The main aim of this python for data science course is to give learners the basic skills in python programming which are essential in data science. From fundamental programming principles to advanced data handling capabilities, the course lays a solid ground that will enable students to manipulate and analyze data well. Aspiring Data scientists, analysts and anyone who wishes to apply Python for decision making based on data will find this curriculum beneficial. At the end of this course, you will have mastered how to handle real life datasets hence giving you an edge in today’s market full of information.

Environment Setup

• Linux: A step by step guide on python installation in Linux systems.
• Windows: Comprehensive instructions for installing Python on Windows platforms.
• MacOS: Detailed walkthroughs for Python setup on macOS environments.
• Virtual Environment: The importance of virtual environments and how to create and manage them for the purposes of isolating project dependencies.

Fundamentals

• Data Types: Data types including integers, floats, strings and booleans are among those found in Python.
• Variable: Understand what variables mean as far as containing and manipulating data is concerned.

Functions

• Declaration: Syntax and principle behind function declaration in python.
• Optional Parameters: This chapter explains how functions can be defined with optional parameters that make them flexible enough when called upon.
• Named Parameters: Explore the use of named parameters to improve the readability and clarity of function arguments.
• Variable-Length Argument Function: Implementation of functions that accept varying numbers of arguments making it possible for more than one design for each function to be done.
• Anonymous Function (Lambda): It delves into the concise syntax and utility of anonymous functions via the lambda keyword.

Collections

• List: Learn about lists, dynamic arrays that facilitate the storage of ordered elements.
• Tuple: Given their immutability property and diverse applications, let’s understand tuples as immutable ordered sequences.
• Set: Explore the concept of sets, unordered collections of unique elements.
• Dictionary: Master dictionaries, key-value pairs that enable efficient data retrieval.

Functional Programming

• Map, Filter, Reduce: Learning these advanced functions will make your data manipulation process easier. Map applies operations across data while filter selects desired data and reduces aggregates.

Data Manipulation Libraries:

• Numpy: This lecture introduces the Numpy library; a crucial tool with numerical operation capability. The library supports handling large-sized multi-dimensional arrays/matrices. Additionally, it contains some mathematical functions for array/matrix operations that are highly optimized in terms of computing time.

• Pandas: Here we have Pandas which is an important Python library for manipulating tabular numerical tables along with time series.

List Comprehensions

• Become more efficient at programming through concise and expressive lists comprehensions.

Loops

• Recognize the different loop structures in Python that can be used for iteration tasks.

Generators

• Explore the concept of generators for dealing with large datasets efficiently using lazy evaluation.

Decorators

• Understand the application of decorators to modify or extend the behavior of functions.

Object-Oriented Programming

• Discover object-oriented programming (OOP) principles in Python.