{"title":"Basic","description":null,"products":[{"product_id":"free-set","title":"Free Set","description":"\u003cp\u003e\u003cspan\u003eProblem Statement\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eBeginning data learners often encounter isolated commands without understanding how those commands belong within a complete analytical process. They may see examples of filtering, selecting columns, or calculating summaries, but still feel uncertain about when each operation should be used.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLarge collections of unrelated examples can also make it difficult to identify a sensible starting point. Without a clear sequence, learners may move between advanced and introductory topics before understanding the underlying data structure.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eSolution\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFree Set presents the subject through a gradual sequence. Learners begin by examining rows, columns, values, and data types before moving into selection, filtering, sorting, and simple summaries.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eEach lesson explains the purpose of an operation, shows how it changes a dataset, and includes a small activity that encourages learners to review the result.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWhat’s Inside\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe course contains introductory modules covering tabular data concepts, dataset inspection, column selection, row filtering, sorting, missing values, basic expressions, and grouped summaries.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLearners also receive guided examples, short review questions, practical exercises, terminology notes, and a compact workflow checklist.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWho Is This For?\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFree Set is intended for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003ePeople beginning their study of data science\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLearners exploring Polars for the first time\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eStudents who prefer structured explanations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAnalysts reviewing foundational data operations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelopers interested in data-processing concepts\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAnyone who wants to examine the course format before choosing another tier\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eWhat You’ll Learn\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eRecognize rows, columns, schemas, and data types\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLoad and inspect a small dataset\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSelect individual columns and groups of columns\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eFilter rows using clear conditions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSort information by one or more values\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eIdentify and review missing data\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCreate introductory column expressions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCalculate basic summary values\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eGroup records into simple categories\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eOrganize several operations into a readable workflow\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eReview outputs and identify common data issues\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e30-Day Refund Policy\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003ePaid course tiers include a 30-day refund request period, subject to the refund terms shown on the website. Free Set does not require payment, so no refund request is needed for this tier.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eCertification\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe course includes a certificate of completion, giving learners a clear way to confirm their progress and present a record of the course they completed.\u003c\/span\u003e\u003c\/p\u003e","brand":"Nolvexiranora","offers":[{"title":"Default Title","offer_id":58486521430364,"sku":null,"price":0.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1040\/1556\/1052\/files\/free.jpg?v=1785338180"},{"product_id":"axis-pack","title":"Axis Pack","description":"\u003cp\u003e\u003cspan\u003eProblem Statement\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eMany learners understand basic filtering and column selection but encounter difficulty when a dataset contains inconsistent values, unsuitable data types, repeated records, missing entries, or unclear column names.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThese issues can make later analysis harder to follow. A dataset may appear ready for use while still containing formatting differences, duplicate rows, mixed date styles, or values stored in the wrong format.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eSolution\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eAxis Pack introduces a repeatable data-preparation workflow. Learners study how to inspect a schema, identify common data-quality issues, convert data types, rename columns, handle missing values, remove repeated records, and create cleaner structures for later work.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eEach module connects one preparation task to the next. Instead of treating cleaning steps as isolated commands, the course shows how they form a connected workflow that can be reviewed and adjusted.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWhat’s Inside\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eAxis Pack contains detailed modules on schema inspection, column naming, type conversion, null handling, duplicate detection, text cleaning, date preparation, conditional expressions, and reusable transformation sequences.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe materials include guided examples, small datasets, review questions, preparation checklists, and exercises based on common analytical situations.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLearners also work through a compact project in which an untidy dataset is reviewed, corrected, documented, and prepared for grouped analysis.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWho Is This For?\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eAxis Pack is intended for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eLearners who have completed an introductory data course\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePeople who understand basic rows and columns\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAnalysts who want a clearer preparation process\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelopers working with structured datasets\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eStudents learning how to identify data-quality issues\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLearners who prefer practical tasks with gradual explanation\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eWhat You’ll Learn\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eRead and interpret a dataset schema\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eIdentify unsuitable or inconsistent data types\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eConvert text, numeric, date, and logical values\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eRename columns using a consistent structure\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDetect and remove repeated records\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eReview missing values across several columns\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eFill, replace, or remove missing entries where appropriate\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eClean text values by trimming and standardizing content\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePrepare date columns for sorting and comparison\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCreate conditional columns from existing values\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCombine several preparation steps into one workflow\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCheck intermediate results before continuing\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDocument the reasoning behind data-cleaning decisions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePrepare a dataset for grouped summaries and later analysis\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e30-Day Refund Policy\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eAxis Pack includes a 30-day refund request period, subject to the terms presented on the website. Learners may review the policy details before completing a purchase.