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所在平台: Udemy |
课程主页: https://www.udemy.com/course/sql-power-bi-data-analyst-ms-sql-ssrs-ssas-power-bi/
课程评论:没有评论
课程名称:数据分析师初学者项目 课程概述:数据分析是一种检查、清理、转化和建模数据的过程,目的是发现有用的信息、做出结论并支持决策。数据分析师在各个领域如金融、咨询、制造业、制药、政府和教育等行业需求旺盛。数据分析师需要具备细致入微的观察力、良好的沟通能力和高度的组织能力。不仅要理解数据,还需通过清晰的视觉、书面及口头沟通提供深入的见解和分析。 数据分析师的主要职责包括: - 制定记录管理流程和政策 - 确定提高效率和自动化的领域 - 设置和维护自动化数据处理流程 - 识别、评估和实施支持数据验证与清理的外部服务及工具 - 生成和跟踪关键绩效指标 - 开发和支持报告流程 - 监控和审计数据质量 - 与内部和外部客户联系,以全面理解数据内容 - 收集、理解并使用适当工具和技术记录详细的业务需求 - 设计并实施调查并分析调查数据 - 操作、分析和解释与企业相关的复杂数据集 - 准备面向内部和外部受众的报告,使用商业分析报告工具 - 创建数据仪表板、图表和可视化 - 提供行业和竞争对手基准 - 开采和分析大型数据集,进行有效推理,并成功展示给管理层。 本课程将帮助您建立数据分析师的项目组合,使您即使没有相关工作经验,也能向招聘经理和面试官展示您的技能与经验。课程中涵盖的实践项目包括: 1. **网络爬取**:学习如何查找并爬取自己的数据,使您能使用符合自己兴趣的数据集。 2. **数据清理**:掌握清理数据的技巧,确保数据的准确性和一致性。 3. **探索性数据分析(EDA)**:通过数据探索找到正确的问题进行分析。 4. **数据分析与转化**:学习如何对数据进行分析和转化。 5. **数据可视化**:掌握有效的可视化展示技术,支持商业决策。 本课程为您启动数据分析师职业生涯提供了坚实的基础与实践经验。
Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education.The ability to pay attention to detail, communicate well and be highly organised are essential skills for data analysts. They not only need to understand the data, but be able to provide insight and analysis through clear visual, written and verbal communication.Some responsibilities of a data analyst includes:Developing records management processes and policiesidentify areas to increase efficiency and automation of processesset up and maintain automated data processesidentify, evaluate and implement external services and tools to support data validation and cleansingproduce and track key performance indicatorsdevelop and support reporting processesmonitor and audit data qualityliaise with internal and external clients to fully understand data contentgather, understand and document detailed business requirements using appropriate tools and techniquesdesign and carry out surveys and analyse survey datamanipulate, analyse and interpret complex data sets relating to the employer's businessprepare reports for internal and external audiences using business analytics reporting toolscreate data dashboards, graphs and visualisationsprovide sector and competitor benchmarkingmine and analyse large datasets, draw valid inferences and present them successfully to management using a reporting toolIf you're getting ready to launch a new career as a data analyst, chances are you've encountered an age-old dilemma. Job listings ask for experience, but how do you get experience if you're looking for your first data analyst job?This is where your portfolio comes in. The projects you include in your portfolio demonstrate your skills and experience-even if it's not from a previous data analytics job-to hiring managers and interviewers. Populating your portfolio with the right projects can go a long way toward building confidence that you're the right person for the job, even without previous work experience.As an aspiring data analyst, you'll want to demonstrate a few key skills in your portfolio. These data analytics project ideas reflect the tasks often fundamental to many data analyst roles. The hands on projects covered in this course includes:Web scrapingWhile you'll find no shortage of excellent (and free) public data sets on the internet, you might want to show prospective employers that you're able to find and scrape your own data as well. Plus, knowing how to scrape web data means you can find and use data sets that match your interests, regardless of whether or not they've already been compiled. We will use Python and tools like Beautiful Soup to crawl the web for interesting data.Data cleaningA significant part of your role as a data analyst is cleaning data to make it ready to analyze. Data cleaning (also called data scrubbing) is the process of removing incorrect and duplicate data, managing any holes in the data, and making sure the formatting of data is consistent. We will perform some practical data cleaning.Exploratory data analysis (EDA)Data analysis is all about answering questions with data. Exploratory data analysis, or EDA for short, helps you explore what questions to ask. This could be done separate from or in conjunction with data cleaning. Either way, you'll want to accomplish the following during these early investigations. We will undertake a data exploration project in this course.Data Analysis & TransformationWe will also perform some data analysis and transformationData VisualizationData visualization is an important aspect of data analysis. As a data analyst you should be able to presentdata in a visual way that will help businesses make important decisions.