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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-machine-learning-and-analytics-without-coding/
课程评论：没有评论
课程名称：无编码的数据科学、机器学习和分析 概述：你想通过学习最受欢迎的技能来提升职业发展吗？对数据科学感兴趣但因需学习编程而感到畏惧？本课程将教你如何解决真实的数据科学商业问题，这些问题是客户愿意花费数十万美元来解决的。虽然我不会将你培养成数据科学家，但可以教授你从第一天起便能增值并解决商业问题的技能。 与大多数课程不同，本课程有以下几点亮点： 1. 从问题解决开始，而不是编码。许多学生在尝试编写几行无意义的代码之前感到沮丧，因此我认为从编程开始是错误的。数据科学家的核心价值在于解决问题，而非编写计算机可以理解的代码。 2. 案例基于真实客户的工作。本课程的示例与真实客户问题相关，而不是使用Kaggle数据集等其他课程中的示例。我们关注的是帮助企业提升销售和优化团队表现的实际问题。 3. 可视化工作流。使用KNIME的可视化工作流提供了更好的数据探索、清洗和建模方式，也让你更容易向非数据科学家解释你的过程，从而与业务其他部分更好地协作。 总结：本课程涵盖机器学习工作流的完整过程，从数据和商业理解、探索、清理、建模到模型评估。我们还将讨论如何及可以变化什么，以在企业中创造影响的实用方面。
Do you want to super charge your career by learning the most in demand skills? Are you interested in data science but intimidated from learning by the need to learn a programming language?I can teach you how to solve real data science business problems that clients have paid hundreds of thousands of dollars to solve. I'm not going to turn you into a data scientist; no 2 hour, or even 40 hour online course is able to do that. But this course can teach you skills that you can use to add value and solve business problems from day 1.This course is different than most for several reasons:1. We start with problem solving instead of coding. I feel like starting to code before solving problems is misguided; many students are turned off by hours of work to try to write a couple of meaningless lines rather than solving real problems. The key value add data scientists make is solving problems, not writing something in a language a computer understands.2. The examples are based on real client work. This is not like other classes that use Kaggle data sets for who survived the Titanic, or guessing what type of flower it is based on petal measurements. Those are interesting, but not useful for people wanting to sell more products, or optimize the performance of their teams. These examples are based on real client problems that companies spent big money to hire consultants (me) to solve.3. Visual workflows. KNIME uses a visual workflow similar to what you'll see in Alteryx or Azure Machine Learning Studio and I genuinely think it is the future of data science. It is a better way of visualizing the problem as your are exploring data, cleaning data, and ultimately modeling. It is also something that makes your process far easier to explain to non-data scientists making it easier to work with other parts of your business.Summary: This course covers the full gamut of the machine learning workflow, from data and business understanding, through exploration, cleaning, modeling, and ultimately evaluation of the model. We then discuss the practical aspects of what you can change, and how you can change it, to drive impact in the business.
