Data science is a dynamic and growing career field that demands knowledge and skills-based in SQL to be successful. This course is designed to provide you with a solid foundation in applying SQL skills to analyze data and solve real business problems.
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课程信息
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第 4 门课程（共 4 门）
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中级
完成时间大约为35 小时
英语（English）
字幕：英语（English）
您将学到的内容有
Develop a project proposal and select your data
Perform descriptive statistics as part of your exploratory analysis
Develop metrics and perform advanced techniques in SQL
Present your findings and make recommendations
您将获得的技能
Presentation SkillsData AnalysisSQLcreating metricsExploratory Data Analysis
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 4 门课程（共 4 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为35 小时
英语（English）
字幕：英语（English）
提供方
加州大学戴维斯分校
UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, veterinary medicine, sustainability, environmental and biological sciences, and technology. With four colleges and six professional schools, UC Davis and its students and alumni are known for their academic excellence, meaningful public service and profound international impact.
教学大纲 - 您将从这门课程中学到什么
完成时间为 9 小时
Getting Started and Milestone 1: Project Proposal and Data Selection/Preparation
In this first milestone, you will select your client and import your dataset. You will begin to explore your data to understand it and make assumptions about your data. You will draft a project proposal to act as a guide as you explore your data and prove or disprove your hypotheses.
完成时间为 9 小时
12 个视频 （总计 48 分钟）, 4 个阅读材料, 1 个测验
12 个视频
Milestone 1 Introduction1分钟
The Proposal Process3分钟
Import of Elon Musk Data5分钟
Initial Feature Exploration / Hypotheses8分钟
Entity Relationship Diagram (ERD) for Analysis2分钟
Data Models, Part 1: Thinking About Your Data5分钟
Data Models, Part 2: The Evolution of Data Models3分钟
Data Models, Part 3: Relational vs. Transactional Models5分钟
SQL in Notebooks3分钟
Import Data2分钟
Introduction of Data of Unknown Quality1分钟
4 个阅读材料
A Note from UC Davis10分钟
Choose Your Client/Dataset30分钟
Connecting to Mode Analytics20分钟
Welcome to Peer Review Assignments!10分钟
完成时间为 8 小时
Milestone 2: Descriptive Stats & Understanding Your Data
In this milestone, you will start to execute your project proposal. You will start looking at your data and perform initial statistic models to explore your data and determine what you have available to you.
完成时间为 8 小时
7 个视频 （总计 39 分钟）, 1 个阅读材料, 1 个测验
7 个视频
Importance of Understanding Your Data2分钟
Foundational Stats in SQL/Sheets9分钟
Pandas Teach on Stats5分钟
Visualization with raw graphics.io6分钟
Impact of Findings on Hypotheses5分钟
Statistics Refresher (Optional)8分钟
1 个阅读材料
Additional Resources20分钟
完成时间为 8 小时
Milestone 3: Beyond Descriptive Stats (Dive Deeper/Go Broader)
In this milestone, you will go beyond the descriptive statistics you completed in the last milestone. This milestone is really about diving deeper to analyze your data, beyond descriptive stats. Maybe you need to analyze qualitative data or textual data to get a full picture.
完成时间为 8 小时
5 个视频 （总计 28 分钟）
5 个视频
TF-IDF for Word Frequency / Theme Analysis7分钟
Text Analysis of Elon Musk Tweets5分钟
Create a New Metric5分钟
Analyze Results7分钟
完成时间为 10 小时
Milestone 4: Presenting Your Findings (Storytelling)
In this milestone, you will present your findings. You will identify your audience and create a presentation tailored to them. You will be able to tell the story of analyses and make recommendations.
完成时间为 10 小时
12 个视频 （总计 61 分钟）, 7 个阅读材料, 1 个测验
12 个视频
Sample Output / Presentation7分钟
Module Introduction1分钟
Working with Text Strings7分钟
Working with Date and Time Strings5分钟
Date and Time Strings Examples5分钟
Case Statements7分钟
Views6分钟
Data Governance and Profiling5分钟
Using SQL for Data Science, Part 15分钟
Using SQL for Data Science, Part 26分钟
Course Summary57
7 个阅读材料
Resources on the Who, What, Why, and How20分钟
Resources on Audience30分钟
Dashboard and Storytelling with Data10分钟
Finding the Story10分钟
Prioritizing, Optimizing and Designing the Data Story10分钟
Tell the Story of Your Data10分钟
Additional SQL Resources to Explore15分钟
关于 Learn SQL Basics for Data Science 专项课程
This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets.
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When will I have access to the lectures and assignments?
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The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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