Overview of the Python Basics Module

Welcome to Python Basics#

This module serves as your foundation for learning Python as a powerful tool for data analytics. Whether you’re completely new to programming or looking to strengthen your fundamentals, these lessons will guide you through essential concepts that form the building blocks of data analysis with Python.

Python has become one of the most popular languages for data analytics, and for good reason. Its clear syntax makes it accessible to beginners, while its extensive libraries and capabilities make it powerful enough for complex data processing tasks. As you work through this module, you’ll develop the skills needed to write programs that can manage, process, and analyze large amounts of information.

What You’ll Learn#

This module consists of fourteen comprehensive lessons, each building upon the previous one. By the end of this module, you will be able to:

• Write and execute Python programs with confidence
• Store and manipulate data using variables and different data types
• Work with numbers, text, and collections of data
• Load and process datasets from CSV files
• Use loops to automate repetitive tasks
• Apply conditional logic to make decisions in your code
• Create reusable functions to organize your code
• Handle files and manage data persistence
• Write robust code with proper exception handling
• Use advanced list techniques like comprehensions
• Work with lambda functions for concise operations
• Apply filter and map functions for data transformation
• Organize data efficiently using dictionaries
• Perform basic data analysis on real-world datasets
• Calculate statistics like averages, sums, and frequencies
• Filter and transform data based on specific criteria

These skills form the essential toolkit you’ll use throughout your data analytics journey. Every advanced technique you learn later will build upon these fundamentals.

Module Structure#

Lesson 1: Programming in Python#

Start your journey by learning how to communicate with the computer through code. You’ll write your first Python programs, understand syntax rules, and perform basic arithmetic operations. This lesson introduces the print() function, comments, and the concept of a computer program.

Key Topics:

• Writing and running Python code
• Using the print() function
• Basic arithmetic operations (+, -, *, /, **)
• Understanding syntax and comments
• Order of operations

Lesson 2: Variables and Data Types#

Learn how to store information in variables and work with different types of data. You’ll understand how Python manages numbers (integers and floats) and text (strings), and how to manipulate both effectively.

Key Topics:

• Creating and naming variables
• Working with integers and floats
• String manipulation and concatenation
• Type conversion and checking
• Variable naming rules and best practices

Lesson 3: For Loops in Python#

Discover how to automate repetitive tasks using for loops. Instead of writing the same code over and over, you’ll learn to iterate through datasets efficiently.

Key Topics:

• Understanding loop syntax
• Iterating through lists
• Extracting values from datasets
• Calculating sums and averages
• Processing multiple data points

Lesson 4: Lists and For Loops#

Dive deeper into working with collections of data. You’ll learn how to organize information in lists, access specific elements, and combine lists with loops for powerful data processing.

Key Topics:

• Creating and manipulating lists
• Indexing and slicing
• List methods (append, extend, etc.)
• Reading CSV files
• Processing tabular data
• Nested lists for complex datasets

Lesson 5: Conditional Statements#

Learn how to write programs that make decisions based on data. Using if, else, and elif statements, you’ll create code that responds differently depending on the conditions it encounters.

Key Topics:

• Writing if statements
• Using comparison operators (==, !=, <, >, <=, >=)
• Combining conditions with else and elif
• Logical operators (and, or, not)
• Filtering data based on conditions

Lesson 6: Dictionaries#

Work with Python’s key-value data structure that’s essential for organizing and accessing data efficiently. Dictionaries are fundamental to data analytics and are used extensively in real-world projects.

Key Topics:

• Creating and accessing dictionaries
• Adding, modifying, and removing items
• Dictionary methods (keys, values, items)
• Iterating over dictionaries
• Nested dictionaries
• Using dictionaries for data aggregation and lookup tables

Lesson 7: Frequency Tables with Dictionaries#

Learn how to use dictionaries to count occurrences and build frequency tables—a fundamental technique in data analysis. You’ll apply this to real datasets to summarize and understand data patterns.

Key Topics:

• Building frequency tables from scratch
• Counting unique values in datasets
• Using dictionaries for data aggregation
• Calculating proportions and percentages
• Practical applications in data analysis

Lesson 8: Functions - Part 1#

Learn how to organize your code into reusable blocks called functions. You’ll discover how to define your own functions, pass data to them through parameters, and return results to use elsewhere in your programs.

