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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-data-science-numpy-pandas-scikit-learn/
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课程名称：数据科学中的Python - NumPy、Pandas与Scikit-Learn 课程概述：本课程是关于Python最强大的数据科学库的全面指南，旨在为您提供进行复杂数据分析项目所需的技能。课程适合希望深入数据科学领域的初学者以及希望拓展技能的有经验的程序员。您将学习使用Python这一数据科学领先编程语言来操作、分析和可视化数据。 课程开始于NumPy的探索，这是Python中数值计算的基础包。您将深入理解数组及面向数组的计算，这对性能密集型的数据分析至关重要。接着，课程将重点介绍Pandas，这是一个旨在数据处理和分析的库。您将学习如何处理Series和DataFrames，处理缺失数据，并执行合并、连接和分组等操作。最后，课程的最后一部分将专注于Scikit-Learn，这是一个提供高效机器学习和统计建模工具的库。在这一部分中，您将深入了解数据预处理、模型选择和评估，以及用于分类、回归、聚类和降维的一系列算法。 完成本课程后，您将牢牢掌握如何使用Python的主要数据科学库进行复杂的数据分析，为您自己开展数据驱动项目提供知识支持。 课程内容亮点： - NumPy练习：数组操作、随机数组生成、线性代数、矩阵计算等。 - Pandas练习：Series与DataFrame的操作、缺失值处理、数据分组与合并、数据准备等。 - Scikit-Learn练习：数据预处理、模型分类与回归、交叉验证、特征提取、聚类与降维等。 通过本课程，您将能够掌握Python在数据科学领域的基础知识和应用，为未来的职业发展打下坚实基础。
This course is a comprehensive guide to Python's most powerful data science libraries, designed to provide you with the skills necessary to tackle complex data analysis projects.This course is tailored for beginners who want to delve into the world of data science, as well as experienced programmers who wish to diversify their skill set. You will learn to manipulate, analyze, and visualize data using Python, a leading programming language for data science.The course begins with an exploration of NumPy, the fundamental package for numerical computing in Python. You'll gain a strong understanding of arrays and array-oriented computing which is crucial for performance-intensive data analysis.The focus then shifts to Pandas, a library designed for data manipulation and analysis. You'll learn to work with Series and DataFrames, handle missing data, and perform operations like merge, concatenate, and group by.The final section of the course is dedicated to Scikit-Learn, a library providing efficient tools for machine learning and statistical modeling. Here you'll delve into data preprocessing, model selection, and evaluation, as well as a broad range of algorithms for classification, regression, clustering, and dimensionality reduction.By the end of this course, you will have a firm grasp of how to use Python's primary data science libraries to conduct sophisticated data analysis, equipping you with the knowledge to undertake your own data-driven projects.Python for Data Science: Empowering Insight Through CodePython is the go-to language for data science, offering powerful libraries like NumPy for numerical computing, Pandas for data manipulation, and Scikit-learn for machine learning. Together, these tools enable efficient data analysis, transformation, and model building-making Python an essential skill for turning raw data into actionable insights.Some topics you will find in the NumPy exercises:working with numpy arraysgenerating numpy arraysgenerating numpy arrays with random valuesiterating through arraysdealing with missing valuesworking with matricesreading/writing filesjoining arraysreshaping arrayscomputing basic array statisticssorting arraysfiltering arraysimage as an arraylinear algebramatrix multiplicationdeterminant of the matrixeigenvalues and eignevectorsinverse matrixshuffling arraysworking with polynomialsworking with datesworking with strings in arraysolving systems of equationsSome topics you will find in the Pandas exercises:working with Seriesworking with DatetimeIndexworking with DataFramesreading/writing filesworking with different data types in DataFramesworking with indexesworking with missing valuesfiltering datasorting datagrouping datamapping columnscomputing correlationconcatenating DataFramescalculating cumulative statisticsworking with duplicate valuespreparing data to machine learning modelsdummy encodingworking with csv and json fillesmerging DataFramespivot tablesTopics you will find in the Scikit-Learn exercises:preparing data to machine learning modelsworking with missing values, SimpleImputer classclassification, regression, clusteringdiscretizationfeature extractionPolynomialFeatures classLabelEncoder classOneHotEncoder classStandardScaler classdummy encodingsplitting data into train and test setLogisticRegression classconfusion matrixclassification reportLinearRegression classMAE - Mean Absolute ErrorMSE - Mean Squared Errorsigmoid() functionentorpyaccuracy scoreDecisionTreeClassifier classGridSearchCV classRandomForestClassifier classCountVectorizer classTfidfVectorizer classKMeans classAgglomerativeClustering classHierarchicalClustering classDBSCAN classdimensionality reduction, PCA analysisAssociation RulesLocalOutlierFactor classIsolationForest classKNeighborsClassifier classMultinomialNB classGradientBoostingRegressor class
