faizns/udemy-advanced-mysql-data-analysis

Hands-on : Udemy course, Advanced SQL: MySQL Data Analytics & Business Intelligence, Maven Analytics. Requirements - MySQL Workbench

Udemy - Advanced SQL: MySQL Data Analysis and Business Intelligence

📂 Introduction

The Situation

You’ve just been hired as an eCommerce Database Analyst for Maven Fuzzy Factory, an online retailer which has just launched their first product.

The Brief

As a member of the startup team, you will work with the CEO, the Head of Marketing, and the Website Manager to help steer the business.

You will analyze and optimize marketing channels, measure and test website conversion performance, and use data to understand the impact of new product launches.

The Objectives

Use SQL to:

• Access and explore the Maven Fuzzy Factory database
• Become the data expert for the company, and the go-to person for mission critical analyses
• Analyze and optimize the business’ marketing channels, website, and product portfolio
  

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📂 Overview Database

We will be working with six related tables, which contain eCommerce data about:

• Website Activity
• Products
• Orders and Refunds

We'll use MySQL to understand how customers access and interact with the site, analyze landing page performance and conversion, and explore product-level sales.

Entity Relationship Database

📂 Outline

No	Outline	Description
1	Traffic Analysis & Optimization	Analyze where our website traffic is coming from, how different sources perform in terms of traffic volume and conversion rates, and how we can adjust bids to optimize our budgets.
2	Website Measurement & Testing	Dive into page-level website data to compare traffic and conversion rates, and use MySQL to build and analyze conversion funnels to help optimize the customer purchase experience.
3	MID-COURSE PROJECT	Preparing data for the executive board meeting.
4	Channel Analysis & Optimization	Dig deeper into our traffic channel mix, explore paid vs. free traffic, break down performance by device type, and write advanced SQL queries to conduct some time-series analyses to understand trending and seasonality.
5	Product-Level Analysis	Break down product-level sales and conversion rates, analyze cross-selling patterns, and use refund rates to keep a pulse on quality.
6	User-Level Analysis	Take a closer look at user behavior and repeat sessions, and use MySQL techniques to identify our most valuable customers and explore which channels they are coming from.
7	FINAL PROJECT	Building a data-driven growth story for potential investors.