Lesson 1Design of transactional tables: orders, order_items, returns, lifetime_value signals and field choicesLearn to design main transactional tables that record orders, line items, returns, and lifetime value signs. We discuss key fields, standardisation choices, and how to aid downstream analysis and recommendation tasks for local shops.
Order header vs line item schema designModelling returns, refunds, and cancellationsCapturing discounts, coupons, and taxesStoring lifetime value and margin signalsKeys, indexes, and partitioning choicesLesson 2Handling noisy and sparse behavioural data: sessionization, bot filtering, deduplication, event weightingLook into ways to clean noisy behaviour logs and make thin data useful. You will learn session rules, bot and scraper removal, duplicate removal logic, and event weighting plans suited to recommendation training in Ghanaian platforms.
Sessionization rules and timeoutsDetecting and filtering bots and scrapersClick, view, and purchase deduplicationEvent weighting for model trainingHandling sparse users and cold startsLesson 3Design of product catalogue table: product_id, title, category hierarchy, attributes, price, brand, stock, images, canonical_text, embeddingsLearn to structure a product catalogue table that aids quick retrieval and rich recommendations. We cover identifiers, attributes, pricing, stock, media, standard text, and embeddings, plus update and non-standardisation strategies.
Stable product and variant identifiersCategory hierarchy and attributesPrice, stock, and availability fieldsImages, media, and canonical textStoring and updating item embeddingsLesson 4Feature engineering principles for recommendations: recency, frequency, monetary, item popularity, category affinity, user embeddingsFind core feature engineering rules for recommender systems. We detail recency, frequency, monetary value, popularity, category links, and user embeddings, including grouping windows and leak-safe calculation patterns for local use.
Recency, frequency, and monetary featuresItem and category popularity signalsUser–category and brand affinity scoresSequence-based and session featuresUser and item embedding generationLesson 5Auxiliary datasets: item metadata, taxonomy, promotions, content (descriptions), supplier dataUnderstand how extra datasets enrich recommendations beyond raw clicks and orders. We cover item metadata, taxonomy, promotions, content, and supplier feeds, plus keeping them steady, versioned, and joinable at scale in Ghana.
Designing item metadata schemasMaintaining product taxonomy hierarchiesModelling promotions and price rulesStoring rich content and descriptionsIntegrating supplier and feed dataLesson 6Data cleaning and imputation strategies: missing attributes, price anomalies, invalid timestampsLearn practical data cleaning and filling methods for e-commerce. We handle missing attributes, odd prices, wrong timestamps, and uneven currencies, focusing on rules, shortcuts, and effects on recommendation quality in local settings.
Detecting and fixing missing attributesHandling outlier and zero pricesCorrecting invalid or noisy timestampsCurrency, tax, and unit normalisationDocumenting cleaning rules and impactsLesson 7Design of event stream and interaction table: event_id, user_id/session_id, event_type, product_id, timestamp, context (referrer, page_type), device, event_valueDesign a unified interaction table and event stream that records user actions across channels. Learn event designs, identifiers, context fields, and how to aid both real-time streaming and offline batch recommendation lines.
Choosing event and user identifiersModelling event types and propertiesCapturing context, device, and referrerEvent time, ingestion time, and orderingStreaming vs batch storage patternsLesson 8Design of user profiles table: essential fields (user_id, signup_ts, email_hash, demographics, lifecycle stage, segments, opt-in flags) and rationaleDesign a user profiles table that balances personalization strength with privacy and rules. We cover essential fields, lifecycle and segments, opt-in flags, hashing sensitive data, and how profiles feed recommendation models.
Core identifiers and signup metadataDemographics and lifecycle stagesBehavioural and marketing segmentsConsent, opt-in, and preference flagsPrivacy, hashing, and retention rules
