Review of Python Courses (Part 33)

In Part 32, I summarized my Datacamp courses 95-97. Today I will continue with the next three.

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

My course #98 was Practicing Statistics Interview Questions in Python. This is a very comprehensive course that covers the following topics:

• Conditional probabilities
• Central limit theorem (from numpy.random import randint)
• Probability distributions
• Descriptive statistics
• Categorical data (from sklearn import preprocessing)
• Two or more variables
• Confidence intervals (import scipy.stats as st; from sm.stats.proportion import proportion_conf)
• Hypothesis testing (from scipy.stats import sem, t, ttest_ind)
• Power and sample size (from statsmodels.stats.power import zt_ind_solve_power)
• Multiple testing (from statsmodels.sandbox.stats.multicomp import multipletests)
• Regression models (from sklearn.linear_model import LinearRegression, LogisticRegression)
• Evaluating models (from sklearn.metrics import mean_squared_error, confusion_matrix, recall_score)
• Missing data and outliers
• Bias-variance tradeoff

My course #99 was Intermediate Spreadsheets for Google Sheets. Topics covered in this course include:

• Data types for data science
• Convert or die!
• Common data transformations
• Rounding numbers
• Generating random numbers
• Logical operations
• Flow control
• Blanks, missing values, and errors
• Cell addresses
• Lookups and matching
• Bringing it all together

My course #100 was Practicing Coding Interview Questions in Python. This is probably the most comprehensive and dense course of all. I took a long time getting through this, but the amount of material covered is really incredible. Props to instructor Kirill Smirnov! The course covers:

• What are the main data structures in Python?
• What are common ways to manipulate strings?
• How to write regular expressions in Python?
• What are iterable objects?
• What is a list comprehension?
• What is a zip object?
• What is a generator and how to create one?
• How to pass a variable number of arguments to a function?
• What is a lambda expression?
• What are the functions .map(), .filter(), and .reduce()?
• What is recursion?
• What is the difference between a NumPy array and a list?
• How to use the .apply() method on a dataframe?
• How to use the .groupby() method on a dataframe?
• How to visualize data in Python?

I will review more courses next time.