Manage Finance Data with Python & Pandas: Unique Masterclass

Manage Finance Data with Python & Pandas: Unique Masterclass

finance-data-with-python-and-pandas

 Manage Finance Data with Python & Pandas: Unique Masterclass - 
Analyze Stocks with Pandas, Numpy, Seaborn & Plotly. Create, analyze & optimize Index & Portfolios (CAPM, Alpha, Beta)
  • Bestseller
  • Created by Alexander Hagmann
  • English [Auto]

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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

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.

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