Review of Python Courses (Part 8)

In 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

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

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)