Scaled prediction through generation of an extrapolated algorithm based on a formula algorithm of a spreadsheet file and/or a data analysis process initiated by an extra-spreadsheet instruction in a cell of the spreadsheet file

A method of scaled data analysis of a spreadsheet file, the method comprising:

• receiving data of the spreadsheet file comprising a formula algorithm defining a data model, the formula algorithm comprising one or more spreadsheet formulas stored in one or more cells of the spreadsheet file, one or more independent variables referenced from one or more cells of the spreadsheet file, and a prediction metric output in a cell of the spreadsheet file,
• wherein the spreadsheet file further comprising an extra-spreadsheet instruction comprising at least one of a word, a phrase, a symbol, and data stored in a cell of the spreadsheet file that initiates an action comprising causing a data analysis process to run;
• generating from at least one of the data model and the data analysis process an extrapolated algorithm expressed in a programming language that is at least one of a query language, an interpreted programming language, and a functional programming language,
• wherein each of the one or more spreadsheet formulas is equivalent to one or more functions of the programming language and each of the one or more independent variables defines a declared variable of at least one of the one or more functions of the programming language;
• specifying a first computation block comprising one or more data entries of a dataset comprising two or more data entries;
• extracting from the dataset each of the one or more data entries within the first computation block;
• submitting the first computation block and the extrapolated algorithm to a computing cluster over a network for parallel processing;
• and
• receiving an output data re-combined from data comprising a first output block and one or more additional output blocks, the output data comprising a prediction value of the prediction metric for each of the two or more data entries of the dataset.

A non-transitory computer-readable medium storing computer readable instructions that when executed on one or more processors:

• receive a spreadsheet file comprising a formula algorithm defining a data model, the formula algorithm comprising one or more spreadsheet formulas stored in one or more cells of the spreadsheet file, one or more independent variables referenced from one or more cells of the spreadsheet file, a prediction metric output in a cell of the spreadsheet file, and an extra-spreadsheet instruction stored in one or more cells of the spreadsheet file, the extra-spreadsheet instruction comprising at least one of a word, a phrase, a symbol, and data stored in a cell of the spreadsheet file that causes an action comprising running a data analysis process;
• generate from at least one of the data model and the data analysis process an extrapolated algorithm expressed in a programming language that is at least one of a query language, an interpreted programming language, and a functional programming language,
• wherein each of the one or more spreadsheet formulas is equivalent to one or more functions of the programming language and each of the one or more independent variables defines a declared variable of at least one of the one or more functions of the programming language;
• specify a first computation block comprising one or more data entries of a dataset comprising two or more data entries;
• extract from the dataset each of the one or more data entries within the first computation block;
• submit the first computation block and the extrapolated algorithm to a computing cluster over a network for parallel processing;
• and
• receiving an output data re-combined from data comprising a first output block and one or more additional output blocks, the output data comprising a prediction value of the prediction metric for each of the two or more data entries of the dataset.