Review of Python Courses (Part 18)

In Part 17, I summarized my Datacamp courses 50-52. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #53 was Introduction to Python for Finance. This course covers:

• Why Python for finance?
• Comments and variables
• Variable data types
• Lists in Python
• Lists in lists
• Methods and functions
• Arrays (probably best for financial analysis)
• Two dimensional arrays
• Using arrays for analyses (indexing arrays—might work in place of .loc or .iloc?)
• Visualization in Python
• Histograms (normed arg)
• Introducing the dataset
• Closer look at the sectors
• Visualizing trends

My course #54 was Experimental Design in Python. This course covers:

• Intro to experimental design (import plotnine as p9)
• Our first hypothesis test—Student’s t-test (from scipy import stats)
• Testing proportion and correlation [stats.chisquare(), stats.fisher_exact(), stats.pearsonr()]
• Confounding variables
• Blocking and randomization (random sampling)
• ANOVA [import statsmodels as sm, stats.f_oneway()]
• Interactive effects (two- and three-way ANOVAs)
• Type I error (Bonferroni and Šidák correction for multiple comparisons)
• Sample size (from statsmodels.stats import power as pwr)
• Power
• Assumptions and normal distributions (Q-Q plot)
• Testing for normality [from scipy import stats, stats.shapiro()]
• Non-parametric tests: Wilcoxon rank-sum and signed-rank (paired) test
• More non-parametric tests: Spearman correlation

My course #55 was Introduction to Data Engineering. For some reason, these data engineering courses are not my cup of tea. This course covers:

• What is data engineering?
• Tools of the data engineer (data engineers are expert users of database systems)
• Cloud providers
• Databases
• Parallel computing (from multiprocessing import Pool) and computation frameworks
• Workflow scheduling frameworks
• Extract
• Transform
• Loading
• Putting it all together
• Case study: course ratings
• From ratings to recommendations
• Scheduling daily jobs

I will review more classes next time.