Bespoke Python

• An understanding of ___.
• Experience with ___.
• ___ programming experience.

Audience

• ___
• ___
• ___

Programming in Python

• Running Python
• Algorithmic thinking
• Getting help
• Debugging

Features of the Python language

• Variables, expressions and statements
• Functions
• Conditional Execution and recursion

Data Structures

• Strings
• Lists
• Dictionaries
• Tuples

Object-oriented programming

• Types
• Mutability
• Attributes
• Classes
• Methods

Importing data

• Pandas and numpy
• Reading in data from files and the web
• Reading in financial data from online sources

Data wrangling

• Numpy arrays
• Common matrix operations
• Optimizing performance by avoiding loops
• Sorting
• Filtering 
• Aggregating 
• Working with time series data 
• Imputation of missing data

Exploratory data analysis

• Cross-tabs
• Graphics in matplotlib and seaborn

Model development and evaluation

• Scikit-learn for modelling
• Regression models
• Classification techniques (logistic regression, random forests)
• Statsmodel
• Principal component analysis

Case study: US housing data

• End-to-end analysis of a real dataset
• Read data
• Impute missing values
• Set up a data analytics pipeline
• Make suitable visualizations 
• Compare different predictive models