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Learner Reviews & Feedback for SQL for Data Science by University of California, Davis

4.6
stars
10,778 ratings
2,810 reviews

About the Course

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. This course is designed to give you a primer in the fundamentals of SQL and working with data so that you can begin analyzing it for data science purposes. You will begin to ask the right questions and come up with good answers to deliver valuable insights for your organization. This course starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You'll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results. You will create new tables and be able to move data into them. You will learn common operators and how to combine the data. You will use case statements and concepts like data governance and profiling. You will discuss topics on data, and practice using real-world programming assignments. You will interpret the structure, meaning, and relationships in source data and use SQL as a professional to shape your data for targeted analysis purposes. Although we do not have any specific prerequisites or software requirements to take this course, a simple text editor is recommended for the final project. So what are you waiting for? This is your first step in landing a job in the best occupation in the US and soon the world!...

Top reviews

JG
Aug 22, 2021

I thought this course was great! Great introduction to Relational Databases and SQLite. Highly reccomend for anyone new to SQL, Databases, or someone looking to get started with a data science career.

JP
Apr 5, 2020

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.

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2576 - 2600 of 2,856 Reviews for SQL for Data Science

By Aman K

Aug 20, 2020

Its GOOD

By ANAND S S

Sep 6, 2021

superb

By Polyakova T I

May 19, 2021

thanks

By Zachary M

Apr 12, 2020

Solid.

By Daniel Z A

Apr 11, 2020

I like

By alom j

Oct 27, 2020

great

By Shiny J

Jul 31, 2021

Good

By Juan J U R

Jul 12, 2021

good

By ARYAN A S 2

Jun 18, 2021

good

By Kedar J

Apr 15, 2021

good

By Prayag p w

Mar 1, 2021

good

By Bat-Enkh O

Oct 15, 2020

Good

By GANGINENI M

Sep 13, 2020

good

By Shivani P U

Aug 20, 2020

good

By Tenzing S

Aug 2, 2020

Good

By VISHNU T B

Jun 24, 2020

Good

By NAGA P Y

May 25, 2020

good

By Emmanuel H

May 5, 2020

good

By XAVIER C A

Dec 13, 2019

Eas

By 王楚豪

Dec 25, 2020

gd

By abhishek a

Jun 25, 2020

ok

By Mina T

Sep 9, 2021

3

By Nicolas A P

Jun 6, 2020

.

By Cian O M

Mar 1, 2018

V

By Greg S

Dec 16, 2020

The lectures and weekly coursework were fine -- although a bit too easy. The emphasis on certain aspects, such as formatting/commenting, and some comments on joins were on point. Sadie has a soothing voice and good pace, although the content somewhat drags at places. Not her fault as a presenter, the videos were just going too slow imo. There were a few typos in the lecture slides, one of which was quite confusing (I have reported them separately).

Unfortunately what really stood out in a negative way was the peer-graded assignment. This *really* needs to be rewritten. Specific problems:

Part1

=====

Questions 5 to 7 could be answered in two different ways: one, which is the "easier" one and the one that the marking guide forced us to accept, is to read off review_count from the Business table (and descend-order it). The first few results this way are

+-----------------+---------+

| city | reviews |

+-----------------+---------+

| Las Vegas | 82854 |

| Phoenix | 34503 |

| Toronto | 24113 |

| Scottsdale | 20614 |

| Charlotte | 12523 |

| Henderson | 10871 |

+-----------------+---------+

The problem with this is that the data set we're working on is a SUBSET of the full Yelp set. This means that the Business.review_count column (which was presumably added there in a redundant, denormalized way, in order to speed up queries) contains much higher counts than the ones that would be obtained by actually joining the Business and Review tables (on business id) and grouping by city. Here are the first few results of this approach, which imo is the correct one:

+-----------------+---------+

| city | reviews |

+-----------------+---------+

| Las Vegas | 193 |

| Phoenix | 65 |

| Toronto | 51 |

| Scottsdale | 37 |

| Henderson | 30 |

| Tempe | 28 |

+-----------------+---------+

I would be totally ok if the marking guide gave us the choice to accept both methods, but that wasn't the case.

Part 2

======

Q1 of Part2 was badly worded and unclear. It asks us to "pick a city AND a category" but then to group the businesses (by star rating) "in that city OR category". What exactly does that mean? Lump together all businesses from eg Phoenix (regardless of category) with the eg Restaurants businesses (regardless of city)? What's the rationale behind this? Or was it meant to read "in that city AND category" (meaning that we would only consider restaurants from Phoenix)?

Also, Q1.iii is both random and vague: why location? What do you mean by location (zip code? longitude/latitude?). This ties to the above ambiguity (X=AND vs X=OR in "in that city X category").