Fast Track to Python for Data Science [TTPS4873]

OVERVIEW

Introduction to Python for Data Science is a three-day, hands-on course that introduces data analysts and business analysts to the Python programming language, as it’s often used in Data Science in web notebooks.  This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.

Students will explore basic Python syntax and concepts applicable to using Python to work with data.  The course begins with quick introduction to Python, with demonstrations of both script-based and web notebook-based Python, and then dives into the essentials of Python necessary to a data scientist.  The tail end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas and Matplotlib. Students will explore the concepts and work with large data sets in a workshop style lab.  This class is hands-on and includes basic programming labs that introduce students to basic Python syntax and concepts applicable to using Python to work with data, AI and machine learning basics. 

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

AUDIENCE

This course is geared for data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks. 

NEXT STEP

Our core Python, data science and machine learning training courses provide students with a solid foundation for continued learning based on role, goals, or their areas of specialty.  Our learning paths offer a wide variety of related follow-on courses such as:

- TTML6802 Machine Learning Essentials with Python (3 days)

- TTPS4876 Next Level Python in Data Science (Intermediate) (5 days)

- Please see the Python Training or Machine Learning Series list for additional topics and titles.

CONTENT

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. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience needs and skill-level.
 
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