Lesson 1Schema Validation: Required Fields, Data Types, Date Parsing, an' Timezone HandlingUnderstand how fi define an' enforce robust schemas fi order-level data, validating required fields, data types, an' date formats while correctly handling time zones, late-arriving data, an' schema evolution across multiple source systems, seen.
Defining required order-level fieldsValidating numeric an' string data typesParsing dates an' timestamps safelyStandardizing time zones an' offsetsCatching schema drift an' evolutionAutomated schema checks in pipelinesLesson 2Documenting Data Lineage an' Assumptions fi Reproducibility an' AuditabilityUnderstand how fi document data lineage, business rules, an' modeling assumptions fi retail order pipelines, enabling reproducibility, governance, an' auditability across teams, tools, an' evolving source systems, yuh zeet.
Capturing source-to-target mappingsRecording business transformation rulesTracking metric definitions over timeMaintaining data dictionariesVersioning pipelines an' schemasAudit trails fi regulatory reviewsLesson 3Loading CSVs into Analytical Tools an' Environment Setup (Excel, SQL, Python, R, BI Tools)Gain practical skills fi loading CSV order files into Excel, SQL databases, Python, R, an' BI tools, configuring encodings, delimiters, data types, an' project environments to ensure reproducible, scalable analytical workflows, mi bredda.
Configuring CSV import optionsManaging encodings an' delimitersBulk loading into SQL warehousesPython an' R data ingestion scriptsConnecting BI tools to raw tablesVersioning an' environment managementLesson 4Temporal Derivations: Extracting Date Parts, Rolling Windows, Fiscal Calendars, Week/Month BoundariesExplore techniques fi derive temporal features from order timestamps, including calendar attributes, fiscal periods, rolling windows, an' custom week or month boundaries dat align wid retail trading patterns an' reporting requirements, yuh know.
Extracting standard date partsBuilding fiscal calendars an' periodsCustom retail week an' month boundariesRolling windows fi KPIsLag an' lead features fi ordersSeasonality an' holiday flagsLesson 5Data Partitioning an' Sampling fi Efficient Exploration an' Reproducible AnalysisLearn how fi partition an' sample large retail order datasets fi efficient exploration, model development, an' testing, while preserving temporal structure, seasonality, an' key business segments fi reproducible analytical experiments, seen.
Partitioning by date an' storeTrain, validation, an' test splitsStratified sampling by segmentDownsampling an' upsampling tacticsCreating reproducible random samplesManaging partitions in data warehousesLesson 6Detecting an' Handling Missing Values: Strategies an' Imputation Specific to Transactional DataLearn systematic methods fi detect, profile, an' treat missing values in transactional retail data, choosing appropriate imputation or exclusion strategies dat preserve revenue, quantity, an' customer behavior signals without biasing analyses, yuh zeet.
Profiling missingness patternsMCAR, MAR, an' MNAR in retail dataImputing prices, discounts, an' costsHandling missing customer identifiersDealing wid incomplete order linesDocumenting imputation decisionsLesson 7Outlier Detection an' Treatment fi Price, Quantity, Discount, an' Revenue FieldsLearn fi detect, diagnose, an' treat outliers in price, quantity, discount, an' revenue fields, distinguishing data errors from genuine extreme behavior to protect model stability an' business reporting accuracy, mi bredda.
Profiling distributions an' extremesRule-based outlier thresholdsStatistical an' robust detection methodsSeparating errors from rare eventsCapping, trimming, an' winsorizingMonitoring outliers over timeLesson 8Standardizing Categorical Fields: Region, Product_Category, Product_Subcategory, Marketing_Channel, Device_TypeLearn how fi standardize key categorical attributes in retail orders so regions, product hierarchies, marketing channels, an' device types are consistent, analyzable, an' ready fi segmentation, attribution, an' performance reporting, yuh know.
Designing canonical code listsNormalizing region an' market labelsStandardizing product category hierarchiesCleaning marketing_channel valuesHarmonizing device_type an' platformHandling legacy an' deprecated valuesLesson 9Creating Derived Fields: Gross_Margin, Margin_Rate, Average_Order_Value, Unit_Cost, Order_Value ComponentsMaster di creation a core financial an' behavioral derived metrics from order data, including gross margin, margin rate, average order value, unit costs, an' decomposed order value components dat support profitability an' pricing analysis, seen.
Calculating gross_margin an' net_revenueComputing margin_rate an' markupsAverage_order_value an' basket sizeUnit_cost an' unit_price derivationsDecomposing order_value componentsValidating derived metric consistency
