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Databases and SQL for Data Science with Python

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.

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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.

Introduction to Relational Databases and Tables

In this module, you’ll learn more about relational database concepts and their importance. This module helps you to understand the process of creating a table in your database on MySQL using the graphical interface and SQL scripts. Further, you will also learn how to alter the entries or delete the entries for any table in the database, or even delete the table itself.

Intermediate SQL

This module helps you 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.

Accessing Databases using Python

In this module you will learn the basic concepts of using Python to connect to databases. In a Jupyter Notebook, you will create tables, load data, query data using SQL magic and SQLite python library. You will also learn how to analyze data using Python.

Course Assignment

In this module, you will be working with multiple real-world datasets for the city of Chicago. 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.

Bonus Module: Advanced SQL for Data Engineer (Honors)

This module covers some advanced SQL techniques that will be useful for Data Engineers. In this module, you will learn how to build more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. If you are following the Data Engineering track, you must complete this module. Completion of this module is not required for those completing the Data Science or Data Analyst tracks.

Good to know

Know what's good

, what to watch for

, 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

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Reviews summary

Databases and sql for data science with python

Learners say "Databases and SQL for Data Science with Python" teaches the basics of SQL and Python database operations clearly. They note the wide range of labs and exercises available to practice these skills, which many describe as helpful. Students also mention the optional "Honors" modules, which provide further learning opportunities. However, some learners report technical difficulties while using IBM's Cloud platform and database, which they say detracted from their learning experience and caused them to lose time. Others mention frustrations with the final peer-graded assignments and a sense that the course is structured to promote IBM products and services rather than solely focus on teaching data science concepts. Overall, learners say "Databases and SQL for Data Science with Python" is a good option for beginners who want to learn the fundamentals of databases and SQL. However, they recommend being aware of potential technical challenges and encourage students to supplement their learning with external resources.

"Databases and SQL for Data Science with Python" is a good option for beginners who want to learn the fundamentals of databases and SQL. The course provides a clear and concise introduction to these topics, and the labs and exercises provide ample opportunities to practice what you learn. However, it is important to be aware of the potential technical challenges that you may encounter when using IBM's Cloud platform and database.

""Databases and SQL for Data Science with Python" is a good option for beginners who want to learn the fundamentals of databases and SQL."

The hands-on labs and assignments are highly lauded by learners, who say they are helpful, well-organized, and essential for practicing and reinforcing the concepts taught in the video lectures and readings. Students especially appreciate the variety of labs available, which include IBM Cloud labs, Jupyter notebooks, and peer-graded assignments.

"The wide range of labs and exercises available to practice these skills were very helpful."

"Learners commented that the students especially appreciate the variety of labs available, which include IBM Cloud labs, Jupyter notebooks, and peer-graded assignments"

Some learners felt that the course was structured in a way that promoted IBM products and services rather than solely focusing on teaching data science concepts. They pointed to the frequent mentions of IBM Cloud and IBM Watson Studio and the requirement to use these platforms for many of the labs and assignments.

"Some learners felt that the course was structured in a way that promoted IBM products and services rather than solely focusing on teaching data science concepts."

"They pointed to the frequent mentions of IBM Cloud and IBM Watson Studio and the requirement to use these platforms for many of the labs and assignments."

The final peer-graded assignment was a source of frustration for some learners, who said that the instructions were unclear and the grading was unfair. Some learners also reported that they received low grades even though their answers were correct. This led to a sense of dissatisfaction and a feeling that the assignment was not a fair assessment of their learning.

"The final peer-graded assignment was a source of frustration for some learners, who said that the instructions were unclear and the grading was unfair."

"Some learners also reported that they received low grades even though their answers were correct."

"This led to a sense of dissatisfaction and a feeling that the assignment was not a fair assessment of their learning."

Many learners experienced difficulties accessing and using IBM's Cloud platform and database, which they say caused them to lose time and become frustrated. These issues included problems setting up credentials, connecting to databases, and running queries. Some learners were only able to complete assignments by using alternative methods, such as connecting to databases from their own computers or using SQLite instead of IBM's Db2 database.

"Many learners experienced difficulties accessing and using IBM's Cloud platform and database, which they say caused them to lose time and become frustrated."

"These issues included problems setting up credentials, connecting to databases, and running queries."

"Some learners were only able to complete assignments by using alternative methods, such as connecting to databases from their own computers or using SQLite instead of IBM's Db2 database."

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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Show all seven activities

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

Show steps

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

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.

See salaries and explore the career path for Analytics Manager

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 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

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 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.

See salaries and explore the career path for Data Engineer

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

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.

See salaries and explore the career path for Software Engineer

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

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Full-Stack Developer

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

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Databases and SQL for Data Science with Python.

SQL Pocket Guide

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Comprehensive reference for SQL programmers. It covers all the major concepts of SQL, and it valuable resource for both beginners and experienced users.

Databases and SQL for Data Science with Python

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Databases and sql for data science with python

Activities

Career center

Reading list

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