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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-data-scientist-and-data-analyst-professional-test/
课程评论：没有评论
**课程名称：** Python数据科学家与数据显示分析师专业认证 **课程概述：** 本课程是一项在线测试，旨在评估学员运用Python及其数据科学库（Pandas, NumPy, Scipy, Scikit-learn）解决数据科学问题的能力。测试内容涵盖： * Python编程基础 * Python数据结构 * Python面向对象编程 * 使用Python进行探索性数据分析 * 使用各种算法进行数据分类 * 数据操作，如聚合、分组、排序和清洗 * 构建机器学习模型 **附加内容（Bonus Part）：** * **Git与GitHub：** * Git基础概念与常用命令 * 仓库的克隆、分支、合并、暂存(stash) * 交互式添加、reflog、cherry-picking * rebase、bisect * fetch与pull、多仓库操作 * 代码推送(push)、Pull Requests * Git日志、squashing commits * cherry-picking与三方合并 * Git Hooks * **GitHub进阶：** * GitHub概览 * SSH认证 * GitHub仓库与分支管理 * GitHub标签(tags)与发布(releases) * 差异比较 * GitHub Issues与Gists * GitHub组织管理 * 社交化编码 **课程目标：** 学员需运用所学知识解决数据科学问题和基础Python问题。本课程旨在提升学员在数据获取、清洗、处理、建模、分析和可视化等Python数据科学技术方面的熟练度。同时，也证实学员在高阶数据分析能力，包括： * 不确定状況下的决策 * 基于数据的决策 * 预测建模 * 模型选择 此外，课程还考察学员使用Python进行文件处理和编程操作的能力，重点使用NumPy, Pandas, Matplotlib, Seaborn, 和 Scikit-learn库。附加的Git和GitHub部分，旨在帮助学员理解版本控制、软件发布、自动化流水线等概念。
This Course about Python Data Science online test evaluates a candidate's proficiency in using Python and its data science libraries (Pandas, NumPy, Scipy, and Scikit-learn) through questionnaire challenges. The test focuses on:Introduction to Python ProgrammingPython Data StructuresPython Object-Oriented ProgrammingExploratory Data Analysis using PythonClassifying data with various algorithms.Performing data operations such as aggregating, grouping, sorting, and cleaning.Constructing machine learning models.Bonus Part: Git And GitHubGit BasicsGit CommandsClone a RepositoryGit BranchingMergingGit StashGit Add InteractiveReflogCherry PickingGit RebaseGit BisectFetching and Pulling ContentWorking With Multiple RepositoriesPushing CodePull RequestsGit LogSquashing CommitsCherry-Picking and Three-Way MergesGit HooksAdvanced: Beyond the BasicsGitHub OverviewSSH AuthenticationGitHub RepositoryGitHub Repository BranchesGitHub Tags and ReleasesComparing DifferencesSocial CodingGitHub IssuesGitHub GistsGitHub OrganisationsAll are expected to apply best of their knowledge to address data science problems and Basic Python problems. This course is built in such a way that every individual's proficiency in various Python data science techniques, including data acquisition, cleaning, manipulation, modeling, analysis, and visualization. It confirms expertise in data analytics, such as:Decision-making under uncertaintyData-based decision-makingPredictive modelingModel selectionBasic Python Data StructureAdditionally, it measures skills in using Python for file processing and programming operations with NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn libraries.Bonus part is added as Git and GitHub so that everyone can learn about versioning, and how software is released, automated pipelines and much more.
