Lecture notes for "Programming for Data Science", "Python for Data Science" and "Python for ML Engineering."

This repository contains lecture notes for classes offered by Shahbaz Chaudhary at the University Of Chicago's Masters in Applied Data Science program.

Class setup

Please follow the instructions below to get your computer ready for this class.

Note Mac users: Once software is downloaded, if you double click to launch it, you may get permission errors. Try to right click on the downloaded software, pick "open" and continue. (Apple is trying to protect you from accidentally starting malware/virus)

Install Python (anaconda distribution)

Please install Python from this website: https://www.anaconda.com/download/ (modern computers are 64 bit so please pick that option)

Mac users: Accept all default prompts

Windows users: Accept all default prompts

Anaconda's distribution of Python is widely used in the industry, particularly among data scientists. This distribution makes it easy to use many libraries and packages for data analysis, building models, visualization, etc.

Once installed, please start jupyter notebook and execute code provided below

1. Start Anaconda Navigator and click Launch on the panel labeled Jupyter Notebook
2. Create new notebook from the web interface
3. Execute this code:

%%timeit
sum(range(1_000_000))

4. Execute this code:

from psutil import virtual_memory, disk_usage, cpu_count, os

bytes_in_gb = 1024**3

print("Memory:\t",round(virtual_memory().total/bytes_in_gb,4), "Gigabytes")
print("Disk:\t",round(disk_usage(os.path.abspath(os.sep)).total/bytes_in_gb,4), "Gigabytes")
print("CPUs:\t", cpu_count())

Clone this repository

1. Visit this web page: https://github.com/falconair/ProgrammingForAnalytics
2. Click "Clone or download" and pick the "Download ZIP" option (unless you already have a GitHub account)

The following steps are optional

Install Git and Git Bash

Please intall Git, a version control sotware, from this website: https://git-scm.com/downloads (you are ok to use default settings)

Note that this is a command-line tool. Once installed, you may not see a new icon to click. We will install a Desktop client to remedy this.

Although we don't make heavy use of version control, you will be introduced to the concept. Installing Git also installs "Git Bash," and comand line environment which simulates Unix/Linux. We will do several exercises which will require this environment.

Additional steps:

1. Install a Graphical interface to Git from this website: https://desktop.github.com/
2. [Windows users only] a. type cd (this will take you to your home directory) b. type echo cd >> .profile (this will make sure your home directory is loaded when you start Git Bash)

Install Visual Studio Code

Please install Visual Studio Code from https://code.visualstudio.com/

Additional steps:

Install Python extensions from https://marketplace.visualstudio.com/items?itemName=ms-python.python (visit that page and click "Install")

