Review of Python Courses (Part 26)

In Part 25, I summarized my Datacamp courses 74-76. Today I will continue with the next three.

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

My course #77 was Visualizing Time Series Data in Python. This course covers:

• Plot your first time series
• Customize your time series plot
• Clean your time series data (counting missing values in df)
• Plot aggregates of your data
• Summarizing the value in your time series data
• Autocorrelation and partial autocorrelation (from statsmodels.graphics import tsaplots)
• Seasonality, noise, and trend in time series data [from pylab import RCparams, sm.tsa.seasonal_decompose()]
• Working with more than one time series
• Plot multiple time series (adding statistical summaries to your plots)
• Find relationships between multiple time series [sns.heatmap(), sns.clustermap()]
• Apply your knowledge to a new dataset
• Beyond summary statistics
• Decompose time series data
• Compute correlations between time series

My course #78 was Financial Forecasting in Python. Topics covered in this course include:

• Introduction to financial statements
• Calculating sales and the cost of goods sold
• Working with raw datasets
• Introduction to the balance sheet
• Balance sheet efficiency ratios
• Financial periods and how to work with them
• The datetime library and Split function
• Tips and tricks when working with datasets
• Building sensitive forecast models and common forecast assumptions
• Dependencies and sensitivity in financial forecasting
• Working with variances in the forecast

My course #79 was Foundations of Probability in Python. This course covers:

• Let’s flip a coin in Python (from scipy.stats import bernoulli, binom)
• Probability mass and distribution functions
• Expected value, mean, and variance (from scipy.stats import describe)
• Calculating probabilities of two events (from scipy.stats import find_repeats, relfreq)
• Conditional probabilities
• Total probability law
• Bayes’ rule
• Normal distributions (from scipy.stats import norm, import matplotlib.pyplot as plt, import seaborn as sns)
• Risk factors
• Factor models
• Portfolio analysis tools
• Normal probabilities
• Poisson distributions (from scipy.stats import poisson)
• Geometric distributions (from scipy.stats import geom)
• From sample mean to population mean (from scipy.stats import binom, describe)
• Adding random variables
• Linear regression (from sklearn.linear_model import LinearRegression, from scipy.stats import linregress)
• Logistic regression (from sklearn.linear_model import LogisticRegression)

I will review more courses next time.