Python Training

Lesson 1 • Installing Python and Dev Tools

Covers Python interpreter installation, version selection, and IDE setup. Establishes the baseline environment needed for all subsequent coding exercises.

Lesson 2 • Basic Input, Output, and Operators

Covers print(), input(), and arithmetic, comparison, and logical operators. These primitives appear in every program written in the course.

Lesson 3 • Variables, Data Types, and Literals

Teaches dynamic typing, built-in data types, and literal notation. Understanding types is prerequisite to all data manipulation chapters.

Lesson 1 • Comprehensions and Functional Iteration

Teaches list, dict, and set comprehensions alongside map() and filter(). Concise iteration patterns improve readability in data-heavy programs.

Lesson 2 • for Loops and Iteration

Covers iterating over sequences and using range(). Loop patterns introduced here recur in collections, files, and data processing chapters.

Lesson 3 • while Loops and Loop Control

Introduces condition-driven loops and break, continue, and pass statements. These controls enable precise flow management in complex algorithms.

Lesson 1 • Lambda Functions and Decorators

Introduces anonymous functions and function-wrapping decorators. These tools appear extensively in frameworks and functional-style code.

Lesson 2 • Defining and Calling Functions

Covers def syntax, return values, and calling conventions. Functions are the primary unit of reuse in all subsequent project work.

Lesson 3 • Advanced Parameter Techniques

Teaches default values, keyword arguments, *args, and **kwargs. These patterns enable flexible APIs used in libraries and frameworks later.

Lesson 1 • Lists: Creation and Manipulation

Covers list indexing, slicing, and all mutating methods. Lists are the most common structure in data processing and algorithm work.

Lesson 2 • Tuples and Immutable Sequences

Teaches tuple creation, packing, unpacking, and use cases. Immutability makes tuples ideal for fixed records and dictionary keys.

Lesson 3 • Sets and Set Operations

Introduces set creation, membership testing, and mathematical operations. Sets enable fast deduplication and relationship queries in data pipelines.

Lesson 4 • Dictionaries: Keys, Values, and Methods

Covers dict creation, access patterns, iteration, and merging. Dicts underpin JSON handling, configuration, and caching in real applications.

Lesson 1 • Methods: Instance, Class, and Static

Covers the three method types and their appropriate use cases. Correct method choice clarifies intent and improves API design.

Lesson 2 • Inheritance and Method Overriding

Introduces single and multiple inheritance, super(), and method resolution. Inheritance reduces duplication across related class families.

Lesson 3 • Encapsulation and Special Methods

Covers name mangling, dunder methods, and operator overloading. These features produce intuitive, Pythonic class interfaces.

Lesson 1 • Exception Handling

Teaches try, except, else, finally, and custom exceptions. Proper error handling prevents crashes and enables graceful degradation.

Lesson 2 • Working with CSV and JSON Files

Introduces the csv and json standard-library modules for structured data. These formats are ubiquitous in data exchange and API integration.

Lesson 3 • Reading and Writing Files

Covers open(), file modes, context managers, and binary vs. text I/O. File handling is essential for data ingestion and output in real projects.

Lesson 1 • Data Cleaning and Transformation

Covers missing value handling, type casting, and apply() transformations. Clean data is a prerequisite for accurate analysis and modeling.

Lesson 2 • NumPy Statistical and Linear Algebra Tools

Introduces aggregation functions, random number generation, and matrix operations. These tools underpin machine learning preprocessing and simulation.

Lesson 3 • Grouping, Merging, and Aggregation

Introduces groupby(), merge(), and pivot tables for summarizing data. These operations replicate SQL-style analytics entirely within Python.

Lesson 4 • NumPy Arrays and Operations

Covers ndarray creation, indexing, broadcasting, and vectorized math. NumPy efficiency is the backbone of scientific and data-science workflows.

Lesson 1 • Scheduling and Task Automation

Introduces scheduled scripts, argument parsing, and logging. Production automation requires reliable scheduling and observable execution.

Lesson 2 • Capstone Project Planning and Delivery

Guides students through scoping, building, and presenting a complete project. Synthesizes all core skills into a portfolio-ready deliverable.

Lesson 3 • File System and OS Automation

Teaches os, pathlib, and shutil for file and directory automation. Automating repetitive file tasks saves significant time in professional workflows.