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Python for Data Science

Please note that this is not an asynchronous on-line track. Everyone is expected to log in every day all day according to the Winter Working Connections schedule. This is a synchronous track.

This on-line Working Connections event is intended solely for the person who registers. Link sharing is only permitted with the prior approval of the National Convergence Technology Center.

This track is now CLOSED, but you can be added to the “wait list” and be notified if space opens up. When you register, select the Python “WAIT LIST ONLY” option as your primary choice and another track as your alternate choice. You will be placed in your alternate choice until space opens up in your primary choice.


This course introduces the Python programming language and select data science topics. It describes Python features, demonstrates tools used to develop and build Python programs, and then uses hands-on training to teach students how to create them. Topics include structured and object-oriented programming concepts, data types (lists, tuples, dictionaries), selection and repetition structures, functions, accessing a SQL database, and file I/O. Students will develop and implement a variety of Python programs and learn about Python’s data science packages for data manipulation, analysis, and visualization.


40 wpm typing speed recommended (; any previous programming exposure/experience will be fine.

This three-day course is not intended to be a first programming language class.


Python Data Science Handbook
, Vanderplas, Jake - 1st Edition, 2016 O'Reilly Media, ISBN 9781491912058 [Free:]

Python Crash Course, Matthes, Eric - 2nd Edition, 2019 No Starch Press, ISBN 9781593279288.
We are trying to acquire donated e-book copies. We will keep you posted.

Additional Technology Requirements

The following software will be used in the course:
Python ( - Please use only the latest release version. Don't download a beta.



Pamela Brauda and David Singletary

Pamela Brauda is a faculty member in the School of Technology at Florida State College at Jacksonville, where she teaches courses in programming, networking, database, and data science. Pamela is a co-designer of the new A.S. in Data Science Technology program at FSCJ, co-principal investigator for NSF Grant #1902524 “Meeting Industry Needs through a Two-Year Data Science Technician Education Program”, and faculty co-advisor for the FSCJ STARS Computing Corps. Before teaching at FSCJ, Pamela worked as a Metadata Analyst with the Florida Department of Law Enforcement, taught programming and software development at the University of North Florida, created and operated several small businesses, and taught high school mathematics. She graduated from the University of Georgia with a B.S. and from the University of North Florida with an M.S. in Computer Science.

David Singletary is a faculty member at Florida State College at Jacksonville (FSCJ), where he teaches courses in networking, software development, and data science. David is a co-designer of the new A.S. in Data Science Technology program at FSCJ, principal investigator for NSF Grant #1902524 “Meeting Industry Needs through a Two-Year Data Science Technician Education Program”, and faculty advisor for the FSCJ STARS Computing Corps. Previously, David worked as a software engineer for Cisco Systems and various Silicon Valley startup companies after serving in the U.S. Air Force. He graduated from the University of Central Florida with a B.S. in Computer Science and an M.S. in Computer Science from the University of Colorado.

Course Objectives

At the successful conclusion of this course, students should be able to:

1. Describe the data life cycle and implement in Python with data science packages.
2. Write interactive Python applications using appropriate data types, control structures, functions, modules, and file I/O.
3. Write Python applications which use Python data structures and object-oriented programming concepts.
4. Learn how to access a SQL database from a Python application.


Day 1 (Monday)

Module 1: Python Language Basics, and Python Data Types
Textbook - Crash Course
Ch. 1 Getting Started
Ch. 2 Variables and Simple Data Types

Module 2: Lists, Tuples, and For Loops
Textbook - Crash Course
Ch. 3 Selections
Ch. 4 Working with Lists

Module 3: Selection Statements
Textbook - Crash Course
Ch. 5 If Statements

Module 4: Dictionaries
Textbook - Crash Course
Ch. 6 Dictionaries

Module 5: User Input and While Loops
Textbook - Crash Course
Ch. 7 User Input and While Loops

Day 2 (Tuesday)

Module 6: Functions, Modules, and Packaging
Textbook - Crash Course
Ch. 8 Functions

Module 7: Object-Oriented Programming
Textbook - Crash Course
Ch. 9 Classes

Module 8: File I/O and Exceptions
Textbook - Crash Course
Ch. 10 Files and Exceptions

Module 9: Accessing Data with SQL
No Textbook

Day 3 (Wednesday)

Module 10: Analyzing Data with Numpy
Textbook - Data Science Handbook
Ch. 2 Introduction to Numpy

Module 11: Manipulating Data with Pandas
Textbook - Data Science Handbook
Ch. 3 Data Manipulation with Pandas

Module 12: Visualizing Data with Matplotlib
Textbook - Data Science Handbook
Ch. 4 Visualization with Matplotlib

Please note that content is subject to change or modification based on the unique needs of the track participants in attendance.

python.txt · Last modified: 2020/10/29 09:24 by admin