Lesson 1Schema Validation: Required Fields, Data Types, Date Parsing, and Timezone HandlingGrasp how to establish and apply strong schemas for order-level data, checking essential fields, data types, and date formats while properly managing time zones, delayed data arrivals, and schema changes from various source systems.
Defining essential order-level fieldsChecking numeric and string data typesSafely parsing dates and timestampsStandardising time zones and offsetsDetecting schema changes and evolutionAutomated schema verification in pipelinesLesson 2Documenting Data Lineage and Assumptions for Reproducibility and AuditabilityLearn to record data lineage, business rules, and modelling assumptions for retail order pipelines, promoting reproducibility, governance, and auditability among teams, tools, and changing source systems.
Capturing source-to-target mappingsRecording business transformation rulesTracking metric definitions over timeMaintaining data dictionariesVersioning pipelines and schemasAudit trails for regulatory reviewsLesson 3Loading CSVs into Analytical Tools and Environment Setup (Excel, SQL, Python, R, BI Tools)Acquire hands-on skills for importing CSV order files into Excel, SQL databases, Python, R, and BI tools, setting up encodings, delimiters, data types, and project environments to guarantee repeatable, scalable analytical workflows.
Configuring CSV import optionsManaging encodings and delimitersBulk loading into SQL warehousesPython and R data ingestion scriptsConnecting BI tools to raw tablesVersioning and environment managementLesson 4Temporal Derivations: Extracting Date Parts, Rolling Windows, Fiscal Calendars, Week/Month BoundariesDiscover methods to derive time-based features from order timestamps, covering calendar details, fiscal periods, rolling windows, and custom week or month boundaries that match retail trading patterns and reporting needs.
Extracting standard date partsBuilding fiscal calendars and periodsCustom retail week and month boundariesRolling windows for KPIsLag and lead features for ordersSeasonality and holiday flagsLesson 5Data Partitioning and Sampling for Efficient Exploration and Reproducible AnalysisUnderstand how to partition and sample large retail order datasets for effective exploration, model building, and testing, while keeping temporal structure, seasonality, and vital business segments intact for repeatable analytical experiments.
Partitioning by date and storeTrain, validation, and test splitsStratified sampling by segmentDownsampling and upsampling tacticsCreating reproducible random samplesManaging partitions in data warehousesLesson 6Detecting and Handling Missing Values: Strategies and Imputation Specific to Transactional DataMaster systematic approaches to identify, profile, and address missing values in transactional retail data, selecting suitable imputation or exclusion methods that maintain revenue, quantity, and customer behaviour signals without skewing analyses.
Profiling missingness patternsMCAR, MAR, and MNAR in retail dataImputing prices, discounts, and costsHandling missing customer identifiersDealing with incomplete order linesDocumenting imputation decisionsLesson 7Outlier Detection and Treatment for Price, Quantity, Discount, and Revenue FieldsLearn to identify, diagnose, and manage outliers in price, quantity, discount, and revenue fields, differentiating data errors from true extreme events to safeguard model stability and business reporting precision.
Profiling distributions and extremesRule-based outlier thresholdsStatistical and robust detection methodsSeparating errors from rare eventsCapping, trimming, and winsorizingMonitoring outliers over timeLesson 8Standardising Categorical Fields: Region, Product_Category, Product_Subcategory, Marketing_Channel, Device_TypeDiscover how to standardise important categorical attributes in retail orders, ensuring regions, product hierarchies, marketing channels, and device types are uniform, analysable, and prepared for segmentation, attribution, and performance reporting.
Designing canonical code listsNormalising region and market labelsStandardising product category hierarchiesCleaning marketing_channel valuesHarmonising device_type and platformHandling legacy and deprecated valuesLesson 9Creating Derived Fields: Gross_Margin, Margin_Rate, Average_Order_Value, Unit_Cost, Order_Value ComponentsExpertly create essential financial and behavioural derived metrics from order data, such as gross margin, margin rate, average order value, unit costs, and broken-down order value components to bolster profitability and pricing analysis.
Calculating gross_margin and net_revenueComputing margin_rate and markupsAverage_order_value and basket sizeUnit_cost and unit_price derivationsDecomposing order_value componentsValidating derived metric consistency
