NeuraPath Journal

Learn the work behind Data, AI & Forward Deployed Engineering

Practical explanations, career decisions and reproducible workflows. Read the reasoning, inspect the evidence and follow the next skill into a real programme.

1305 articlesPage 72 of 109
Data ScienceClustering, reduction and recommendations

Evaluate recommendations with temporal holdouts

A random interaction split can train on a user's future and test on their past. Recommendation systems serve forward in time, so an offline split should reproduce what the system knew before each held-out interaction.

20 Sept 20262 min read
Generative AI & Agentic AIRetrieval quality and grounded answers

Evaluate retrieval on numerical tables

Table questions require the right row, column, unit and condition. A response can quote the correct number from the wrong band and still look plausible.

20 Sept 20262 min read
Generative AI & Agentic AIRetrieval quality and grounded answers

Evaluate retrieval recall at k with labelled evidence

Generation cannot cite evidence that retrieval never supplied. Recall@k measures whether at least one labelled relevant item appears among the first k eligible results for an answerable question.

20 Sept 20262 min read
Data ScienceNLP and text analytics

Evaluate sentiment on mixed and sarcastic examples

A sentiment model can score clear praise and complaints well while failing exactly where interpretation matters. Mixed statements contain competing aspects. Sarcasm can reverse literal wording. Evaluate these as named sl

20 Sept 20262 min read
Data AnalyticsExcel and spreadsheet quality

Excel data cleaning: preserve the raw sheet and document changes

Clean spreadsheet data should remain traceable to what arrived. Keep an unchanged source snapshot, transform a separate working table and record rules that explain every dropped row, changed label or rejected value. A po

20 Sept 20264 min read
Data AnalyticsExcel and spreadsheet quality

Excel date serials and imported timestamps explained

Excel dates are numeric values displayed through date formatting. A timestamp's time-of-day component is represented by a fraction of a day. Text that looks like a date is a different kind of value and may behave differe

20 Sept 20263 min read
Data AnalyticsExcel and spreadsheet quality

Excel formula errors: fix the cause before using IFERROR

IFERROR changes what a formula displays when it encounters an error. It does not correct the underlying calculation, restore missing data or distinguish every possible cause. Replacing errors with zero can turn a visibly

20 Sept 20263 min read
Data AnalyticsExcel and spreadsheet quality

Excel missing values: distinguish blank cells and empty strings

Two cells can look empty while containing different things. One may be truly unused; another may contain a formula returning an empty string; a third may contain a space. Numeric zero is different again. Choose a missing

20 Sept 20263 min read
Data AnalyticsExcel and spreadsheet quality

Excel percentage points versus percentage change

A rate rising from 20% to 25% increases by five percentage points and by 25% relative to its previous value. These are different descriptions of the same change. Label the unit explicitly so readers do not confuse an abs

20 Sept 20263 min read
Data AnalyticsExcel and spreadsheet quality

Excel scenario analysis with explicit assumptions

Scenario analysis asks what a model produces under specified assumptions. It does not establish that those assumptions will occur. Keep inputs, calculations and outputs separate, and label hypothetical scenarios clearly

20 Sept 20263 min read
Data AnalyticsExcel and spreadsheet quality

Excel SUMIFS with dates and inconsistent category labels

SUMIFS adds values that meet several conditions, but it cannot repair incorrect dates, duplicated rows or inconsistent category mappings. Establish a clean reporting table first, then express each eligibility rule explic

20 Sept 20263 min read
Data AnalyticsExcel and spreadsheet quality

Excel weighted averages for price and discount analysis

A weighted average gives each observation influence according to a meaningful denominator, such as units sold or eligible gross value. An ordinary average gives each row equal influence, which can be misleading when rows

20 Sept 20263 min read
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