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所在平台: Udemy |
课程主页: https://www.udemy.com/course/datascience101/
课程评论：没有评论
课程名称：数据科学101：方法论、Python及数学基础 课程概述：欢迎参加本课程！我相信在课程结束时，你将学到很多有价值的技能。本课程分为三个部分，我将以不同的方式呈现每个部分的内容。在最后一个部分中，数据科学所需的基本数学知识将几乎完全通过白板演示展示。 课程的开头部分探讨了一些热情学习者常问的问题，如数据科学家究竟做什么、数据科学的最佳编程语言以及诸如“大数据”、“数据挖掘”及“机器学习与深度学习”的相关术语的比较。接下来，我们将通过一个医疗保险案例研究来探讨数据科学的方法论，你将看到数据专业人士常用的数据科学步骤和技术。你可能会惊讶地发现，除了数据科学家之外，还有其他职位。 如果你对机器学习和自然语言处理感兴趣，我们将一起构建一个简单的聊天机器人，帮助你更加清晰地理解这些系统的构建过程。 接下来的部分是关于Python在数据科学中的应用介绍。你将有机会掌握Python，每个部分后都有作业让你练习技能。到这部分结束时，你将理解Python的基础知识、决策和循环结构、Python函数以及如何处理嵌套数据和列表推导。 最后一部分深入探讨数据科学所需的基本数学知识。你将学习与数据科学相关的线性代数、概率和统计。我希望在线性代数部分介绍所有必要的概念和直觉，帮助你理解在数据拟合中常用的最小二乘法技术。我还希望花大量时间讨论概率，特别是经典概率与贝叶斯概率，因为对问题进行推理是数据科学中比单纯运行统计更为复杂的方面。 所以，不要等待，开始你的数据科学101之旅，掌握现代技能。如果你因任何原因对课程不满意，Udemy提供30天退款保证。
Welcome! Nice to have you. I'm certain that by the end you will have learned a lot and earned a valuable skill. You can think of the course as compromising 3 parts, and I present the material in each part differently. For example, in the last section, the essential math for data science is presented almost entirely via whiteboard presentation.The opening section of Data Science 101 examines common questions asked by passionate learners like you (i.e., what do data scientists actually do, what's the best language for data science, and addressing different terms (big data, data mining, and comparing terms like machine learning vs. deep learning). Following that, you will explore data science methodology via a Healthcare Insurance case study. You will see the typical data science steps and techniques utilized by data professionals. You might be surprised to hear that other roles than data scientists do actually exist. Next, if machine learning and natural language processing are of interest, we will build a simple chatbot so you can get a clear sense of what is involved. One day you might be building such systems.The following section is an introduction to Data Science in Python. You will have an opportunity to master python for data science as each section is followed by an assignment that allows you to practice your skills. By the end of the section, you will understand Python fundamentals, decision and looping structures, Python functions, how to work with nested data, and list comprehension. The final part will show you how to use the two most popular libraries for data science, Numpy, and Pandas.The final section delves into essential math for data science. You will get the hang of linear algebra for data science, along with probability, and statistics. My goal for the linear algebra part was to introduce all necessary concepts and intuition so that you can gain an understanding of an often utilized technique for data fitting called least squares. I also wanted to spend a lot of time on probability, both classical and bayesian, as reasoning about problems is a much more difficult aspect of data science than simply running statistics.So, don't wait, start Data Science 101 and develop modern-day skills. If you should not enjoy the course for any reason, Udemy offers a 30-day money-back guarantee.
