Review of Python Courses (Part 20)

In Part 19, I summarized my Datacamp courses 56-58. Today I will continue with the next three.

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

My course #59 was Dealing with Missing Data in Python. This course covers:

• Why deal with missing data (built-in Python NoneType vs. np.nan)?
• Handling missing values
• Analyze the amount of missingness (import missingno as msno)
• Is the data missing at random?
• Finding patterns in missing data
• Visualizing missingness across a variable
• When and how to delete missing data
• Mean, median, and mode imputations (from sklearn.impute import SimpleImputer)
• Imputing time-series data
• Visualizing time-series imputations
• Imputing using fancyimpute (from fancyimpute import KNN; from fancyimpute import IterativeImputer)
• Imputing categorical values
• Evaluation of different imputation techniques

My course #60 was Intermediate Python for Finance. This course covers:

• Representing time with datetimes
• Working with datetimes
• Dictionaries
• Comparison operators
• Boolean operators
• If statements (with dictionary)
• For and while loops
• Creating a dataframe
• Accessing data
• Aggregating and summarizing
• Extending and manipulating data
• Peeking at data with head, tail, and describe
• Filtering data
• Plotting data

My course #61 was Object-Oriented Programming in Python. These OOP-related courses were really confusing to me the first time through. This course covers:

• What is OOP?
• Class anatomy: attributes and methods
• Class anatomy: the __init__ constructor
• Instance and class data
• Class inheritance
• Customizing functionality via inheritance
• Operator overloading: comparison
• Operator overloading: string representation
• Exceptions (try – except – finally)
• Designing for inheritance and polymorphism (Liskov substitution principle)
• Managing data access: private attributes
• Properties

I will review more courses next time.