Key Topics:

• Defining functions with def
• Parameters and arguments
• Return values
• Function scope and local vs global variables
• Writing modular, reusable code
• Built-in functions vs custom functions

Lesson 9: Functions - Part 2#

Continue exploring functions with more advanced topics including multiple parameters, default values, and debugging techniques. Learn best practices for writing clean, maintainable function-based code.

Key Topics:

• Multiple parameters and keyword arguments
• Default parameter values
• Variable scope in depth
• Debugging functions
• Best practices for function design
• Common function patterns in data analytics

Lesson 10: Working with Files#

Master the essential skill of reading data from files and writing results back to storage. You’ll work extensively with CSV files and learn best practices for file handling in data analytics projects.

Key Topics:

• Opening and closing files
• Reading file content
• Writing data to files
• Using the with statement
• Working with CSV files in depth
• File paths and error handling

Lesson 11: Exception Handling#

Learn how to write robust programs that can handle errors gracefully. Understanding exception handling is crucial for creating reliable data processing scripts that won’t crash when encountering unexpected data.

Key Topics:

• Understanding exceptions and errors
• Using try and except blocks
• Handling specific exception types
• The finally clause
• Raising exceptions
• Best practices for error handling in data workflows

Lesson 12: List Comprehension#

Discover Python’s elegant syntax for creating and transforming lists in a single line of code. List comprehensions make your code more concise and often more readable than traditional loops.

Key Topics:

• Basic list comprehension syntax
• Conditional list comprehensions
• Nested list comprehensions
• Performance benefits
• When to use comprehensions vs loops
• Applying comprehensions to data filtering and transformation

Lesson 13: Lambda Functions#

Explore anonymous functions—small, single-expression functions that you can define inline. Lambda functions are particularly useful when working with data processing operations that require simple function arguments.

Key Topics:

• Lambda function syntax
• Differences between lambda and regular functions
• When to use lambda functions
• Common use cases in data analytics
• Limitations of lambda functions
• Combining lambda with other functions

Lesson 14: Filter and Map Functions#

Learn two powerful built-in functions for data transformation. filter() helps you select specific items from datasets, while map() lets you apply transformations to every item efficiently.

Key Topics:

• Using filter() to select data
• Using map() to transform data
• Combining filter and map with lambda functions
• Practical data processing examples
• Alternatives: list comprehensions vs filter/map
• Chaining operations for complex transformations

Learning Approach#

Each lesson in this module follows a consistent, beginner-friendly approach:

1. Clear Explanations: Concepts are introduced with straightforward language and real-world context
2. Code Examples: Every concept includes working code examples you can run and modify
3. Visual Aids: Figures and diagrams help illustrate key concepts
4. Hands-On Practice: Exercises throughout each lesson let you apply what you’ve learned
5. Real Datasets: You’ll work with actual CSV files to experience authentic data analysis tasks

Working with Datasets#

Throughout this module, you’ll work with several datasets that simulate real-world scenarios:

PageGarden.csv - A fictional dataset representing an online bookstore with information about books including titles, prices, reviews, and ratings.

bookstore_apps.csv - Sample data about digital bookstore apps, containing details like pricing, user ratings, and categories.

These datasets let you practice realistic data analytics tasks in a controlled learning environment. You’ll learn to:

• Read data from CSV files into Python
• Store data in lists and nested lists
• Extract specific information from rows and columns
• Calculate meaningful statistics
• Filter data based on criteria

Prerequisites#

No prior programming experience is required to start this module. If you’re new to Python, we recommend:

1. Setting up Python on your computer (Installation Guide)
2. Choosing a code editor like Visual Studio Code (Setup Guide)
3. Being comfortable with basic computer operations like creating folders and running programs

If you’re completely new to programming, don’t worry—we start from the very beginning and build up gradually.

What Comes Next#

After completing this Python Basics module, you’ll have a solid foundation for more advanced topics in data analytics, including:

• Data manipulation with pandas
• Data visualization with matplotlib and seaborn
• Statistical analysis
• Working with databases
• Machine learning fundamentals

The skills you develop here will serve as building blocks for every data analytics task you encounter in the future.

Getting Started#

Ready to begin? Start with Lesson 1 - Programming in Python and work through each lesson in order. Take your time with each concept, run all the code examples, and complete the exercises. Learning to program is a skill that develops through practice, so don’t hesitate to experiment and try things beyond what’s in the lessons.

Remember: every expert programmer started exactly where you are now. With consistent effort and practice, you’ll be analyzing data with Python in no time.

Let’s get started!