Python Primer for Data Science

Course Objectives 

Throughout the course students will be led through a series of progressively advanced topics, where each topic consists of lecture, demo, hands-on lab exercises, and lab review. This course is “skills-centric”, designed to train attendees in core Python data science skills, coupling the most current, effective techniques with best practices.

Working within in an engaging, hands-on learning environment, guided by our expert, students will explore:

• How to work with Python interactively in web notebooks
• The essentials of Python scripting
• Key concepts necessary to enter the world of Data Science via Python

Audience & Pre-requisites

This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with SAS or working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course.

Take After:

•  TTPS4876  Next Level Python for Data Science – 3 days

Session 1:  An Overview of Python

·       Why Python?

·       Python in the Shell

·       Python in Web Notebooks (iPython, Jupyter, Zeppelin)

·       Demo: Python, Notebooks, and Data Science

·       Python 2 vs 3

Session 2:  Getting Started

·       Using variables

·       Builtin functions

·       Strings

·       Numbers

·       Converting among types

·       Writing to the screen

·       Command line parameters

·       Running standalone scripts under Unix and Windows

Session 3: Flow Control

·       About flow control

·       White space

·       Conditional expressions

·       Relational and Boolean operators

·       While loops

·       Alternate loop exits

Session 4: Sequences, Arrays, Dictionaries and Sets

·       About sequences

·       Lists and list methods

·       Tuples

·       Indexing and slicing

·       Iterating through a sequence

·       Sequence functions, keywords, and operators

·       List comprehensions

·       Generator Expressions

·       Nested sequences

·       Working with Dictionaries

·       Working with Sets

Session 5:  Working with files

·       File overview

·       Opening a text file

·       Reading a text file

·       Writing to a text file

·       Reading and writing raw (binary) data

Session 6:  Functions

·       Defining functions

·       Parameters 

·       Global and local scope

·       Nested functions

·       Returning values

Session 7:  Sorting

·       The sorted() function

·       Alternate keys

·       Lambda functions

·       Sorting collections

·       Using operator.itemgetter()

·       Reverse sorting

Session 8:  Errors and Exception Handling

·       Syntax errors

·       Exceptions

·       Using try/catch/else/finally

·       Handling multiple exceptions

·       Ignoring exceptions

Session 9:  Essential Demos

·       Importing Modules

·       Classes

·       Regular Expressions

Session 10:  The standard library

·       Math functions

·       The string module

Session 11:  Dates and times

·       Working with dates and times

·       Translating timestamps

·       Parsing dates from text

·       Formatting dates

·       Calendar data

Session 12: numpy

·       numpy basics

·       Creating arrays

·       Indexing and slicing

·       Large number sets

·       Transforming data

·       Advanced tricks

Session 13: Python and Data Science

·       Data Science Essentials

·       Working with Python in Data Science

Session 14: Working with Pandas

·       pandas overview

·       Dataframes

·       Reading and writing data

·       Data alignment and reshaping

·       Fancy indexing and slicing

·       Merging and joining data sets

Time Permitting

Session: matplotbil

·       Creating a basic plot

·       Commonly used plots

·       Ad hoc data visualization

·       Advanced usage

·       Exporting images