Lesson 1Data validation rules: duplicates, referential integrity (customers/products), out-of-range values, negative prices/quantitiesDefine robust validation rules fi keep sales CSVs trustworthy. Yuh wi detect duplicates, enforce referential integrity, an flag out-of-range or negative values before dem corrupt dashboards an downstream models.
Detectin duplicate orders an order linesCheckin referential integrity keysValidatin numeric ranges an thresholdsHandlin negative prices an quantitiesBuildin 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 di meanin an role a core sales columns used in dashboards. Yuh wi map identifiers, dates, product attributes, quantities, an monetary fields, ensurin consistent semantics across models an visualizations.
Order-level identifiers an grain selectionCustomer an region identification fieldsProduct, category, an subcategory rolesQuantity, unit_price, discount, an revenueCost, channel, an margin-related fieldsLesson 3Handling discounts and price calculations: recomputing revenue from unit_price, quantity, and discount and reconciling with reported revenueUnderstand how fi recompute an validate revenue an price metrics. Yuh wi calculate line revenue from unit_price, quantity, an discount, reconcile wid reported totals, an flag inconsistencies fi review.
Revenue formulas from unit_price an quantityApplyin percentage an absolute discountsReconciliin computed an reported revenueDetectin inconsistent discount patternsDocumentin pricin an discount logicLesson 4Time-based transformations: extracting year, quarter, month, week, weekday, rolling windows, and fiscal calendarsUnderstand how fi transform order dates into rich time features fi analysis. Yuh wi derive calendar an fiscal attributes, build rollin windows, an prepare consistent time fields fi dashboards an time-series models.
Extractin year, quarter, month, an weekDerivin weekday an weekend indicatorsBuildin rollin an movin window metricsImplementin fiscal calendars an offsetsAlignin time grains fi dashboardsLesson 5Data cleaning transformations: trimming, case normalization, standardizing region and channel labelsExplore practical cleanin steps fi make raw sales CSVs consistent an analysis-ready. Yuh wi trim whitespace, normalize case, an standardize region an channel labels fi avoid duplicates an broken dashboard filters.
Trimm in whitespace an invisible charactersCase normalization fi text dimensionsStandardizin region an channel taxonomiesMergin near-duplicate label variantsDocumentin cleanin rules fi reuseLesson 6Derived metrics and transformations: profit = revenue - cost, profit_margin = profit / revenue, gross_margin, AOV = revenue / order_count, unit_total = quantity * unit_priceLearn fi derive key sales metrics from raw CSV fields. Yuh wi compute profit, margins, AOV, an unit totals, ensurin formulas are consistent, well documented, an aligned wid business definitions.
Computin profit an gross marginCalculatin profit_margin safelyDerivin AOV from revenue an ordersUnit totals from quantity an unit_priceAlignin metrics wid business definitionsLesson 7Techniques for reproducible ETL: documented steps, scripts, notebooks, and checksums for CSV import integrityLearn how fi design reproducible ETL pipelines fi sales CSVs. Yuh wi script transformations, track versions, use notebooks fi exploration, an apply checksums an validation steps fi guarantee import integrity over time.
Scriptin repeatable CSV transformationsUsin notebooks fi exploratory ETLVersionin ETL code an configurationChecksums an file integrity validationAutomated ETL runs an logginLesson 8Missing values and null patterns: detection methods, imputation strategies, and when to drop rowsMaster techniques fi detect an treat missin or null values in sales CSVs. Yuh wi profile null patterns, choose imputation strategies, decide when fi drop rows, an document assumptions fi protect downstream metrics.
Profilin missingness across key columnsVisualizin null patterns an correlationsImputation strategies fi numeric fieldsImputation strategies fi categorical fieldsRules fi safely droppin rows or columnsLesson 9Data types and parsing: date formats, numeric types, categorical encoding, handling string vs numeric valuesLearn how fi correctly parse dates, numbers, an categories in sales CSVs. Yuh wi distinguish text from numeric fields, apply locale-aware pars in, an design robust categorical encodings dat remain stable across refreshes.
Detectin column data types in CSV importsParsin dates wid multiple locale formatsHandlin numeric separators an currency symbolsDesignin stable categorical encodingsConvertin mixed-type columns safelyLesson 10Dealing with multi-line orders and aggregation at order vs order-line levelLearn how fi handle orders dat span multiple lines in sales CSVs. Yuh wi distinguish order an order-line grain, aggregate correctly, an avoid double countin revenue, quantity, an discounts in dashboards.
Identif yin order vs order-line grainAggregatin revenue at order levelSummarizin discounts across linesAvoidin double countin in rollupsChoosin grain fi dashboard metrics
