标签：#analysis

Introduction: Turning Jumia Product Data into Business Insights E-commerce platforms generate a lot of product data, but raw numbers become useful only when they can support better decisions. For Jumia sellers, prices, discounts, ratings and customer reviews can provide clues about product performance, customer engagement and possible pricing strategies. For this project, I worked with a dataset of 112 Jumia products to explore these relationships using Microsoft Excel. I wanted to find out whether higher discounts are associated with more customer reviews, whether highly rated products receive stronger engagement, and whether product price is related to rating. I also wanted to identify the products performing best and those that may require a different pricing or marketing approach. I followed a complete data-analysis workflow: Raw Data → Cleaning → Transformation → Analysis → Visualization → Insights → Recommendations The project uses Excel Tables, Power Query, formulas and functions, PivotTables, PivotCharts, slicers and dashboard techniques. This article documents that process and shows how the raw Jumia data was transformed into an interactive dashboard and, ultimately, evidence-based business recommendations. Understanding the Dataset and Its Initial Problems Before cleaning the data, I first needed to understand what I was working with. The dataset contains 112 Jumia products and six main fields: Product, Current Price, Old Price, Discount, Review and Rating. Current Price and Old Price represent product pricing, Discount captures the promotional percentage, Review represents the number of customer reviews, while Rating records the average customer rating out of 5. I treated this stage as a data-quality audit rather than immediately changing anything. The purpose was to identify issues that could affect calculations and visualizations later. The raw dataset contained formatting and consistency issues that needed attention, particularly around numerical field

During Naomi Osaka's match against Anastasia Zakharova at this year's US Open earlier this week, a gaggle of ring light-wielding influencers who were packed in a luxury suite became enough of a distraction that the umpire paused the match and repeatedly asked them to quiet down. Elsewhere in the USTA Billie Jean King National Tennis […]

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can add an extension to a dashboard, tell the two hosting kinds apart, and read the permission box well enough to know what you're agreeing to. You'll also know the one behavior that surprises people after publishing, which is what an extension looks like in a PDF. It's about twelve minutes. Here's what to do before you add your first one. Find out where it runs. An extension you drop onto a dashboard is a web application, and some of them are hosted on Tableau-managed servers while others are hosted by whoever built them. That single fact decides how much thought the rest of the decision needs. The short version: an extension is a third-party web application running inside a dashboard object, and one of the two permission levels gives it your full underlying data along with table and field names. Where the code actually runs is the thing the panel doesn't show you, so it gets the picture. The original carries a diagram here. In words: A large rectangle labeled your dashboard contains four panels that all look alike. Three of them are shaded the same and marked as ordinary views. The fourth, in the lower right and outlined in a warning color, is labeled extension. A line runs from that fourth panel, crosses the boundary of the dashboard rectangle, and continues out to a separate box drawn outside and to the right labeled third-party host. The three ordinary views have no lines leaving the rectangle. The drawing shows that the extension panel sits inside the dashboard visually while its code and its data traffic reach outside it, which the other three panels never do. 1. What an extension actually is Before the explanation: you drop an extension onto a dashboard and it draws a chart type Tableau doesn't have. Where did that chart come from? From a web application, written by somebody else, running inside a panel on your dashboard. Tableau's own description is that extensions "let

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can turn a chart that says E, W, N and S into one that says East, West, North and South, in about thirty seconds, without editing the data or writing a calculation. You'll also know exactly why the Aliases option is missing on some fields, which is the part that sends people looking for a workaround they don't need. It's about ten minutes. Here's the move. Right-click a dimension in the Data pane, choose Aliases, and type the name you want beside each value. The chart updates, the stored data doesn't change, and every view built on that field picks up the new labels. The short version: an alias renames the members of a discrete dimension. Only discrete dimensions have members, which is why measures, dates and continuous dimensions can't have one. An alias sits in a specific place, between what's stored and what's shown, and that placement explains everything else here. So it gets the picture. The original carries a diagram here. In words: Three stacked panels connected left to right. The left panel is labeled stored and holds four small cells reading E, W, N and S. The middle panel is a narrow vertical band labeled alias, holding four arrows. The right panel is labeled shown and holds four cells reading East, West, North and South. A solid arrow runs from the stored panel through the alias band to the shown panel, indicating the direction labels travel. A second arrow attempting to run backwards from the shown panel to the stored panel is crossed through with a heavy X, showing that renaming the label never changes the stored value. The stored cells still read E, W, N and S after the change. This is on the certification. Aliases sit in Section 2, Exploring and Analyzing Data, which is 37% of the Tableau Desktop Foundations exam and the largest section on it. The questions people get wrong are almost always about which field types accept an alias, which is section 2 below. 1. What

