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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-driven-product-management-with-r/
课程评论：没有评论
课程名称：数据驱动的产品管理与R 课程概述： 本课程是专门为产品经理设计的一系列分析课程的第一部分，涵盖了一系列经过精心挑选的主题，如离群值分析、探索性数据分析（EDA）和同 cohort 分析。这些主题通过与产品管理相关的案例进行教学，帮助学员将所学知识立即应用于日常工作。课程的主要目标是使产品经理能够利用数据影响客户和利益相关者。 课程特色： 本课程并非编程语言课程，而是教授如何使用R作为工具来推进职业和商业目标。课程提供的R笔记本经过严格测试，确保在Windows和Mac上均可低门槛运行。学员可以专注于根据自身需求修改笔记本，而不必担心复杂的编程学习。 课程益处： 该课程是为产品经理高度定制的，帮助学员节省几个月学习R和将其应用于工作的时间。课程内容聚焦于实际应用，材料简明扼要，不会给学员带来大量无关的讲座和文件，从而避免困惑。 为何选择R？ R是一种被研究科学家和统计学家广泛使用的脚本语言，而非软件程序员。R易于学习，有助于快速获得结果，初学者也能快速掌握。R拥有活跃的社区并不断推出新功能，支持多种统计分析和机器学习模型，且其软件是免费的、稳定的，占用资源较少，适合云端运行，并能与主流IDE（如VS Code）无缝集成。 工作应用： 课程由一位产品经理为其他产品经理创建，代码样例可以直接运行并进行无限修改。R笔记本包含课堂所教的所有代码，可在任何Windows和Mac笔记本上运行。建议学员采取实践和好奇的态度，针对个人需求修改文件。 课程内容： 本课程集中于描述性分析技巧，以促进数据驱动的决策和跨职能合作。此外，还涵盖了入门级的数据工程主题（如EDA和数据管理）。课程初期介绍的这些主题为后续课程内容奠定基础。针对产品经理的三个关键职业技能包括：跨部门合作、创建可重复的数据分析环境和创建主数据集以便于跨部门合作。 学习时间及设备要求： 课程总时长约为5小时，共分9个部分。若每天腾出1小时，10天内可完成课程；也可选择集训型模式，周末一口气完成。相比传统的电子表格软件，R对设备的要求不高，8GB内存的Windows或Mac笔记本便可满足课程练习的需要。 若在学习过程中遇到问题，可以在课程中留言咨询，课程负责人会在24小时内回复。
What's this course about?This is the first part of a series of analytics courses that are fine-tuned for product managers. It covers a carefully-curated list of topics like outlier analysis, exploratory data analysis (EDA) and cohort analysis. These topics are taught using product management specific use cases for immediate application to your daily work. The overarching goal of the course is to enable product mangers influence customers and stakeholders using data.What's unique about this course? Why should I care?First, this is not a programming language course. This course teaches you to use R as a tool to advance your career and business goals. The R notebooks provided with this course are meant to be run with minimal training. They have been rigorously tested on Windows and Mac. Updates if any, will be posted in a timely manner. This was done so learners can focus on modifying the notebooks for their specific needs.How will it benefit me?This is a highly curated course with a very narrow target learner - the product manager. With this course, product managers will save months of time they would spend learning R and applying it to their work. The curriculum focuses on practical implementation so the material is concise and precise. You will not be bombarded with hours of lectures and hundreds of source files only to find yourself confused about what's next.Why R?R is a scripting language that is widely used by research scientists and statisticians - not software programmers. It is easy to learn and master and you get results instantaneously. If scientists, with little or no programming skills can master R, so can you. R has a very active and solid community that maintains existing functionality and regularly introduces new innovation. As R is statistical software, you will find several excellent packages for every statistical procedure imaginable. What's more, you can also write powerful ML models in R easily. The best part is that R is free and secure. It is not as CPU-hungry as most spreadsheet tools and can be run in the cloud as well. It also works seamlessly with popular IDEs like VS Code.How do I use it at work?Remember, this course has been created by a product manager for other product managers. The code samples can be run straight out of the box and modified endlessly.The R Notebooks contain all the code being taught in the class. They can be run on any Windows or Mac laptop. It is highly recommended that you take a hands-on and curious approach to this course. Modify the files to suit your needs.What does this course cover?This course focuses on descriptive analytic techniques to facilitate data-driven decision making and cross-functional collaboration. In addition, this course covers entry-level data engineering topics like EDA and data management. These topics are introduced early to serve as foundations for the rest of the course. Three key career skills are addressed for product managers: Cross Functional CollaborationCreating a Reproducible Data Analysis EnvironmentCreating a Master Dataset for Inter-departmental CollaborationStorytelling with DataBuilding Customer ProfilesSegmentation Using IndicatorsTranslating Feature Usage to RetentionLearning from Extreme CustomersAutomation of Data Analysis TasksAutomating the Data Curation ProcessCreating Reproducible ReportsIn future courses, I will cover prescriptive and predictive techniques.Are there any copyright issuesR is a very popular language and there are thousands of free and paid resources available on the internet. To avoid copyright infringement, I have developed the data set used in this course. It is not copied from any paid or free repository. All the code in this course has been developed by me.What if I have problems?If you have questions about the course, send me a note in the course and I will respond within 24 hours.How long will it take me?The total course duration is approximately 5 hours spanning 9 sections. By blocking off 1-hour or so a day, you can finish the course in 10 days. You can also go at in Boot Camp style and finish it over a weekend.To get the most out of this course, prioritize your learning time and stick to the plan. There is no shame is copy-pasting code and there are no brownie points for memorizing the function and parameter names. If you obsess over them, you will not do yourself any justice. Just understand the overall flow of each lesson and how the code is organized. Focus on running the notebook and studying the results. Then modify the code to suit your needs, run the notebooks, and study the results again. Rinse and repeat.What kind of machine do I need?In comparison to traditional spreadsheet software like Microsoft Excel, R is not a resource-intensive software. A Windows or Mac laptop with 8 GB of RAM is more than sufficient to run the exercises in this course. Check the R and R Studio sites for detailed system requirements.
