Fast Track to Python for Data Science | Introduction to Python for Data Science (TTPS4873)

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

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional python, data science, AI / machine learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

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

Course Materials

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive related (as applicable) project files, code files, data sets and solutions required for the hands-on work.

Classroom Setup Made Simple:  Our dedicated tech team will work with you to ensure your classroom and lab environment is setup, tested and ready to go well in advance of the course delivery date, ensuring a smooth start to class and seamless hands-on experience for your students. We offer several flexible student machine setup options including guided manual set up for simple installation directly on student machines, or cloud based / remote hosted lab solutions where students can log in to a complete separate lab environment minus any installations, or we can supply complete turn-key, pre-loaded equipment to bring ready-to-go student machines to your facility.  Please inquire for details.