Machine learning and artificial intelligence thrive on large datasets, but when I began developing this course, I faced the challenge of sourcing such data. While using real client websites might have been an option, businesses are understandably hesitant to share their data and platforms with outsiders. To overcome this, I created simulated companies. These businesses don’t exist in the real world. Still, their websites, databases, and connected platforms are filled with the data we need to learn and apply AI and machine learning techniques effectively.
As a marketer, you already understand that data is the lifeblood of effective marketing strategies. As a data scientist, you know that data is one of the most valuable assets a business can have today. But you might be wondering: where does all this data come from if these businesses aren’t real? To solve this, I ran a series of JavaScript and Python scripts to generate realistic dummy data, ensuring that our simulated e-commerce platforms are stocked with orders, customer information, and clickstream data—tracking user interactions with our platforms. I’ve also set up model WordPress websites connected to Google Analytics (GA) and social media channels. These websites include fully functional storefronts powered by WooCommerce, providing a rich dataset for exploring machine learning and AI strategies without starting from scratch. This environment closely mirrors real-world scenarios, giving you practical, hands-on experience that will be directly applicable to your career.
Data Collection
We gather data from various sources:
- HubSpot: Offers CRM data, email marketing metrics, and customer information.
- Google Analytics: Provides insights into website traffic, user behavior, and conversion rates.
- Social Media: Supplies engagement metrics and audience demographics.
- WordPress: Provides user data and is the base for all our platforms.
- WooCommerce: Captures e-commerce data, including product performance and sales information.
Data Processing with AI
The collected data will be exported, prepared, and processed on AI platforms. First, the data must be connected to a storage place to be processed on AI platforms.
Generating Outcomes
The insights derived from the AI platform are used to create recommendation engines, customer segmentation, chatbots and virtual assistants, predictive analytics, and personalized marketing strategies.