You’re almost there! This is the eight course of the Google Advanced Data Analytics Certificate. In this course, you have the opportunity to complete an optional capstone project that includes key concepts from each of the six preceding courses. During this capstone project, you'll use your new skills and knowledge to develop data-driven insights for a specific business problem.


Google Advanced Data Analytics Capstone
本课程是 Google Advanced Data Analytics 专业证书 的一部分

顶尖授课教师
60,420 人已注册
包含在 中
您将学到什么
Examine data to identify patterns and trends
Build models using machine learning techniques
Create data visualizations
Explore career resources
您将获得的技能
要了解的详细信息

添加到您的领英档案
4 项作业
了解顶级公司的员工如何掌握热门技能

积累 Data Analysis 领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 通过 Google 获得可共享的职业证书

该课程共有3个模块
To start, you’ll be provided with an overview of the optional capstone project and how it differs from the end-of-course projects. You’ll also receive helpful suggestions for successfully completing the capstone project. Finally, you'll learn how to incorporate your completed capstone project into your professional portfolio.
涵盖的内容
7个视频6篇阅读材料3个作业2个非评分实验室
You’ll explore AI opportunities in the field of advanced data analytics.
涵盖的内容
7个视频4篇阅读材料1个作业
You’ll complete the final tasks necessary to earn your Google Advanced Data Analytics Certificate badge. Congratulations!
涵盖的内容
2个视频4篇阅读材料1个插件
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
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学生评论
1,319 条评论
- 5 stars
87.42%
- 4 stars
10.60%
- 3 stars
1.36%
- 2 stars
0.37%
- 1 star
0.22%
显示 3/1319 个
已于 Sep 13, 2023审阅
I learned a great deal. This is a well-designed online course. I'd highly recommend it to others seeking an entry point into the data realm.
已于 Sep 2, 2024审阅
Nice and very challenging training but this knowledge very usefully for our organization to achieving target.
已于 Sep 10, 2023审阅
excelent course for developing skills on EDA process and good start on creating machine learning models that fits the data.
常见问题
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.
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