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
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.
Deal Score+1
Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.
