SQL for Data Science

Go to Course: https://www.coursera.org/learn/sql-for-data-science

Introduction

**Course Review: SQL for Data Science on Coursera** In today’s data-driven world, the ability to effectively manage and analyze data has become an invaluable skill. The Coursera course "SQL for Data Science" stands out as a superb entry-point for anyone looking to harness the power of SQL in their data-related endeavors. This course is thoughtfully designed to equip learners with the necessary skills to thrive in the rapidly growing field of data science. ### Course Overview The course begins with an engaging premise: with the exponential rise in data collection, the demand for skilled data professionals is at an all-time high. SQL (Structured Query Language) is at the heart of this skill set, serving as a critical tool for data scientists. The course eloquently posits that modern data scientists combine mathematical prowess, computer science skills, and the ability to spot trends—traits that are sought after in today’s job market. According to Glassdoor, the role of a data scientist has been dubbed the "best job in America," with competitive salaries averaging around $110,000 and numerous job opportunities available. ### Syllabus Breakdown The curriculum is divided into several well-structured modules, each focusing on specific aspects of SQL that are crucial for data science. 1. **Getting Started and Selecting & Retrieving Data with SQL**: This foundational module introduces learners to the basic concepts of SQL, comparing its functionalities to other computer languages and clarifying the roles of a database administrator versus a data scientist. Beginners will appreciate the clear explanations of database relationships and the syntax of SQL commands, particularly the pivotal SELECT statement. 2. **Filtering, Sorting, and Calculating Data with SQL**: Here, the course dives deeper into SQL with practical applications. Learners will discover how to manipulate and filter data using various clauses and operators—skills that are essential for effective data analysis. Instruction on aggregate functions like AVERAGE and COUNT empowers students to derive meaningful insights from datasets. 3. **Subqueries and Joins in SQL**: Understanding the ability to link data across multiple tables is critical in SQL. This module explains subqueries and different types of JOINs, reinforcing the importance of data relationships. The emphasis on using aliases for clearer code is a particularly useful tip for learners looking to write efficient queries. 4. **Modifying and Analyzing Data with SQL**: As the course progresses, learners will become adept at modifying data through string manipulation and using case statements. This module also touches upon data governance—an increasingly important topic in today’s data landscape. The practical tips shared in applying SQL for data science will add significant value for aspiring data professionals. ### Recommendation I highly recommend "SQL for Data Science" for anyone—whether a complete novice to programming or a professional looking to bolster their data capabilities. The course excels in its structured approach, making complex concepts accessible. It is rich in practical examples, exercises, and assessments, ensuring that learners not only understand the theoretical aspects but also gain hands-on experience. The benefits of completing this course extend beyond just learning SQL—students will foster critical thinking skills necessary for data analysis, empowering them to make insightful business decisions. Given the rising demand for data-savvy professionals, this course can serve as a stepping stone into an exciting and rewarding career in data science. In conclusion, if you are eager to enhance your skills in SQL and data analysis while preparing for a successful career in data science, enrolling in "SQL for Data Science" on Coursera is an excellent choice. Start your journey today and unlock the potential that data holds!

Syllabus

Getting Started and Selecting & Retrieving Data with SQL

In this module, you will be able to define SQL and discuss how SQL differs from other computer languages. You will be able to compare and contrast the roles of a database administrator and a data scientist, and explain the differences between one-to-one, one-to-many, and many-to-many relationships with databases. You will be able to use the SELECT statement and talk about some basic syntax rules. You will be able to add comments in your code and synthesize its importance.

In this module, you will be able to use several more new clauses and operators including WHERE, BETWEEN, IN, OR, NOT, LIKE, ORDER BY, and GROUP BY. You will be able to use the wildcard function to search for more specific or parts of records, including their advantages and disadvantages, and how best to use them. You will be able to discuss how to use basic math operators, as well as aggregate functions like AVERAGE, COUNT, MAX, MIN, and others to begin analyzing our data.

In this module, you will be able to discuss subqueries, including their advantages and disadvantages, and when to use them. You will be able to recall the concept of a key field and discuss how these help us link data together with JOINs. You will be able to identify and define several types of JOINs, including the Cartesian join, an inner join, left and right joins, full outer joins, and a self join. You will be able to use aliases and pre-qualifiers to make your SQL code cleaner and efficient.

In this module, you will be able to discuss how to modify strings by concatenating, trimming, changing the case, and using the substring function. You will be able to discuss the date and time strings specifically. You will be able to use case statements and finish this module by discussing data governance and profiling. You will also be able to apply fundamental principles when using SQL for data science. You'll be able to use tips and tricks to apply SQL in a data science context.