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所在平台: Udemy |
课程主页: https://www.udemy.com/course/numpy-for-data-science-140-practical-exercises-in-python/
课程评论：没有评论
**课程名称：** NumPy for Data Science: 140+ Practical Exercises in Python (面向数据科学的NumPy：140+ Python实战练习) **课程概述：** 本课程提供NumPy库及其功能的全方位入门介绍。课程设计注重实践，包含140多个实战练习，帮助学习者扎实掌握NumPy在数据处理和分析中的应用。课程将涵盖以下核心概念： * **数组创建 (Array Routine Creation):** arange, zeros, ones, eye, linspace, diag, full, intersect1d, tri * **数组操作 (Array Manipulation):** reshape, expand\_dims, broadcast, ravel, copy\_to, shape, flatten, transpose, concatenate, split, delete, append, resize, unique, isin, trim\_zeros, squeeze, asarray, split, column\_stack * **逻辑函数 (Logic Functions):** all, any, isnan, equal * **随机采样 (Random Sampling):** random.rand, random.cover, random.shuffle, random.exponential, random.triangular * **输入输出 (Input and Output):** load,loadtxt, save, array\_str * **排序、搜索与计数 (Sort, Searching and Counting):** sorting, argsort, partition, argmax, argmin, argwhere, nonzero, where, extract, count\_nonzero * **数学运算 (Mathematical):** mod, mean, std, median, percentile, average, var, corrcoef, correlate, histogram, divide, multiple, sum, subtract, floor, ceil, turn, prod, nanprod, ransom, diff, exp, log, reciprocal, power, maximum, square, round, root * **线性代数 (Linear Algebra):** linalg.norm, dot, linalg.det, linalg.inv * **字符串操作 (String Operation):** char.add, char.split, char.multiply, char.capitalize, char.lower, char.swapcase, char.upper, char.find, char.join, char.replace, char.isnumeric, char.count **目标学员：** 本课程适合希望学习如何使用NumPy进行数据处理和分析的**数据科学家、数据分析师和开发人员**。无论您是数据科学初学者，还是有经验的从业者，希望加深对NumPy库的理解，本课程都非常适用。 **先修知识：** 无。 **课程大纲：** 无。
This course will provide a comprehensive introduction to the NumPy library and its capabilities. The course is designed to be hands-on and will include over 140+ practical exercises to help learners gain a solid understanding of how to use NumPy to manipulate and analyze data. The course will cover key concepts such as:Array Routine CreationArange, Zeros, Ones, Eye, Linspace, Diag, Full, Intersect1d, TriArray ManipulationReshape, Expand_dims, Broadcast, Ravel, Copy_to, Shape, Flatten, Transpose, Concatenate, Split, Delete, Append, Resize, Unique, Isin, Trim_zeros, Squeeze, Asarray, Split, Column_stackLogic FunctionsAll, Any, Isnan, EqualRandom SamplingRandom.rand, Random.cover, Random.shuffle, Random.exponential, Random.triangularInput and OutputLoad, Loadtxt, Save, Array_strSort, Searching and CountingSorting, Argsort, Partition, Argmax, Argmin, Argwhere, Nonzero, Where, Extract, Count_nonzeroMathematicalMod, Mean, Std, Median, Percentile, Average, Var, Corrcoef, Correlate, Histogram, Divide, Multiple, Sum, Subtract, Floor, Ceil, Turn, Prod, Nanprod, Ransom, Diff, Exp, Log, Reciprocal, Power, Maximum, Square, Round, RootLinear AlgebraLinalg.norm, Dot, Linalg.det, Linalg.invString OperationChar.add, Char.split. Char.multiply, Char.capitalize, Char.lower, Char.swapcase, Char.upper, Char.find, Char.join, Char.replace, Char.isnumeric, Char.count.This course is designed for data scientists, data analysts, and developers who want to learn how to use NumPy to manipulate and analyze data in Python. It is suitable for both beginners who are new to data science as well as experienced practitioners looking to deepen their understanding of the NumPy library.
