Data Analysis Using SQL (Coursera)

Offered by Fractal Analytics,
Data Analysis Using SQL (Coursera)

In this comprehensive course, you will embark on a transformative journey to master the art of analyzing data through SQL. SQL is a powerful tool used for managing and manipulating data in relational databases. Throughout this course, you will acquire the essential skills to efficiently extract relevant data from databases, empowering you to navigate through vast amounts of information with ease.

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With a focus on practical application, you will delve into the world of data analysis, discovering how to derive meaningful insights from large datasets housed within complex relational databases. By the end of this course, you will
Gain skills to create and modify databases, equipping you with the ability to solve real-world business problems.
Learn to filter and clean datasets, ensuring the accuracy and reliability of your analysis.
Whether you aspire to be a data analyst, a business intelligence professional, or a decision-maker relying on data-driven insights, this course will provide you with the necessary tools and knowledge to succeed.
This course is part of the Fractal Data Science Professional Certificate.

What you'll learn

  • Extract relevant data from the database in a time efficient manner
  • Build powerful SQL queries to derive insights
  • Analyse and manage large datasets, and derive inferences from complex relational database
  • Enable students to create and modify databases for relevant business problems

Syllabus

Introduction to Data Analysis using SQL
This module aims to introduce the key aspects and applications of SQL. The first module will cover the limitations of excel (via the story of a data scientist) as well as define the different types of databases. Post installation the learners will go through a pseudo code exercise wherein the learner will also learn elementary SQL queries to retrieve data, using keywords such as SELECT, FROM, COUNT, DISTINCT etc.

Data Analysis with SQL on a Single Table
This module is designed to empower users with comprehensive skills in analysing data within a single table using SQL queries. By delving into various SQL querying techniques, students will gain proficiency in efficiently retrieving, manipulating, and aggregating data. The students will get acquainted with keywords such as WHERE, LIMIT, AVG, MAX, MIN etc. The module goes beyond theoretical concepts by providing practical exercises and a case study, allowing users to apply what they've learnt in a real-world scenario.

Advanced functions on Single Tables in SQL
This module will enhance a learner's proficiency in utilising advanced SQL functions for comprehensive data analysis. Throughout this module, students will delve into a diverse range of robust SQL functions, including string functions such as SUBSTRING, CONCAT, date/time functions like DATEADD and DATEDIFF, case operators, and branching logic. The module will also incorporate practical exercises and an engaging case study, enabling students to further strengthen their problem-solving skills and gain hands-on experience in applying these functions in real-life scenarios.

Analyzing Multiple Tables in SQL
In this module the learner will gain valuable skills to navigate complex data structures, harness the power of JOIN operations, grasp the intricacies of subqueries, and comprehend the significance of different types of keys. Additionally, they will be introduced to the fundamental concept of normalisation, which plays a vital role in database design. By the conclusion of this module, students will possess the expertise to make informed decisions by extracting insights from joined datasets.

CRUD Operations in SQL
In this module, learners will dive deeper into various table operations, expanding their knowledge beyond just querying. They will learn essential skills, including creating tables, modifying table structures, adding and deleting columns, and implementing table constraints. These essential concepts and techniques will empower learners to handle data manipulation tasks, maintain robust databases, and optimise data organisation.

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