Instagram's visible AI labels are supposed to help people quickly spot synthetically generated content at a glance. Over the last few weeks, however, users have been reporting that the system has gone haywire. They say Meta has been automatically applying an "AI Content" label to images that they didn't create or edit using generative AI […]

OpenAI's next big model is here: GPT-6 Astra. The company calls it a "generational leap in capability" for areas like cybersecurity, professional work, software engineering, science, and computer use. As OpenAI announced earlier this week, it's also the first model designated as meeting OpenAI's "critical cybersecurity capability threshold" - but the company promises that won't […]

昨天我在這裡寫下一句話：阿富如果真的相信大盤跟 00919 該用兩套邏輯，就該讓它們分開受審，別再兩邊押同一個 flat。今天早上八點半，他對加權指數押 down、信心 0.42，對 00919 押 flat、信心 0.37。 盤前他看到的隔夜訊號幾乎全是空的：美股三大指數收黑，道瓊 -0.79%、標普 -0.71%、納斯達克 -1.03%；西德州原油單日漲 5.2% 站上 90 美元；美國十年期公債殖利率升到 4.79% 的新高；美軍 9 月 2 日對伊朗又打了一輪，長達六個半小時。對面只站著一個利多，SEMICON Taiwan 在南港的第二天，主題鎖在 AI。 他把前面那串叫 M 類，跨資產的總體訊號；把展會叫 S 類，單一產業的排定事件。他的新規則寫著，M 類壓過 S 類的時候，指數跟著 M 類走，信心壓在 0.40 到 0.45 之間。00919 走另一條路：前十大成分股裡五檔金融股碰上殖利率走揚不見得吃虧，廣達、聯詠、瑞昱、華碩那幾檔則被展會利多跟晶片股逆風夾在中間，方向混沌，所以判 flat。 風控那邊他設了兩條線：00919 跌破 31.70 就把 36 股全部出清，當日虧損碰到 60 元就停止一切新交易。他還先算過摩擦成本，真要停損賣掉，手續費加證交稅約 2 元，占部位不到 0.2%。 這是他上線以來第一次對兩個標的給出不同方向。 中午的時候，這條規則看起來已經死了 10:30 巡檢，加權指數 46,225.41，比前一天收盤漲 0.13%。00919 報 32.72，漲 1.14%，帳上未實現獲利 89 元。12:30 再看一次，指數 46,281.86，漲幅擴到 0.25%；00919 報 32.76，漲 1.27%，未實現 91 元。 預測押跌，市場整個上午在漲。他自己在盤中紀錄裡寫得很直白：若收盤維持這個方向，這會是新規則的第一個反向樣本。他沒有偷偷改口，也沒有把預測往回調。這一點我給分。 尾盤翻黑 加權指數收 45,857.66，比前一天跌 0.665%。日內最高 46,517.45、最低 45,992.50，收盤價比盤中低點還低。方向回到他早上押的那一邊。大盤那題 HIT，Brier 0.210。 00919 收 32.52，比前收 32.35 漲 0.53%。flat 那題 MISS，Brier 0.284。手上 36 股市值 1,170，未實現獲利 82 元，約 7.6%。 一對一錯，這比昨天有價值得多。昨天兩邊同押 flat，一個對一個錯，事後拿兩套說法各自解釋，怎麼算都不會輸。今天他先把賭注分開放，市場再各給一個答案，其中一個明確打了他的臉。可以輸的考卷才叫考卷。 但他今天多學了一件不該學的事 復盤裡他寫：盤中偏離而收盤回歸，代表 M 類總體利空的傳導有時間延遲，可能來自尾盤外資調節或法人結算。 這句話背後只有一天的資料。今天大盤尾盤翻黑，明天可能整天黑到底，後天可能開低走高。用單一樣本去解釋盤中跟收盤的落差，等於幫規則多裝了一個彈性關節：以後盤中走反了可以說延遲還沒到，收盤走反了才算數。這種關節裝多了，規則會慢慢變成永遠不會錯的東西。 他從同一段經驗抽出的另一個結論反而是對的：盤中巡檢不該憑瞬時方向就對規則下判決，計分要以收盤為準。這是紀律，跟解釋是兩回事。 00919 那題的處理我也有意見。他把 MISS 寫成「flat 用詞精確度不足」，說核心論點「00919 對大盤衝擊的傳導較弱」方向仍然對——00919 漲 0.53%、大盤跌 0.665%，確實相對抗跌。這個辯護不算離譜，但預測寫的是 flat，收的是 0.53% 的上漲，MISS 就是 MISS。他若真覺得該預測的是「跌幅明顯小於大盤」，那就把規則直接改成相對強弱的寫法，接受它下次被更嚴格地打分，別留在原地把失分講成用字問題。 十八天，十四天沒交易 今天沒有下任何一張單。往回翻，帳戶最後一次真的成交是 8 月 14 日那筆 2317 的停損賣出，實驗第 4 天。從第 5 天到今天第 18 天，十四個交易日，零筆委託。 差別在計分。他的校準報告只算跟下單掛鉤的預測，今天那份報告的樣本數還是 12，跟昨天一模一樣，標籤還是「與運氣無法區分」，方向命中率 0.5。他每天盤前申報的預測累積到 31 筆、結算 26 筆，大盤 13 筆中 6 次命中、00919 13 筆中 6 次命中，都是 46.2%，系統照樣判定樣本不足、不給結論。 規則從舊版改到新版，判讀從一體改成分家，這些進步是真的。只是它們到現在還沒有一次真的動用到錢。帳上總資產大約 2,259 元（券商可動用餘額 1,089 加持股市值 1,170），對照本金 2,200，十八天下來多了 2.7%。目標是三十個交易日翻倍，剩十二天。 一個把預測寫得越來越細、卻十四個交易日不下

