Databases and SQL for Data Science with Python from Coursera

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.

What's inside

Syllabus

Getting Started with SQL

In this module, you will be introduced to databases. You will learn how to use basic SQL statements like SELECT, INSERT, UPDATE and DELETE. You will also get an understanding of how to refine your query results with the WHERE clause as well as using COUNT, LIMIT and DISTINCT.

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what should give you pause

and possible dealbreakers

Examines working knowledge of structured query language (SQL), a key asset for various roles in the Data Science and Data Analysis fields

Develops foundational skills in SQL, including data retrieval through queries

Covers advanced SQL concepts and techniques, such as joins and subqueries

Provides practice with real-world data sets and industry-standard tools

Features instructors with expertise in database management and data analysis

Reviews summary

Comprehensive sql and python for data science

According to learners, this course offers a solid introduction to databases and SQL, particularly praising the clear explanations of fundamental SQL concepts. Many found the hands-on labs and practical final project to be highly beneficial for reinforcing learning. While the course is generally well-structured and a good starting point, some students felt the section on accessing databases with Python was a bit rushed or could benefit from more in-depth coverage, and the advanced SQL module requires significant effort.

Serves as a strong beginner's course.

"This course is an excellent starting point for anyone new to SQL and data science."

"Perfect for getting your feet wet with databases and how they're used in data analysis."

"I felt it prepared me well for subsequent courses in the data science specialization."

"A solid foundation building block for a data science career."

Final project applies skills practically.

"The final project was a great way to apply everything I learned to a real-world scenario."

"Analyzing multiple datasets in the final assignment felt very practical and useful."

"I liked that the project uses data from Chicago - it felt authentic and relevant."

"Completing the project gave me confidence in my ability to use SQL and Python together."

Labs are useful for practicing skills.

"The labs are incredibly helpful for practicing SQL commands and Python integration."

"I really appreciate the hands-on approach in the labs. It makes a big difference."

"Working with real databases in the labs solidified my understanding more than just watching videos."

"The lab environment was easy to access and use, allowing focused practice."

Explains basic SQL concepts well.

"The course provides a really clear and well-explained foundation for SQL."

"I found the explanations for SELECT, WHERE, and JOINs very easy to follow."

"Great for understanding the basics of databases and running queries. The foundations were strong."

"It simplifies SQL concepts for someone starting out in data science."

Advanced SQL module is demanding.

"The advanced SQL module was quite challenging and felt less integrated than the main content."

"I found the Honors module on views, transactions, and stored procedures difficult without more context."

"This section seems geared towards someone with prior experience; it was a steep learning curve."

"Would benefit from more explanation or prerequisites for the advanced topics."

Python integration section feels quick.

"The module on accessing databases with Python felt a bit rushed compared to the SQL sections."

"I think the Python part could be expanded or explained more thoroughly for beginners."

"While the SQL content was clear, I struggled a bit when it came to integrating it with Python."

"Could use more examples or detailed explanations in the Python module."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Databases and SQL for Data Science with Python with these activities:

Review SQL Basics

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Review the fundamentals of SQL, including data types, operators, and basic queries, to strengthen your foundation before starting the course.

Browse courses on SQL

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Read through tutorials or documentation on SQL basics.
Practice writing simple SQL queries using an online SQL editor or a SQLite database.

Join a SQL Study Group

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Connect with other students or professionals in a study group to discuss SQL concepts, share knowledge, and solve problems collaboratively.

Browse courses on SQL

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Reach out to classmates or colleagues to form a study group.
Set regular meeting times and decide on topics to cover.
Take turns presenting concepts, solving problems, and providing feedback.

Follow SQL Tutorial Series

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Enroll in an online SQL tutorial series to gain a structured and comprehensive understanding of SQL concepts and techniques.

Browse courses on SQL

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Find a reputable SQL tutorial series on platforms like Coursera, edX, or Udemy.
Follow the tutorials step-by-step, completing the exercises and assignments.
Take notes and refer back to the tutorials as needed.

Four other activities

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Read "SQL Cookbook" by Anthony Molinaro

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Refer to "SQL Cookbook" to enhance your SQL knowledge by exploring practical recipes and solutions for common database tasks.

View SQL Cookbook: Query Solutions and Techniques... on Amazon

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Obtain a copy of the book.
Read through the chapters that align with the course topics.
Apply the techniques and solutions presented in the book to your own SQL projects.

