Fast Track to Python for Data Science | Introduction to Python for Data Science

Course Objectives

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.  Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom.  Throughout the hands-on course students will learn to leverage core Python scripting for data science skills using the most current and efficient skills and techniques.

Working in a hands-on learning environment, guided by our expert team, attendees will learn about and 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

Course Prerequisites

This introductory-level course is geared for data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.  While there are no specific programming prerequisites, students should be comfortable working with files and folders and should not be afraid of the command line and basic scripting.  This is for attendees new to Python.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

1. An Overview of Python

• Why Python?
• Python in the Shell
• Python in Web Notebooks (iPython, Jupyter, Zeppelin)
• Demo: Python, Notebooks, and Data Science

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

3. Flow Control

• About flow control
• White space
• Conditional expressions
• Relational and Boolean operators
• While loops
• Alternate loop exits

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

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

6. Functions

• Defining functions
• Parameters 
• Global and local scope
• Nested functions
• Returning values

7. Sorting

• The sorted() function
• Alternate keys
• Lambda functions
• Sorting collections
• Using operator.itemgetter()
• Reverse sorting

8. Errors and Exception Handling

• Syntax errors
• Exceptions
• Using try/catch/else/finally
• Handling multiple exceptions
• Ignoring exceptions

9. Essential Demos

• Importing Modules
• Classes
• Regular Expressions

10. The standard library

• Math functions
• The string module

11. Dates and times

• Working with dates and times
• Translating timestamps
• Parsing dates from text
• Formatting dates
• Calendar data

12. numpy

• numpy basics
• Creating arrays
• Indexing and slicing
• Large number sets
• Transforming data
• Advanced tricks

13. Python and Data Science

• Data Science Essentials
• Working with Python in Data Science

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

15. matplotlib

• Creating a basic plot
• Commonly used plots
• Ad hoc data visualization
• Advanced usage
• Exporting images