As data collection has increased exponentially, so has the need for people skilled at using and interacting with data; to be able to think critically, and provide insights to make better decisions and optimize their businesses. This is a data scientist, “part mathematician, part computer scientist, and part trend spotter” (SAS Institute, Inc.). According to Glassdoor, being a data scientist is the best job in America; with a median base salary of $110,000 and thousands of job openings at a time. The skills necessary to be a good data scientist include being able to retrieve and work with data, and to do that you need to be well versed in SQL, the standard language for communicating with database systems.
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.
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來自SQL FOR DATA SCIENCE的熱門評論
This course has really helped with optimizing queries that I work with everyday, enhancing my understanding of RDBMS, joins, analyzing and structuring exactly what you need and yielding those results.
The course is nice but I suppose the PowerPoint presentation that accompanies it could use some effects to show us the clauses one by one, as they are spoke. That would make understanding them easier.
Please Included examples while the instructor is teaching in the video lectures. The instructor is very talented in teaching but due to lack of examples in the video it downgrades the overall course.
A comprehensive course that covers major aspects of query building and retrieval in a management system. The topics were delivered well and the materials/assignments were relevant for skill-building.
關於 Learn SQL Basics for Data Science 專項課程
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.