Review of Python Courses (Part 8)
Posted by Mark on December 10, 2020 at 07:34 | Last modified: January 26, 2021 11:22In Part 7, I summarized my Datacamp courses 19-21. Today I will continue with the next three.
As a reminder, I introduced you to my recent work learning Python here.
My course #22 was Statistical Thinking in Python (Part 2). This course covers:
- Optimal parameters [statistical inference using scipy.stats, statsmodels, or hacker stats with numpy; plt.margins() ]
- Linear regression by least squares [slope, intercept = np.polyfit() ]
- The importance of exploratory data analysis: Anscombe’s quartet (generating and plotting line of best fit)
- Generating bootstrap replicates [ecdf() written in my course (prequel) #14]
- Bootstrap confidence intervals
- Pairs bootstrap
- Formulating and simulating a hypothesis (permutation sample)
- Test statistics and p-values (permutation replicate)
- Bootstrap hypothesis tests
- A/B testing
- Test of correlation
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My course #23 was Introduction to Financial Concepts in Python. This course covers:
- Fundamental financial concepts (calculating return on investment and compound interest)
- Present and future value [np.pv(), np.fv() ]
- Net present value and cash flows [np.npv(rate= , values=np.array([]) ) ]
- Common profitability analysis methods [np.npv(), np.irr(np.array([]) ) ]
- Weighted average cost of capital
- Comparing two projects of different life spans (EAA)
- Mortgage basics [np.pmt(rate, nper, pv) ]
- Amortization, principal, and interest (simulating periodic mortgage payments)
- Home ownership, equity, and forecasting (cumulative operations in numpy)
- Budgeting project proposal [constant cumulative growth with np.repeat(), calculating monthly expenses]
- Net worth and valuation in your personal financial life
- The power of time and compound interest
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My course #24 was Introduction to Portfolio Risk Management in Python. This course covers:
- Financial returns
- Mean, variance, and normal distributions (scaling volatility)
- Skewness and kurtosis (from scipy.stats import skew, kurtosis, Shapiro-Wilk test)
- Portfolio composition (calculating market-cap weights)
- Correlation and covariance (calculating portfolio volatility)
- Markowitz portfolios (MSR and GMV)
- The capital asset pricing model (calculating Beta)
- Alpha and multi-factor models (Fama-French 3-factor model)
- Expanding the 3-factor model (Fama-French 5-factor model)
- Estimating tail risk (historical drawdown, historical/conditional VaR)
- VaR extensions
- Random walks (Monte Carlo simulations)
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