Python for Data Science, AI & Development

所在平台: Coursera

课程主页: https://www.coursera.org/learn/python-for-applied-data-science-ai

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课程简介

课程名称：数据科学、人工智能与开发的Python基础 课程概述： 本课程旨在为学习数据科学及编程入门的初学者提供友好的Python介绍。Python是全球最受欢迎的编程语言之一，运用Python基础推动各行业的商业解决方案的专业人才需求日益增长。课程内容覆盖Python的基础知识，通过实践练习和最终项目帮助学员掌握必须的编程技能。 完成本课程后，学员将能够自信地编写基本程序、处理数据，并解决现实世界中的问题。同时，课程为更高级的学习打下坚实的基础，并提升学员的职业竞争力。 本课程可应用于多个专业证书或认证项目，完成此课程可为以下项目的学习计入学分： - IBM 应用AI专业证书 - 应用数据科学专业化 - IBM 数据科学专业证书 完成上述任何项目后，除了获得Coursera的专业化完成证书外，学员还将获得IBM颁发的数字徽章以认可其在该领域的专业知识。 课程大纲： 1. **Python基础**： - 学习Python编程的基本概念，了解Python的用户及其优势，及其社区的多样性和包容性。 - 初识Jupyter Notebook环境，编写并管理代码单元，进行字符串等数据类型的基本操作，配合动手实验和交互练习。 2. **Python数据结构**： - 探索Python的重要数据结构，包括列表、元组、字典和集合，学习如何使用索引、切片和排序技术处理数据。 - 通过动手实验，实践列表和元组的关键操作，并掌握字典和集合的创建与操作。 3. **Python编程基础**： - 深入了解条件、分支、循环和函数等核心编程概念。 - 学习异常处理及面向对象编程的基础知识，包括类和对象的定义及使用。 4. **Python中的数据处理**： - 学习如何处理不同格式的文件（如文本、CSV和JSON），并通过核心Python库（如Pandas和NumPy）进行数据操作和数学运算。 - 为更高级的数据分析技术打下基础。 5. **API与数据收集**： - 探索通过API、网络爬虫等技术收集结构化与非结构化数据的各种方法，掌握数据收集的必要工具与知识。 通过以上模块的学习，学员将获得全面的Python基础，并在数据科学领域的学习和发展中迈出坚实的一步。

课程大纲

Name:Python Basics

Description:In this module, you will begin by exploring the fundamentals of Python programming. You will identify the users and benefits of Python and understand the diversity and inclusion efforts of the Python community. Next, you will be introduced to the Jupyter Notebook environment, where you will learn how to create, run, and manage code cells, as well as present and shut down notebooks. You will then learn to write your first Python program and work with different data types such as integers, floats, and strings. As you progress, you will use expressions and variables to perform basic operations and practice manipulating strings using indexing, escape sequences, and formatting techniques. Throughout the module, you will apply your learning through hands-on labs and interactive exercises.

Name:Python Data Structures

Description:In this module, you will explore essential Python data structures including lists, tuples, dictionaries, and sets. Starting with lists and tuples, you will learn how to store and manipulate collections of data using indexing, slicing, and sorting techniques. Through hands-on labs, you’ll practice key operations such as cloning lists and performing tuple manipulations. The module then introduces dictionaries, where data is stored in key-value pairs, and you will gain practical experience in creating and working with them. Finally, you will examine sets, an unordered collection that contains only unique elements, and learn how to perform set operations and logic-based tasks. By the end of this module, you’ll have a strong foundational understanding of these core Python data structures.

Name:Python Programming Fundamentals

Description:In this module, you will build a strong foundation in core Python programming concepts essential for applied data science. The module begins with conditions and branching, where you’ll learn to use comparison and logical operators to control the flow of your program. You will then move on to loops, including for and while loops, to iterate over sequences and perform repetitive tasks efficiently. Next, the module covers functions, teaching you how to use built-in Python functions and define your own function to structure and reuse code effectively. You’ll also explore exception handling, a critical concept that enables your program to handle errors gracefully and maintain robustness. Finally, you’ll be introduced to objects and classes, the foundation of object-oriented programming in Python. You’ll learn how to define your own classes and create objects, understand attributes and methods, and see how real-world problems can be modeled using OOP principles.

Name:Working with Data in Python

Description:In this module, you’ll begin by understanding the fundamentals of working with data in Python, focusing on how to read and write data to files in various formats such as text, CSV, and JSON. You’ll learn how to open, read, write, and manipulate files efficiently, which is essential for handling real-world data. As you progress, you’ll explore essential Python libraries for data manipulation and mathematical operations. Key libraries such as Pandas help you work with structured data in tabular formats, whereas NumPy supports numerical operations on arrays and matrices. By the end of the module, you’ll be equipped with the skills to efficiently handle, manipulate, and perform mathematical operations on data using Python, setting a strong foundation for more advanced data analysis techniques.

Name:APIs and Data Collection

Description:This module explores various techniques for collecting data, focusing on the use of APIs, web scraping, and working with different file formats. By the end of this module, you will be equipped with the necessary tools and knowledge to collect data from different sources, both structured and unstructured. The module includes hands-on labs, optional content for further exploration, and a final comprehensive exam to test your overall understanding of the course.

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