Best SQL Courses on Coursera

Introduction to Structured Query Language (SQL) – University of Michigan

In this course, you’ll walk through installation steps for installing a text editor, installing MAMP or XAMPP (or equivalent) and creating a MySql Database. You’ll learn about single table queries and the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. Lastly, you’ll learn to model many-to-many relationships like those needed to represent users, roles, and courses.

first technical task is to work through the installation steps including installing a text editor, installing MAMP or XAMPP (or equivalent), creating a MySql Database. You will  learn about single table queries and the basic syntax of the SQL language. Covering database design with multiple tables, foreign keys, and the JOIN operation..

• Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation.
• Learn to model many-to-many relationships like those needed to represent users, roles, and courses.

Skills you will gain from this course:

• Phpmyadmin
• Relational Database
• SQL
• MySQL

Rating: 4.8 | Level: Intermediate | Course Duration: 15hrs | Assignments: 2

Learn SQL Basics for Data Science Specialization – UC Davis

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, Delta Lake 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.

What you will learn from this course:

• Use SQL commands to filter, sort, & summarize data; manipulate strings, dates, & numerical data from different sources for analysis
• Use the collaborative Databricks workspace and create an end-to-end pipeline that reads data, transforms it, and saves the result
• Assess and create datasets to solve your business questions and problems using SQL
• Develop a project proposal & select your data, perform statistical analysis & develop metrics, and present your findings & make recommendations
• Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results.
• Use SQL commands to filter, sort, and summarize data.
• Create an analysis table from multiple queries using the UNION operator.
• Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis.
• Validate and clean a dataset
• Assess and create datasets to answer your questions
• Solve problems using SQL
• Build a simple testing framework to touch on AB Testing
• 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

Rating:4.5 | Level: Beginners | Course Duration: 2months | Provider: UC Davis

SQL for Data Science – UC Davis

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 you 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!

First Module: Getting Started and Selecting and 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.

Second Module: Filtering, Sorting, and Calculating Data with SQL

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.

Third Module: Subqueries and Joins in SQL

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.

Fourth Module: Modifying and Analyzing Data with SQL

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.

• Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results.
• Use SQL commands to filter, sort, and summarize data.
• Create an analysis table from multiple queries using the UNION operator.
• Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis.

Rating: 4.6 | Level: Beginner | Course Duration: 15 hrs | Assignments:14 | Provider: UC Davis

Databases and SQL for Data Science with Python – IBM

Working knowledge of SQL (or Structured Query Language) is a must for data professionals like Data Scientists, Data Analysts and Data Engineers. Much of the world’s data resides in databases. SQL is a powerful language used for communicating with and extracting data from databases.

In this course you will learn SQL inside out- from the very basics of Select statements to advanced concepts like JOINs. You will:

• write foundational SQL statements like: SELECT, INSERT, UPDATE, and DELETE
• filter result sets, use WHERE, COUNT, DISTINCT, and LIMIT clauses -differentiate between DML & DDL
• CREATE, ALTER, DROP and load tables
• use string patterns and ranges; ORDER and GROUP result sets, and built-in database functions
• build sub-queries and query data from multiple tables
• access databases as a data scientist using Jupyter notebooks with SQL and Python
• work with advanced concepts like Stored Procedures, Views, ACID Transactions, Inner & Outer JOINs through hands-on labs and projects
• You will practice building SQL queries, work with real databases on the Cloud, and use real data science tools.
• In the final project you’ll analyze multiple real-world datasets to demonstrate your skills.

• Analyze data within a database using SQL and Python.
• Create a relational database and work with multiple tables using DDL commands.
• Construct basic to intermediate level SQL queries using DML commands.
• Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

Rating: 4.7 | Level: Beginners | Course Duration: 20hrs | Provider: IBM | Assignments: 17

SQL for Data Science with R – IBM

Much of the world’s data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist.

The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL and R languages. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and R. No prior knowledge of databases, SQL, R, or programming is required. Anyone can audit this course at no charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.

• Create and access a database instance on the cloud
• Compose and execute basic SQL statements – SELECT, INSERT, UPDATE, DELETE, CREATE, DROP
• Construct SQL statements to filter, sort, group results, use built-in functions, compose nested queries, access multiple tables
• Analyze data from Jupyter using R and SQL by combining SQL and R skills to query real-world datasets

Structured Query Language, or SQL, provides a standard language for selecting and manipulating data in a relational database. Understanding SQL is a foundational skill that you must have when applying data science principles in R because SQL is the key to helping you unlock insights about the information stored deep inside relational databases. In this module, you will learn some basic SQL statements and practice them hands-on on a live database.

you will explore the fundamental concepts behind databases, tables, and the relationships between them. You will then create an instance of a database, discover SQL statements that allow you to create and manipulate tables, and then practice them on your own live database.

you will learn how to use string patterns and ranges to search data and how to sort and group data in result sets. You will also practice composing nested queries and execute select statements to access data from multiple tables.

you will learn the benefits of using R to connect to relational databases and how to persist R database objects in files. You’ll also learn some of the similarities between R data frames and relational databases, including how data types compare and when you must convert from one type to another to improve the effectiveness of your data analysis. Finally, you’ll learn different methods for connecting to a database from R.

you will learn the full process of accessing and querying databases using R. You’ll learn how to create the logical and physical model of the database and then implement the model by creating the physical database objects and loading them with data. Finally, you’ll examine an example of accessing and querying the database.

you will be working with multiple real-world datasets for the Canadian Crop Data and Exchange Rates. You will be asked questions that will help you understand the data just as you would in the real world. You will be assessed on the correctness of your SQL queries and results.

Rating:4.3 | Level: Beginner | Course Duration: 27hrs | Assignments: 15