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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-analysis/
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课程名称：R和Python的数据科学与机器学习 课程概述： 本课程提供了关于数据科学的全面介绍，重点是使用R和Python两种编程语言进行数据分析和机器学习。具体内容如下： 1. **R语言的数据科学**： - 学习R中的运算符、条件语句（如IF、嵌套IF）、循环结构（如FOR、While循环）等基本语法。 - 熟悉R的数据类型，包括向量、标量、矩阵及其访问方法。 - 数据导入与预处理，包括设置工作目录、数据集导入以及数据探索性函数（如str、summary、head、tail等）的使用。 - 数据操作与清理，利用Dplyr包进行数据过滤、变换、排序和汇总等。 - 数据可视化，包括柱状图、线图、箱线图等，利用可视化工具展示时间序列数据及其统计特征。 - 使用逻辑回归分析癌症缓解的数据集以及K均值聚类和市场篮子分析等关联分析方法。 2. **Python的数据科学**： - 学习Python的基础知识，包括数据类型、字符串处理、列表、字典等数据结构及其操作。 - 使用NumPy库进行数组的数学运算和形状操作，如数组的重塑、切分、堆叠等。 - 使用Pandas进行数据处理，创建、访问和操作数据框，对数据进行分组、排序和标准化处理。 - 运用Python进行线性回归、K均值聚类及数据可视化（使用matplotlib）。 - 结合测验和练习测试来巩固所学知识。 本课程通过结合实践与理论，帮助学员掌握数据科学以及机器学习的基本技能，是希望从事数据分析与建模工作的理想选择。
Data Science with R: Types of operators used in R,IF statements,IF then else statement,nested IF, ifelse() function,switch statement,FOR loop,While loop,repeat,break,next statement Data types in R-Vector,Scalar,Matrix,accessing a matrix by dimensions,creating matrices from vectors Importing the data set. Setting up the working directory,creating a duplicate file,Data Exploratory functions in R such as str, summary for descriptive statistics of data,names,head,tail,sd(standard deviation),var(variance),mean,minimum,level,dim,unique,duplicate,range,tolower,toupper. Functions in R such as apply,lapply,sapply,tapply,mapply Data manipulation in R-Dply,Filter,multiple filter,mutate,arrange,summarize. Functions in R such as apply,lapply,sapply,tapply,mapply Data manipulation in R-Dply,Filter,multiple filter,mutate,arrange,summarize. Data visualization in R-Bar graphs,Stacked bar,grouped bar graph Data Visualization in R Data Visualization in R-Line chart for time series data,Box plot to calculate mean, median, min ,max ,3rd quartile and 1st quartile values Logistic Regression using Cancer remission data set. Clustering using Kmeans Association Analysis in R using Market Basket analysis Machine Learning using R Data Science with Python: Basics of Python,importing sys library,platform,checking the platform,run a code,checking data type,performing calculations,concatenating of strings,conversion of values,working on tuples, Working with tuple continued,list,tuple,dictionaries(key value pair),set,In keyword,define a function,sorting using sorted function. Data mapping and indexing using enumerate,creating dictionaries using enumerate,sorting without order,reversing the list,append the list. Zip function,Iterate the value of tuple,If statement,else if statement,for loop,while,exception handling Mathematical on Array using Numpy,decalring the 2D array,3D array,dtype,numpy operations,boolean array. Shape manipulation on Arrays,flatten the data set,reshape the data set,resize the array,split array,stacking of arrays,broadcasting,scalar on arrays,transpose function,inverse function on arrays using linalg function,sum of diagonal elements using trace. Pandas,series containing data and label,list,series from a scalar,creating series from dictionary,access the element in series according to location using iloc() Vector operation with data alignment,creating data frame out of dictionary of equal length list,create Data frame by combining two series,create Data frame from array. Vector operation with data alignment,creating data frame out of dictionary of equal length list,create Data frame by combining two series,create Data frame from array Grouping by variable,Sorting of Data,standardization of data,apply standardized function to data frame Linear regression in python Clustering using Kmeans Data Visualization using matplotlib Machine learning using PythonQuizzes and practice tests included for knowledge test