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eCertification\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe course includes a certificate of completion, giving learners a clear way to confirm their progress and present a record of the course they completed.\u003c\/span\u003e\u003c\/p\u003e","brand":"Nolvexiranora","offers":[{"title":"Default Title","offer_id":58486529130844,"sku":null,"price":78.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1040\/1556\/1052\/files\/axis.jpg?v=1785338180"},{"product_id":"frame-kit","title":"Frame Kit","description":"\u003cp\u003e\u003cspan\u003eProblem Statement\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eReal data tasks often involve more than one table. Customer details may be stored separately from orders, category information may appear in another file, and dates or identifiers may use different formats across datasets.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLearners who understand basic cleaning may still find it difficult to decide how tables should be connected. An unsuitable join type, mismatched key column, or repeated identifier can create missing rows, duplicated records, or confusing results.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eReshaping data can present another challenge. Information may be arranged across many columns when it would be more useful in rows, or repeated categories may need to become separate columns for reporting.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eSolution\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFrame Kit explains how to examine relationships between datasets before combining them. Learners study key columns, matching values, join types, row counts, and schema compatibility.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe course also introduces practical reshaping methods. Each module shows how data changes during an operation and explains how to review the result before moving to the next step.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWhat’s Inside\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFrame Kit contains modules covering vertical and horizontal concatenation, table joins, key preparation, suffix handling, nested structures, column expansion, pivot-style organization, unpivoting, and multi-table workflow design.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe materials include guided examples, relationship diagrams, comparison tables, review questions, workflow checklists, and practical exercises.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLearners complete a course project that combines several related datasets into one organized analytical table.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWho Is This For?\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFrame Kit is intended for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eLearners familiar with basic data preparation\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAnalysts working with several related tables\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eStudents studying relational data concepts\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelopers organizing structured information\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLearners who want to understand joins more clearly\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePeople preparing datasets for reports or grouped analysis\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eWhat You’ll Learn\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eIdentify relationships between separate datasets\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eChoose suitable key columns for table operations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCompare inner, left, full, semi, and anti joins\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eReview unmatched records after a join\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePrevent unexpected duplication during table combinations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePrepare key columns with consistent data types\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eConcatenate tables with compatible schemas\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eHandle overlapping column names\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eExpand structured values into readable columns\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eReshape wide data into a longer format\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eArrange category values into separate columns\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCheck row counts before and after transformations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCombine several datasets in a clear sequence\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDocument table relationships and transformation decisions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePrepare a consolidated dataset for later analysis\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e30-Day Refund Policy\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFrame Kit includes a 30-day refund request period, subject to the terms presented on the website. Learners may review the policy details before completing a purchase.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eCertification\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe course includes a certificate of completion, giving learners a clear way to confirm their progress and present a record of the course they completed.\u003c\/span\u003e\u003c\/p\u003e","brand":"Nolvexiranora","offers":[{"title":"Default Title","offer_id":58486531555676,"sku":null,"price":119.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1040\/1556\/1052\/files\/frame.jpg?v=1785338180"},{"product_id":"flux-deck","title":"Flux Deck","description":"\u003cp\u003e\u003cspan\u003eProblem Statement\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eAfter learning how to prepare and combine datasets, learners often need to perform more detailed calculations. They may need to compare categories, calculate values within groups, create new columns from several conditions, or summarize information across different time periods.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThese tasks can become difficult to manage when expressions are written without a clear structure. Repeated calculations, unclear aliases, and long transformation chains may make a workflow harder to read and review.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eSolution\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFlux Deck introduces analytical expressions through a gradual sequence. Learners begin with arithmetic and conditional calculations before moving into grouped aggregations, window expressions, ranking, and reusable expression patterns.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe course emphasizes clear naming, logical sequencing, and regular result checks. Learners examine how each expression changes a dataset and how several expressions can work together inside one analytical process.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWhat’s Inside\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFlux Deck contains modules covering expression contexts, arithmetic operations, conditional logic, string expressions, date calculations, grouped aggregations, window expressions, ranking, cumulative calculations, and reusable expression groups.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe materials include annotated examples, comparison diagrams, analytical exercises, review questions, workflow notes, and a structured course project.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eDuring the project, learners prepare an analytical dataset, calculate category-level measurements, compare records within groups, and organize the results into a clear summary table.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWho Is This For?