFrames from Scratch with pd.DataFrame()
8
Adding new Rows (Hands-on)
9
Adding new Rows to a DataFrame
10
Manipulating Elements in a DataFrame
11
Coding Exercise 8 (Intro)
12
Coding Exercise 8 (Solution)
13
Introduction to GroupBy Operations
14
Understanding the GroupBy Object
15
Splitting with many Keys
16
split-apply-combine
17
split-apply-combine applied
You can view and review the lecture materials indefinitely, like an on-demand channel.
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!
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27 hours on-demand video
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Manage Finance Data with Python & Pandas: Unique Masterclass
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