|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-python-for-data-science-by-quiz/
课程评论：没有评论
该课程名为“Python 数据科学测验”，旨在为学生提供针对数据科学应用而设计的Python编程基本技能。课程内容全面，涵盖数据类型、条件语句、异常处理、函数、模块、面向对象等核心概念，以及Matplotlib、NumPy和Pandas等关键库。学完本课程后，学生将打下坚实的Python编程基础，能够有效地进行数据处理和分析，以应对各种数据科学任务。 课程大纲包括：Python编程入门（Python概述、开发环境搭建）、Python数据类型（数值类型、序列、映射、集合和布尔值）、条件语句（if/else/elif、比较与逻辑运算符、嵌套条件）、异常处理（异常识别、try/except块、自定义异常）、函数（定义调用、参数返回值、作用域、lambda函数）、模块（导入使用、常用模块、自定义模块）、面向对象编程（OOP概念、类和对象、继承多态、封装抽象）、Matplotlib数据可视化（基本绘图、图表定制、子图注解）以及NumPy数值计算（ndarray、数组运、切片索引、随机数统计）和Pandas数据处理与分析（Series/DataFrame、数据加载清洗、数据转换、数据分析任务）。
The "Python Test for Data Science" course is designed to equip students with essential programming skills in Python specifically tailored for data science applications. This comprehensive course covers fundamental concepts, such as data types, conditional statements, exception handling, functions, modules, object-oriented programming (OOP), and key libraries including Matplotlib, NumPy, and Pandas. By the end of this course, students will have a solid foundation in Python programming, enabling them to effectively manipulate and analyze data for various data science tasks.Course Outline:Introduction to Python ProgrammingOverview of Python and its applications in data scienceSetting up the development environment (Python installation and IDEs)Python Data TypesNumeric data types (integers, floats, complex numbers)Sequences (strings, lists, tuples)Mapping types (dictionaries)Sets and booleansConditional Statementsif, else, and elif statementsComparison operators and logical operatorsNested conditionalsException HandlingUnderstanding exceptions and error handlingHandling exceptions using try and except blocksRaising and catching custom exceptionsFunctionsDefining and calling functionsFunction parameters and return valuesScope and variable visibilityLambda functions and built-in functionsModulesImporting and using modules in PythonExploring commonly used modules for data scienceCreating and organizing your own modulesObject-Oriented Programming (OOP)Introduction to OOP concepts (classes, objects, attributes, methods)Defining and using classes in PythonInheritance and polymorphismEncapsulation and abstractionData Visualization with MatplotlibIntroduction to Matplotlib for creating visualizationsPlotting basic graphs (line plots, scatter plots, bar plots)Customizing plots (labels, titles, legends)Creating subplots and adding annotationsNumerical Computing with NumPyIntroduction to NumPy and its multidimensional array object (ndarray)Performing mathematical operations on arraysArray slicing and indexingWorking with random numbers and basic statisticsData Manipulation and Analysis with PandasIntroduction to Pandas and its core data structures (Series, DataFrame)Loading and cleaning dataManipulating and transforming dataPerforming data analysis tasks (filtering, grouping, aggregating)
