Python for Data Scientists: From Fundamentals to Advanced Analysis

Ready to unlock the full power of Python for data science? Whether you’re transitioning from Excel or R, or just starting your data science journey, this course will transform the way you work with data. We’ve designed this learning experience to take you from your first Python script to implementing sophisticated data analysis workflows that you can use in your daily work.

Python has become the language of choice for data scientists worldwide - and for good reason. Its elegant syntax makes it easy to learn, while its powerful ecosystem of libraries lets you tackle complex data challenges with just a few lines of code. This course provides a structured path that builds your skills progressively, ensuring you develop both the foundational knowledge and practical expertise needed for real-world data science.

Throughout this course, you’ll progress from basic Python concepts to sophisticated data manipulation and analysis techniques. You’ll learn how to work with Jupyter Notebooks, manipulate data using powerful libraries, and implement efficient data processing workflows. We emphasise practical, hands-on learning with real-world datasets and scenarios you’re likely to encounter in your data science journey.

By the end of this course, you’ll be able to write efficient Python code, work with essential data science libraries, and implement sophisticated data analysis workflows. Whether you’re looking to transition into a data science role, enhance your current analytical capabilities, or simply understand how to better work with data using Python, this course will provide you with the knowledge and practical skills you need.

Learning Outcomes

Upon completion of this course, participants will be able to:

• Write efficient and idiomatic Python code for data analysis
• Use Jupyter Notebooks effectively for interactive data exploration
• Manipulate and analyse data using Python’s core data structures
• Work with files and handle different data formats
• Implement data processing workflows using Python’s standard library
• Apply Python’s powerful comprehension syntax for data transformation
• Handle errors and exceptions in data processing pipelines
• Use regular expressions for text data processing and cleaning
• Create modular and reusable code for data analysis projects
• Apply Pythonic principles to write more elegant and efficient code

Course Outline

Module 1: Python Foundations

• Understanding Python as a dynamic language for data science
• Setting up your development environment (Python installation and IDEs)
• Introduction to Jupyter Notebooks
• Overview of key data science libraries (NumPy, Pandas, Matplotlib)
• Essential Python resources and documentation

Module 2: Getting Started with Python

• Working with numbers and basic mathematics
• String operations and text manipulation
• Basic input and output operations
• Variables and data types
• Code formatting and style guidelines

Module 3: Flow Control in Python

• Understanding if statements and conditional logic
• Working with for loops and the range function
• While loops and their applications
• Loop control with break and continue
• Practical examples in data processing

Module 4: Working with Data Types

• Understanding Python’s object-oriented nature
• Mutable vs immutable types
• Working with strings and their methods
• Date and time operations
• Type conversion and checking

Module 5: Core Data Structures

• Lists and their operations
• Tuples and their use cases
• Indexing and slicing sequences
• Nested data structures
• Memory efficiency considerations

Module 6: Advanced Data Structures

• Dictionaries for key-value data
• Sets for unique collections
• Dictionary and set operations
• Performance considerations
• Choosing the right data structure

Module 7: Data Transformation with Comprehensions

• Understanding list comprehensions
• Dictionary comprehensions
• Set comprehensions
• Nested comprehensions
• Best practices and when to use them

Module 8: Functions

• Function definition and basic syntax
• Parameters and arguments (including *args and **kwargs)
• Return values and argument unpacking
• Lambda functions
• Function documentation and best practices

Module 9: Writing Pythonic Code

• Understanding the Zen of Python
• Working with sequences effectively
• Using built-in functions (enumerate, zip, etc.)
• Writing clear and idiomatic code
• Best practices for data science applications

Module 10: Jupyter Notebooks

• Understanding Jupyter Notebooks architecture
• Working with code and markdown cells
• Essential keyboard shortcuts and magic commands
• Notebook organization and best practices
• Debugging and performance optimization

Module 11: File Operations and I/O

• Reading and writing text files
• Working with different file formats
• File handling best practices
• Error handling in file operations
• Processing large files efficiently

Module 12: Working with Modules

• Understanding module system and imports
• Creating custom modules
• Working with packages
• Module search path and reloading
• Best practices for module organization

Module 13: Python Standard Library

• Working with command line arguments (argparse)
• System operations (os and sys modules)
• File management utilities (shutil, glob)
• Working with paths
• Running external commands

Module 14: Error Handling and Debugging

• Understanding exceptions
• Try-except block structure
• Handling multiple exceptions
• Creating custom exceptions
• Debugging techniques

Module 15: Text Processing with Regular Expressions

• Understanding pattern matching basics
• Working with pattern elements and quantifiers
• Using groups and capturing
• Regular expression methods in Python
• Best practices for text processing
• Pattern compilation and optimization

Conclusion

• Review of key concepts
• Best practices recap
• Next steps in your Python data science journey
• Additional resources and learning paths
• Building a sustainable practice routine

Intended Audience

This course is designed for data analysts, researchers, and professionals who want to leverage Python for data analysis and scientific computing. It's ideal for those transitioning from other analytical tools (like R or Excel) to Python, as well as those new to programming who need to work with data. The course assumes no prior Python experience but does expect familiarity with basic mathematical and statistical concepts.

Prerequisites

Those attending this course should meet the following:

• Basic understanding of mathematics and statistics
• Familiarity with spreadsheet software (e.g., Excel)
• Comfort with basic computer operations