\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFlux Deck is intended for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eLearners familiar with data preparation and table joins\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAnalysts who want to organize calculations more clearly\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eStudents studying grouped data analysis\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelopers creating repeatable data transformations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLearners working with category, date, and numeric information\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePeople who want to understand expression contexts in Polars\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eWhat You’ll Learn\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eDistinguish between selection and aggregation contexts\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCreate calculated columns from existing values\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eWrite conditional expressions with several branches\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eApply arithmetic operations to numeric columns\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eWork with text values through expression methods\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eExtract and compare date components\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eGroup records by one or more categories\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCalculate totals, averages, counts, and ranges\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eApply several aggregations within one grouped operation\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eUse window expressions without reducing row detail\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eRank values within categories\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCreate cumulative totals and running calculations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCompare each record with a group-level value\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eName calculated columns clearly\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan\u003eReuse related expression patterns\u003c\/span\u003e\u003cspan\u003e\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e30-Day Refund Policy\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFlux Deck includes a 30-day refund request period, subject to the terms presented on the website. Learners may review the refund conditions before completing a purchase.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eCertification\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe course includes a certificate of completion, giving learners a clear way to confirm their progress and present a record of the course they completed.\u003c\/span\u003e\u003c\/p\u003e","brand":"Nolvexiranora","offers":[{"title":"Default Title","offer_id":58486533325148,"sku":null,"price":174.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1040\/1556\/1052\/files\/flux.jpg?v=1785338180"},{"product_id":"flow-course","title":"Flow Course","description":"\u003cp\u003e\u003cspan\u003eProblem Statement\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLearners may understand individual data operations but still find it difficult to combine them into a complete workflow. A script can include loading, cleaning, joining, calculating, and summarizing steps, yet become difficult to review when those stages are not clearly separated.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLong transformation chains may also hide important decisions. It can become unclear where values were changed, why records were removed, or which calculation produced a final result.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eAnother challenge is deciding when to inspect intermediate outputs. Without review points, a small issue introduced near the beginning of a workflow may affect every later stage.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eSolution\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFlow Course presents analytical work as a sequence of defined stages. Learners study how to plan a workflow before writing transformations, divide larger tasks into readable sections, and check results after important operations.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe course connects earlier skills into broader examples. Learners work with datasets that require preparation, joining, expression design, grouped analysis, and final reporting.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eEach module encourages clear naming, documented decisions, reusable logic, and careful review of intermediate and final outputs.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWhat’s Inside\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFlow Course includes modules on workflow planning, staged transformations, reusable functions, expression organization, validation checks, error review, analytical summaries, and final dataset preparation.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe materials contain guided demonstrations, workflow diagrams, practical exercises, planning templates, review questions, and a detailed course project.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe project guides learners through the full process of examining raw tables, preparing columns, connecting related datasets, calculating analytical measures, checking results, and producing a final structured output.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWho Is This For?\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFlow Course is intended for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eLearners who understand core Polars operations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAnalysts building multi-stage data workflows\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eStudents connecting separate data science concepts\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelopers organizing repeatable transformation logic\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePeople working with several related datasets\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLearners who want to improve workflow readability\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAnyone interested in creating clearer analytical processes\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eWhat You’ll Learn\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003ePlan a data workflow before writing transformations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDivide complex tasks into clear analytical stages\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eOrganize loading, cleaning, joining, and calculation steps\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCreate readable names for columns and workflow sections\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eReuse transformation logic across related tasks\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eBuild small helper functions for repeated operations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAdd checks for schemas, row counts, and missing values\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eReview intermediate datasets at important points\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eIdentify where unexpected values enter a workflow\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCompare results before and after key transformations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eConnect several datasets within one analytical process\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCombine row-level and grouped calculations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePrepare summary tables for review\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDocument assumptions and transformation decisions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eStructure a final dataset for later use\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eRefine long workflows into clearer sections\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCreate a complete Polars course project from raw data to final output\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e30-Day Refund Policy\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eFlow Course includes a 30-day refund request period, subject to the terms displayed on the website. Learners may review the refund conditions before completing a purchase.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eCertification\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe course includes a certificate of completion, giving learners a clear way to confirm their progress and present a record of the course they completed.\u003c\/span\u003e\u003c\/p\u003e","brand":"Nolvexiranora","offers":[{"title":"Default Title","offer_id":58486537159004,"sku":null,"price":192.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1040\/1556\/1052\/files\/flow.jpg?v=1785338180"}],"url":"https:\/\/nolvexiranora.org\/collections\/basic.oembed","provider":"Nolvexiranora","version":"1.0","type":"link"}