This hands-on course provides a solid starting point for business analysts, technical managers, or anyone interested in understanding the basics of Python in the context of data science. Attendees will be able to:  

• Run Python Scripts: You will be able to execute basic Python scripts using both traditional script-based environments and interactive web notebooks like Jupyter, which is fundamental for beginning any data science project. 
• Manipulate Simple Data Structures: Gain the ability to handle simple operations with Python's standard data structures (such as lists and dictionaries), enabling you to organize and manage data efficiently. 
• Apply Basic Python Commands for Data Analysis: Learn to use essential Python commands and functions for basic data analysis tasks, giving you a taste of what Python can offer in processing and analyzing data. 
• Read and Write Basic Files: Develop the skills to open, read, write, and close text files with Python, which is crucial for importing data for analysis and exporting results. 
• Get Acquainted with Python's Data Science Libraries: Acquire a foundational awareness of how libraries like NumPy and Pandas are used in Python for tasks like statistical analysis and data manipulation, preparing you for further exploration in the field of data science. 

This introductory-level course is geared for technical professionals new to Python. Roles include data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks. Familiarity with basic scripting skills is recommended, as this course does not teach general scripting basics. 

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation.  

1. Getting Started: Explore the Python Environment

• Python in the Shell
• The python interpreter
• Getting started with Jupyter notebook)
• Python in Web Notebooks (iPython, Jupyter, Zeppelin)
• Exploring Python, Notebooks, and Data Science

2. Variables and Values

• Using variables
• Builtin functions
• Strings
• Numbers
• Converting among types

3. Basic Input and output

• Writing to the screen
• Command line parameters

4. Flow Control

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

5. 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

6. Working with files

• File overview
• Opening a text file
• Reading a text file
• Writing to a text file
• Reading and writing raw (binary) data

7. Functions, modules, & packages

• Defining functions
• Parameters
• Variable Scope
• Creating modules
• Using import
• Creating packages

8. Python and Data Science

• Python data science overview
• NumPy Overview (with SciPy)
• Pandas Overview
• MatPlotLib Overview