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Table of Contents

	Module	Class	Description
	Intro to consoles	Intro to consoles	This lectures introduces the concept of a console, such as dos cmd or mac terminal, to students
	Programming vs calculators	Programming vs calculator	Helps novices understand what features need to be added to a calculator to make it a  fully programming environment
	First programs	First programs	Several examples of small, but full programs which use all common programming constructs and data structures
	Intro to Jupyter	Intro to Jupyter - not technical	Provides hsitorical context for Jupyter
		Intro type Jupyter - technical	Provides a practioner specific intro to Jupyter
	All of Python	All of Python - faster basics	An overview of Python for computer programmers (multi-week lecture)
		All of Python - basics	An overview of Python for novice or non-programmers: teaches programming constructs
		All of Python - variables and tuples	Teaches multiple variable assignment
		All of Python - basic functions	Introduces functions
		All of Python - numbers	Overview of numbers and related operations
		All of Python - strings	Overview of strings and related operations
		All of Python - Boolean algebra	Dives deeper into the world of comparisons, and/or/not
		All of Python - basic plotting	General matlab intro (not recommended for novices)
		All of Python - dictionaries	Introduces Python dictionaries (aka maps, associative arrays)
		All of Python - lists	Teaches lists
		All of Python - comprhensions	Teaches list and dictionary comprehensions (useful but intermediate feature)
		All of Python - basic classes	Introduces classes and the very basics of object oriented programming
		All of Python - loops	Describes while and for loops
		All of Python - conditionals and None	Deeper dive into if/else conditions and Python's None type
		All of Python - function arguments	Deeper dive into functions, including optional parameters
		All of Python - lambda functions	Introduces anaonymous functions (aka lambda functions)
		All of Python - recursive functions	Introduces the world of functions themselves
		All of Python - regexes	A very basic intro to regular expressions
	Intro to Numpy	Numpy quick start	A broad overview of Numpy
	Intro to Pandas	Pandas - quick start	A broad overview of Pandas
		Pandas - Series	A deeper dive into Pandas Series
		Pandas - Dataframes	Build up a dataframe using a collection of Series or a Numpy matrix, shows basic functioanality
		Pandas - general operations	Introduces additional dataframe operations
		Pandas -  combining: merge, join, concat	Shows how to combine multiple dataframes, similar to SQL joins
		Pandas - groupby	Show how to break a population into subgroups and find aggregates for those subgroups
		Pandas - Index	Does a deep dive into Pandas indexes, a topic often not known to casual Pandas users
		Pandas - reshape, pivot, melt, stack	Shows how to convert columns to rows and back, features similar to Excel's pivot table or cub rollup analysis
		Pandas - operations: str, dt, apply	Shows how to apply string or date functions to Pandas series
	Scikit learn	Scikit Learn - method behind the madness	Describes Scikit learn's architecture and introduces pipes
		Scikit Learn - Run saved models	Shows how to connect SKLearn models to the web (very basic)
	Secret lives of text files	Secret lives of text files	Describes encodings (UTF, ASCII), multi-byte characters, special characters such as \n and \t, etc.
	How to read technical docs	How to read technical docs
	Basic computer archtecture	Basic computer architecture	Provides a broad overview of a CPU, registers, floating points vs integers, disk vs memory speed differences
Python for Analytics	First programs	First programs	Several examples of small, but full programs which use all common programming constructs and data structures
	Intro to Jupyter	Intro to Jupyter - not technical	Provides hsitorical context for Jupyter
		Intro type Jupyter - technical	Provides a practioner specific intro to Jupyter
	All of Python	All of Python - faster basics	An overview of Python for computer programmers (multi-week lecture)
	Intro to Numpy	Numpy quick start	A broad overview of Numpy
	Intro to Pandas	Pandas - quick start	A broad overview of Pandas
		Pandas - Series	A deeper dive into Pandas Series
		Pandas - Dataframes	Build up a dataframe using a collection of Series or a Numpy matrix, shows basic functioanality
		Pandas - general operations	Introduces additional dataframe operations
		Pandas -  combining: merge, join, concat	Shows how to combine multiple dataframes, similar to SQL joins
		Pandas - groupby	Show how to break a population into subgroups and find aggregates for those subgroups
		Pandas - Index	Does a deep dive into Pandas indexes, a topic often not known to casual Pandas users
		Pandas - reshape, pivot, melt, stack	Shows how to convert columns to rows and back, features similar to Excel's pivot table or cub rollup analysis
		Pandas - operations: str, dt, apply	Shows how to apply string or date functions to Pandas series
	Scikit learn	Scikit Learn - method behind the madness	Describes Scikit learn's architecture and introduces pipes
		Scikit Learn - Run saved models	Shows how to connect SKLearn models to the web (very basic)
Programming for Analytics	Programming vs calculators	Programming vs calculator	Helps novices understand what features need to be added to a calculator to make it a  fully programming environment
	First programs	First programs	Several examples of small, but full programs which use all common programming constructs and data structures
	Intro to Jupyter	Intro to Jupyter - not technical	Provides hsitorical context for Jupyter
		Intro type Jupyter - technical	Provides a practioner specific intro to Jupyter
	All of Python	All of Python - basics	An overview of Python for novice or non-programmers: teaches programming constructs
	Secret lives of text files	Secret lives of text files	Describes encodings (UTF, ASCII), multi-byte characters, special characters such as \n and \t, etc.
	How to read technical docs	How to read technical docs
	Basic computer archtecture	Basic computer architecture	Provides a broad overview of a CPU, registers, floating points vs integers, disk vs memory speed differences
	Intro to Numpy	Numpy quick start	A broad overview of Numpy
	Intro to Pandas	Pandas - quick start	A broad overview of Pandas
	Lectures on R omitted