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can compute quartiles by hand, build the standard outlier fence from them, and run that fence over any column to get back a short list of rows worth looking at. On the sixteen orders below, one mistyped quantity gets flagged automatically while every honest large order stays inside the fence. Here is what to actually do today. On the column you care about most, get four numbers: the 25th percentile, the 75th, their difference, and 1.5 times that difference added to the 75th. Anything above that last number is a row to open and read. It is one query, and it turns "is this data clean" into a list of specific rows. The short version: a percentile is a value with a known share of the data below it. The interquartile range is the width of the middle half. Values more than one and a half of those widths beyond the middle half get flagged. The fence is easier to see than to read, so it gets the picture. The original carries a diagram here. In words: A horizontal line with a row of small filled dots along it, spaced unevenly and thinning out towards the right. A tall rectangle is drawn around the dots in the middle of the row, covering the central half of them, with a thick vertical bar inside it. The rectangle's left edge is labelled Q1, its right edge Q3, and the bar inside it median. From each edge of the rectangle a horizontal whisker line runs outward to a short vertical cap, reaching the furthest dot on that side that still lies within range. To the right of the right-hand cap stands a tall dashed vertical line labelled fence, drawn one and a half rectangle-widths beyond the rectangle's right edge, with a small double-headed measuring arrow underneath showing that distance against the rectangle's own width. One lone dot sits well to the right of that dashed line, drawn as a hollow ring instead of a filled dot, so it reads as picked out rather than belonging with the rest. Every oth

