Lesson 1Data validation rules: duplicates, referential integrity (customers/products), out-of-range values, negative prices/quantitiesEstablish strong validation rules to ensure sales CSVs remain reliable. Learners will identify duplicates, maintain referential integrity, and mark out-of-range or negative values to prevent corruption in dashboards and subsequent models.
Detecting duplicate orders and order linesChecking referential integrity keysValidating numeric ranges and thresholdsHandling negative prices and quantitiesBuilding reusable validation checklistsLesson 2Understanding column semantics: order_id, order_date, customer_id, customer_region, product_id, product_category, product_subcategory, quantity, unit_price, discount, revenue, cost, channelClarify the meaning and function of essential sales columns in dashboards. Learners will map identifiers, dates, product details, quantities, and financial fields, guaranteeing uniform semantics across models and visualisations in Namibian contexts.
Order-level identifiers and grain selectionCustomer and region identification fieldsProduct, category, and subcategory rolesQuantity, unit_price, discount, and revenueCost, channel, and margin-related fieldsLesson 3Handling discounts and price calculations: recomputing revenue from unit_price, quantity, and discount and reconciling with reported revenueGrasp methods to recalculate and verify revenue and price metrics. Learners will compute line revenue using unit_price, quantity, and discount, align with reported totals, and identify discrepancies for examination.
Revenue formulas from unit_price and quantityApplying percentage and absolute discountsReconciling computed and reported revenueDetecting inconsistent discount patternsDocumenting pricing and discount logicLesson 4Time-based transformations: extracting year, quarter, month, week, weekday, rolling windows, and fiscal calendarsLearn to convert order dates into detailed time features for analysis. Learners will extract calendar and fiscal attributes, create rolling windows, and prepare uniform time fields for dashboards and time-series models relevant to Namibian fiscal years.
Extracting year, quarter, month, and weekDeriving weekday and weekend indicatorsBuilding rolling and moving window metricsImplementing fiscal calendars and offsetsAligning time grains for dashboardsLesson 5Data cleaning transformations: trimming, case normalization, standardizing region and channel labelsDiscover effective cleaning steps to render raw sales CSVs consistent and ready for analysis. Learners will remove extra spaces, standardise case, and unify region and channel labels to prevent duplicates and faulty dashboard filters in local settings.
Trimming whitespace and invisible charactersCase normalization for text dimensionsStandardizing region and channel taxonomiesMerging near-duplicate label variantsDocumenting cleaning rules for reuseLesson 6Derived metrics and transformations: profit = revenue - cost, profit_margin = profit / revenue, gross_margin, AOV = revenue / order_count, unit_total = quantity * unit_priceAcquire skills to derive vital sales metrics from basic CSV fields. Learners will calculate profit, margins, AOV, and unit totals, ensuring formulas are uniform, properly recorded, and matched to business definitions in Namibian commerce.
Computing profit and gross marginCalculating profit_margin safelyDeriving AOV from revenue and ordersUnit totals from quantity and unit_priceAligning metrics with business definitionsLesson 7Techniques for reproducible ETL: documented steps, scripts, notebooks, and checksums for CSV import integrityUnderstand designing repeatable ETL pipelines for sales CSVs. Learners will script transformations, monitor versions, utilise notebooks for exploration, and employ checksums and validation to ensure import reliability over time in Namibian data workflows.
Scripting repeatable CSV transformationsUsing notebooks for exploratory ETLVersioning ETL code and configurationChecksums and file integrity validationAutomated ETL runs and loggingLesson 8Missing values and null patterns: detection methods, imputation strategies, and when to drop rowsExpertise in detecting and addressing missing or null values in sales CSVs. Learners will analyse null patterns, select imputation methods, determine row dropping, and record assumptions to safeguard downstream metrics.
Profiling missingness across key columnsVisualizing null patterns and correlationsImputation strategies for numeric fieldsImputation strategies for categorical fieldsRules for safely dropping rows or columnsLesson 9Data types and parsing: date formats, numeric types, categorical encoding, handling string vs numeric valuesMaster parsing dates, numbers, and categories in sales CSVs accurately. Learners will differentiate text from numeric fields, use locale-sensitive parsing, and create stable categorical encodings for consistent refreshes in Namibian locales.
Detecting column data types in CSV importsParsing dates with multiple locale formatsHandling numeric separators and currency symbolsDesigning stable categorical encodingsConverting mixed-type columns safelyLesson 10Dealing with multi-line orders and aggregation at order vs order-line levelHandle orders spanning multiple lines in sales CSVs effectively. Learners will differentiate order and order-line levels, aggregate properly, and prevent double counting of revenue, quantity, and discounts in dashboards.
Identifying order vs order-line grainAggregating revenue at order levelSummarizing discounts across linesAvoiding double counting in rollupsChoosing grain for dashboard metrics