Solve SQL Practice Problems

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Engage in regular practice by solving SQL problems on platforms like LeetCode, HackerRank, or SQLZoo to reinforce your understanding and improve your problem-solving skills.

Browse courses on SQL

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Sign up for a coding challenge platform that offers SQL problems.
Start with easier problems and gradually work your way up to more challenging ones.
Analyze the solutions provided by the platform or community to enhance your learning.

Build a Mini SQL Project

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Apply your SQL skills to create a small-scale project, such as a data analysis dashboard or a simple web application that utilizes a database, to solidify your understanding and gain practical experience.

Browse courses on SQL

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Identify a small project idea that aligns with your interests and the course topics.
Design the database schema and create the necessary tables.
Write SQL queries to populate and manipulate the data.
Develop a user interface or visualization to present the results.

Contribute to an Open-Source SQL Project

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Engage in open-source SQL projects on platforms like GitHub to gain hands-on experience, contribute to the community, and deepen your understanding of SQL best practices.

Browse courses on SQL

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Identify open-source SQL projects that align with your interests and skill level.
Review the project documentation and codebase.
Make contributions to the project by fixing bugs, implementing new features, or improving documentation.
Collaborate with other contributors and seek feedback on your work.

Career center

Learners who complete Databases and SQL for Data Science with Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist uses programming and statistical skills to help organizations with data analysis and problem-solving. This course may be useful to this role by helping to build a foundation in Structured Query Language (SQL), a critical skill for data scientists.

See salaries and explore the career path for Data Scientist

Database Administrator

A Database Administrator manages and maintains a database system. This course may be useful to this role by providing a foundational understanding of Structured Query Language (SQL), which is used to manage and maintain databases.

See salaries and explore the career path for Database Administrator

Database Developer

A Database Developer designs, develops, and maintains databases. This course may be useful to this role by providing a foundation in Structured Query Language (SQL), a major component of database development.

See salaries and explore the career path for Database Developer

Data Architect

A Data Architect plans and designs the data infrastructure for an organization. This course may be useful to this role by providing a foundation in Structured Query Language (SQL), a key technology for data architects.

See salaries and explore the career path for Data Architect

Data Engineer

A Data Engineer designs and develops systems for managing large amounts of data. This course may be useful to this role by providing a foundation in Structured Query Language (SQL), a critical technology for data engineers.

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Analytics Manager

An Analytics Manager develops and implements analytics strategies and solutions. This course may be useful to this role by providing a foundation in Structured Query Language (SQL), a key technology for data analysis and management.

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BI Analyst

A BI Analyst analyzes data to help businesses make better decisions. This course may be useful to this role by providing a foundational understanding of Structured Query Language (SQL), a tool that is widely used for data analysis.

See salaries and explore the career path for BI Analyst

Data Mining Analyst

A Data Mining Analyst identifies patterns and trends in data to help businesses make more informed decisions. This course may be useful to this role by providing a foundation in Structured Query Language (SQL), a skill that is commonly used in data mining.

See salaries and explore the career path for Data Mining Analyst

Data Analyst

A Data Analyst collects, processes, and analyzes data primarily to support business decisions. This course may be useful to this role by helping to build a strong foundation in Structured Query Language (SQL), a programming language that is widely used in this field.

See salaries and explore the career path for Data Analyst

Software Engineer

A Software Engineer designs and develops computer systems and applications. This course may be useful to this role by providing a foundation in Structured Query Language (SQL), which is used for data retrieval and manipulation, two critical tasks in software development.

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Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. This course may be useful to this role by providing a foundation in Structured Query Language (SQL), which can be used for querying and manipulating large datasets.

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Business Analyst

A Business Analyst develops solutions using IT systems and processes. This course may be useful to this role by providing a technical foundation in Structured Query Language (SQL), which is essential for extracting meaningful information from large datasets.

See salaries and explore the career path for Business Analyst

Statistician

A Statistician collects, analyzes, interprets, and presents data. This course may be useful to this role by providing a foundational understanding of Structured Query Language (SQL), a programming language that is commonly used for data analysis.

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Full-Stack Developer

A Full-Stack Developer designs and develops both the front-end and back-end of websites and applications. This course may be useful to this role by providing a foundational understanding of Structured Query Language (SQL), a technology that is used for data retrieval and manipulation.

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Database Engineer

A Database Engineer works with the DBMS and the admins to design, implement, and maintain a quality database. This course may be useful to this role by helping to build a foundational understanding of SQL and its appropriate use cases.

See salaries and explore the career path for Database Engineer