Databases and SQL for Data Science with Python

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

Introduction

**Course Review: Databases and SQL for Data Science with Python** In today's data-driven world, having a solid understanding of databases and SQL is essential for any aspiring data professional. “Databases and SQL for Data Science with Python,” offered on Coursera, is an excellent course that equips learners with crucial SQL skills needed for data science, analytics, and engineering. This course caters to various experience levels, from absolute beginners to intermediate users looking to strengthen their knowledge. ### **Course Overview** The course dives deep into SQL and its applications in data science using Python, making it a well-rounded program. With databases housing immense amounts of information, mastering Structured Query Language (SQL) is imperative. This course lays the groundwork for querying databases effectively, covering all essential SQL concepts from the basics to advanced techniques. ### **Course Syllabus Breakdown** #### **1. Getting Started with SQL** This introductory module is perfect for newcomers. It covers foundational SQL statements like SELECT, INSERT, UPDATE, and DELETE. Students learn to refine query results using the WHERE clause and familiarize themselves with functions like COUNT, LIMIT, and DISTINCT. This module sets the stage for the rest of the course, ensuring everyone starts on a level playing field. #### **2. Introduction to Relational Databases and Tables** Building on the basics, this section delves into the structure of relational databases. Learners will understand the significance of creating tables in MySQL through both a graphical interface and SQL scripts. Practical skills in altering and deleting entries, as well as tables, are vital for everyday database management. #### **3. Intermediate SQL** Here, the course progresses into more sophisticated SQL techniques. Students learn how to search data using string patterns, sort, and group results. The introduction of nested queries empowers learners to engage with multiple tables, building their confidence in advanced SQL querying. #### **4. Accessing Databases using Python** One of the highlights of this course is its integration of Python with SQL. It emphasizes how to connect to databases and run SQL queries within a Jupyter Notebook. The course offers hands-on experience in creating tables, loading data, and leveraging the SQLite library, vital for analyzing data using Python effectively. #### **5. Course Assignment** Application of knowledge is where this course truly shines. Students will engage with real-world datasets, specifically from Chicago, and apply their SQL skills to answer practical questions. This module not only solidifies learning but also prepares learners for challenges they will face in real-world scenarios. #### **6. Bonus Module: Advanced SQL for Data Engineer (Honors)** For learners interested in data engineering, this elective module introduces advanced SQL techniques such as transactions, stored procedures, and views. While not mandatory for data science or analysis tracks, it is incredibly beneficial for those wishing to take their skills further. ### **Strengths of the Course** - **Comprehensive Content:** The syllabus provides a thorough understanding of SQL, moving from foundational skills to advanced querying. - **Hands-On Practice:** Real-world datasets and practical assignments reflect typical database challenges faced in the field. - **Integration with Python:** Given the importance of Python in data science, learning how to use it with SQL is a significant advantage. - **Flexible Learning:** Available on Coursera, this course allows you to learn at your own pace, with access to resources whenever required. ### **Recommendations** I highly recommend “Databases and SQL for Data Science with Python” for anyone interested in pursuing a career in data science, analytics, or engineering. Whether you are a beginner or looking to sharpen your SQL skills, this course offers valuable knowledge applicable in the workplace. Completing this course will not only enhance your skill set but also significantly improve your employability in a data-centric job market. Feel free to enroll if you’re ready to dive into the world of databases and SQL! Happy learning!

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.

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.

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.

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.

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.

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.