Lesson 1Schema Validation: Required Fields, Data Types, Date Parsing, and Timezone HandlingGrasp methods to establish and apply strong schemas for order data, checking essential fields, data types, and date formats, while managing time zones, delayed data, and schema changes from various Eritrean source systems effectively.
Defining Required Order FieldsValidating Numeric and String TypesParsing Dates and Timestamps SafelyStandardizing Time Zones and OffsetsCatching Schema Drift and EvolutionAutomated Schema Checks in PipelinesLesson 2Documenting Data Lineage and Assumptions for Reproducibility and AuditabilityLearn to record data lineage, business rules, and modeling assumptions for retail order pipelines in Eritrea, 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 to load CSV order files into Excel, SQL databases, Python, R, and BI tools, setting up encodings, delimiters, data types, and project environments for repeatable, scalable workflows in Eritrean retail analytics.
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 BoundariesInvestigate ways to derive time-based features from order timestamps, covering calendar details, fiscal periods, rolling windows, and custom week or month boundaries suited to Eritrean retail trading and reporting practices.
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 partitioning and sampling of large retail order datasets for effective exploration, model building, and testing in Eritrea, maintaining temporal structure, seasonality, and vital business segments for consistent 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, analyze, and address missing values in transactional retail data, selecting suitable imputation or exclusion methods that retain revenue, quantity, and customer behavior signals without skewing analyses in Eritrean contexts.
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 FieldsAcquire skills to identify, assess, and manage outliers in price, quantity, discount, and revenue fields, differentiating errors from true extremes to safeguard model reliability and accurate business reporting in Eritrea.
Profiling Distributions and ExtremesRule-Based Outlier ThresholdsStatistical and Robust Detection MethodsSeparating Errors from Rare EventsCapping, Trimming, and WinsorizingMonitoring Outliers Over TimeLesson 8Standardizing Categorical Fields: Region, Product_Category, Product_Subcategory, Marketing_Channel, Device_TypeLearn to normalize key categorical attributes in retail orders, ensuring regions, product hierarchies, marketing channels, and device types are uniform, analyzable, and prepared for segmentation, attribution, and performance reporting in Eritrean markets.
Designing Canonical Code ListsNormalizing Region and Market LabelsStandardizing Product Category HierarchiesCleaning Marketing_Channel ValuesHarmonizing Device_Type and PlatformHandling Legacy and Deprecated ValuesLesson 9Creating Derived Fields: Gross_Margin, Margin_Rate, Average_Order_Value, Unit_Cost, Order_Value ComponentsExpertly generate essential financial and behavioral metrics from order data, such as gross margin, margin rate, average order value, unit costs, and order value breakdowns, aiding profitability and pricing analysis for Eritrean retailers.
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
