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

• Understand Python's Core Topics: Gain a firm grasp of fundamental Python concepts such as flow control, sequences, arrays, dictionaries, and file handling. This understanding forms the cornerstone of your Python programming journey. 
• Navigate Key Python Libraries: Develop proficiency in leveraging the power of Python's primary libraries, numpy and pandas. By the end of the course, you'll be confidently transforming, reshaping data, and handling large number sets. 
• Generate Insightful Visualizations: Learn how to create meaningful and visually appealing data visualizations using matplotlib. These skills will enable you to better communicate data-driven insights. 
• Efficient Data Handling: Acquire techniques to optimize your data handling processes, enhancing productivity and making your workflow more efficient. 
• Manage Errors Effectively: Become proficient in handling common challenges like syntax errors and exceptions, enhancing the reliability and robustness of your Python code. 
• Hands-on Experience with Web Notebooks: Gain practical experience using interactive web notebooks like iPython, Jupyter, and Zeppelin. These tools offer a dynamic platform for writing, testing, and debugging your Python code, enriching your learning experience. 

This 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.   

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.  

An Overview of Python 

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

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  

Flow Control 

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

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 

Working with files 

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

Functions 

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

Sorting 

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

Errors and Exception Handling 

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

Essential Demos 

• Importing Modules 
• Classes 
• Regular Expressions 

The standard library 

• Math functions  
• The string module 

Dates and times 

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

numpy 

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

Python and Data Science 

• Data Science Essentials 
• Working with Python in Data Science 

Working with Pandas 

• pandas overview 
• Dataframes 
• Reading and writing data 
• Data alignment and reshaping 
• Fancy indexing and slicing 
• Merging and joining data sets 

Working with matplotlib 

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

BONUS Day Four or Optional Topics 

For Dedicated / Private Classes:  

Leveraging AI for Python in Data Science 

Introduction to AI with Python for Data Analysis 

• Overview of AI Libraries 
• Setting Up Your Environment:  
• Understanding AI Models 
• Creating Your First Model 
• Evaluating Model Performance 

Practical AI Projects in Python 

• Set up a Python project for AI applications. 
• Data Handling 
• Model Development 
• Test and validate your AI model's effectiveness. 
• Applying Your Model 

Using GPT Tools for Record Analysis in Data Science 

• Introduction to GPT 
• Setting Up GPT Tools 
• Analyzing Text Data  
• Generating Insights  
• Practical Applications