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30-Day Money-Back Guarantee
Finance & Accounting Financial Modeling & Analysis Data Analysis

Manage Finance Data with Python & Pandas: Unique Masterclass

Analyze Stocks with Pandas, Numpy, Seaborn & Plotly. Create, analyze & optimize Index & Portfolios (CAPM, Alpha, Beta)
Bestseller
Rating: 4.7 out of 54.7 (394 ratings)
4,737 students
Created by Alexander Hagmann
Last updated 4/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • 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
Curated for the Udemy for Business collection

Requirements

  • No specific Finance knowledge needed! The course intuitively explains the major fundamentals of Finance and Portfolio Theorie based on data examples.
  • Ideally some Spreadsheet Basics/Programming Basics (not mandatory, the course guides you through the basics)
  • 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

+++++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!

Who this course is for:

  • Investment & Finance Professionals who want to transition from Excel into Python to boost their careers and working efficiency.
  • (Finance) Students and Researchers who need to handle large datasets and reached the limits of Excel.
  • Data Scientists who want to improve their Data Handling/Manipulation skills (in particular for Time Series Data)
  • Everyone who want to step into (Financial) Data Science. Pandas is Key to everything.
  • Everyone curious about how Financial Performance is measured and how (Stock) Indexes and Portfolios are created, analyzed, visualized and optimized. It´s the easiest way to understand the concepts with data examples rather than theories and formulas.

Featured review

Johnny Vergara
Johnny Vergara
47 courses
13 reviews
Rating: 5.0 out of 58 months ago
Professor Alexander's course is very complete, it has everything I need to start using this amazing library which is Pandas, he explains everything really well, detailed and one practically can't get lost (even with all this information, which is a lot, but he manages to deliver it in a really excellent way). The course covers everything you will need and more. This is a great choice if one wants to learn Pandas.

Course content

23 sections • 257 lectures • 26h 54m total length

  • Preview10:05
  • Tips: How to get the most out of this Course (don´t skip!)
    05:27
  • Preview03:08
  • FAQ / Important Information
    02:37
  • Installation of Anaconda
    08:08
  • Opening a Jupyter Notebook
    09:29
  • How to use Jupyter Notebooks
    14:00

  • Welcome to Part 1: Intro
    01:01

  • Intro to Tabular Data / Pandas
    05:03
  • Preview00:00
  • First Steps (Inspection of Data, Part 1)
    11:10
  • First Steps (Inspection of Data, Part 2)
    08:45
  • Coding Exercise 0: Coding the Video Lectures
    14:58
  • Built-in Functions, Attributes and Methods
    08:26
  • Make it easy: TAB Completion and Tooltip
    08:57
  • First Steps
    3 questions
  • Explore your own Dataset: Coding Exercise 1 (Intro)
    07:35
  • Preview04:35
  • 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
    00:00
  • 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
    00:00
  • Summary and Outlook
    09:38
  • Indexing and Slicing
    6 questions
  • Coding Exercise 2 (Intro)
    01:22
  • Coding Exercise 2 (Solution)
    06:31

  • Intro
    00:13
  • 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
  • Pandas Series
    3 questions
  • Coding Exercise 3 (Intro)
    01:30
  • 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
  • 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()
    09:01
  • 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
  • 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
    10:26
  • The agg() method
    03:27
  • Coding Exercise 6 (Intro)
    01:50
  • Coding Exercise 6 (Solution)
    10:21

  • 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

  • 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)
    00:59
  • 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

  • Welcome
    00:28

  • 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

  • Intro
    00:25
  • Getting Ready (Installing required library)
    02:42
  • Importing Stock Price Data from Yahoo Finance (it still works!)
    09:29
  • Preview05:32
  • 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

  • Intro
    02:50
  • Importing Financial Data from Excel
    11:25
  • Simple Moving Averages (SMA) with rolling()
    08:44
  • Preview07:08
  • 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
    Preview09:18
  • 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

Instructor

Alexander Hagmann
Data Scientist | Finance Professional | Entrepreneur
Alexander Hagmann
  • 4.6 Instructor Rating
  • 3,968 Reviews
  • 38,695 Students
  • 8 Courses

Alexander is a Data Scientist and Finance Professional with more than 10 years of experience in the Finance and Investment Industry.

He is also a Bestselling Udemy Instructor for

- Data Analysis/Manipulation with Pandas

- (Financial) Data Science

- Python for Business and Finance

- Algorithmic Trading

Alexander started his career in the traditional Finance sector and moved step-by-step into Data-driven and Artificial Intelligence-driven Finance roles. He is currently working on cutting-edge Fintech projects and creates solutions for Algorithmic Trading and Robo Investing. And Alexander is excited to share his knowledge with others here on Udemy. Students who completed his courses work in the largest and most popular tech and finance companies all over the world.

Alexander´s courses have one thing in common: Content and concepts are practical and real-world proven. The clear focus is on acquiring skills and understanding concepts rather than memorizing things.   

Alexander holds a Master´s degree in Finance and passed all three CFA Exams (he is currently no active member of the CFA Institute).   

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