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can say what a p-value measures in one sentence, compute one by hand with no distribution theory at all, and name the four things people routinely claim a p-value says that it does not. The worked example is nine real orders where two regions differ by 168.50, and the answer comes out of counting rather than out of a table. Here is what to actually do today. Any time you are about to report that two groups differ, write down the two group sizes first. If either is under about twenty, a p-value will almost certainly come back large no matter how real the difference is, and the honest report is the difference, the sizes, and an interval, not a verdict. The short version: a p-value is the share of results at least as extreme as yours that you would get if the thing you are testing had no effect at all. Small means your result would be unusual under nothing-happening. It does not mean the effect is large, and it does not mean the effect is real. That definition is doing a lot of work in one sentence, so it gets the picture. The original carries a diagram here. In words: A histogram built from small dots, one dot per outcome, arranged in ten vertical columns of different heights standing on a horizontal baseline. The columns rise from one dot at the far left to a peak of twenty-seven dots just right of centre, then fall away to a single dot at the far right, giving the whole shape a rounded hump centred slightly left of the middle of the picture. Two vertical dashed lines cut down through the shape, one on the left of the hump and one on the right, placed symmetrically about the hump's centre. The dots lying in the two tails beyond those lines are drawn in a darker, warmer shade, and the dots in the bulk between them are drawn in a lighter blue, so the tails stand out from the middle. In the two columns the dashed lines pass through, the darker dots are stacked at the bottom of the co

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can build a moving average by hand, choose a window length on purpose rather than by habit, explain the lag it introduces, and spot the one data problem that silently corrupts every moving average you will ever compute. On the sixteen weeks below, the raw series wobbles with a standard deviation of 237. A three-week average brings that to 101, and a five-week to 62. Here is what to actually do today. Add a moving average to your busiest weekly or daily chart, using a window that matches your cycle: 7 for daily data with a weekday pattern, 4 or 13 for weekly, 12 for monthly. Then plot it on top of the raw series rather than instead of it, so the reader can see both the noise and the trend. The short version: a moving average replaces each point with the average of it and the points around it. Wobble cancels out, trend survives, and the price is that the smoothed line reacts late. The effect is easier to see than to describe, so it gets the picture. The original carries a diagram here. In words: A line chart with two lines drawn on the same axes over sixteen evenly spaced points. The first line is thin and violently jagged, swinging from near the top of the chart down to near the bottom and back again several times, with one especially deep trough about a quarter of the way along and one especially tall spike just past the middle. The second line is thicker and starts two points later than the first. It stays in a narrow band through the middle of the chart, rising and falling only gently, and never comes close to either the peaks or the troughs of the jagged line. Where the jagged line spikes to its highest point, the thick line rises only slightly and does so one point later than the spike. The word actual labels the jagged line near its left end, and the phrase three-week average labels the thick line. Every number on this page is real. The same sixteen orders used across these

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can compute both averages by hand, say in one sentence which one your question needs, and show what each does when one number in your data is wrong. On the sixteen orders below, a single mistyped quantity moves the mean by 337.50 and the median by 30. That gap is the whole reason both words exist. Here is what to actually do today. Next to every average you report, put the median beside it and look at the two numbers together. If they are close, report the mean and move on. If they are far apart, you have found something worth saying out loud, and this page is about what. The short version: the mean shares the total out equally, so every value pulls on it. The median is the middle value once you sort, so only the position of a value matters, not its size. That difference is easiest to see rather than read, so it gets the picture. The original carries a diagram here. In words: Two horizontal number lines, one above the other, drawn on the same scale. On the top line, sixteen small filled dots sit in a loose cluster across the left and middle of the line. Two markers sit almost on top of each other just past the centre of that cluster: a solid triangle pointing up, labelled mean, and a short vertical bar, labelled median. On the bottom line the same sixteen dots appear, except one dot from the middle of the cluster has moved far to the right and off the end of the line, shown by an arrow leaving the line through a small break mark at the right edge. The median bar on the bottom line has barely shifted from where it was on the top line, moving only a hair to the right. The mean triangle on the bottom line has slid a long way to the right, roughly ten times further than the median moved, and a horizontal dotted guide connects its old position on the top line to its new position on the bottom line to show the size of the slide. Every number on this page is real. One sixteen-row order

The idea of humanoid robots running our homes has been the stuff of science fiction for years. But unlike hoverboards and self-tying sneakers, a robot housekeeper has never felt close to rolling into our homes. Until now. This has been the year of the humanoid robot. At CES, there were dozens of human-shaped bots on […]

Google has reportedly been reaching out to a number of Hollywood's biggest studios, hoping to strike licensing agreements that would allow it to train its AI models on copyrighted material in exchange for massive piles of cash. In theory, these deals would be a win-win: a huge financial boon to the studios that would also […]

Expectations for Tim Cook were almost impossibly high when he stepped in to replace Apple's visionary cofounder in 2011. He inherited a company on a blockbuster run, after Steve Jobs returned and revitalized the Mac, launched the iPod, and oversaw the launch of the iPhone. Now, after 15 years in the CEO role, Tim Cook […]

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you can load a CSV into pandas, find out in twenty seconds what type every column became, stop the identifier columns losing their leading zeros, get dates read the way they were written, and turn a money column that arrived as text into numbers. It is about twenty-five minutes, and every output below was produced by running the code. Here is what to do today, the moment after you first load a file. Run df.dtypes . Not df.head() , which shows you what the values look like, but dtypes , which shows you what they are. A column of identifiers that says int64 has already lost its leading zeros, and a money column that says object or str is text that will refuse to add up. The short version: read_csv reads characters and guesses a type per column. The guess is usually right, it is silent when it is wrong, and four arguments replace guessing with instruction. The same characters becoming two different values is the idea, so it gets the picture. The original carries a diagram here. In words: On the left, a strip of five small square boxes holds one character each, reading zero, eight, zero, five, three, as the characters appear in the file. Two arrows branch out from that strip. The upper arrow leads to a strip of five boxes in which the first box is empty, crossed through and outlined in amber, while the remaining four hold eight, zero, five and three; the leading character has been discarded. The lower arrow leads to a strip of five boxes holding zero, eight, zero, five and three, identical to the original, outlined in blue. Both destinations came from the same source strip, and only one of them still contains everything the file did. Every output on this page is real. Run on pandas 3.0.2 against a small CSV built to contain the four problems every real export has: an identifier with leading zeros, ambiguous dates, a text marker for missing values, and money with a thousands separator. If

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you can turn transactions into a monthly series, add period-on-period change and a cumulative total, get a share-of-total column, smooth a noisy line, and run all of it separately for every group. It is about twenty-five minutes, and every number below came out of running the code. Here is what to do today, on the series you already have. Count its rows against the number of periods in your date range. If your data covers January to May and the series has four rows, a period produced nothing, it never became a row, and every change figure after the gap is comparing the wrong pair. The short version: pct_change() divides each value by the one in the row above; cumsum() adds everything up to and including the current row. Both trust the rows you gave them to be the periods you meant. What happens when the previous period is zero is the idea, so it gets the picture. The original carries a diagram here. In words: Three bar positions stand on a baseline, labelled Mar, Apr and May. The March position holds a tall bar and the May position holds a slightly shorter tall bar. The April position holds no bar at all; there is only a short flat mark sitting on the baseline where a bar would start, drawn in amber to show a value of zero. An arc runs from the top of the March bar down to the April mark, and the figure minus one hundred percent is printed on it, which is a perfectly ordinary answer. A second arc runs from the April mark up to the top of the May bar, and the symbol printed on that one is not a percentage at all but the sideways figure eight that means infinity. The picture shows that a fall to nothing has an answer and a rise from nothing does not. Every number on this page is real. The sixteen-row orders table used across this whole set of guides, run in pandas 3.0.2. It runs from 5 January to 25 May 2026 and contains no April orders at all, which is not staged for this page; it is

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you can attach columns from one DataFrame to another on a shared key, choose the right how for the question, see at a glance which rows failed to match, and catch the failure that quietly inflates every total in the frame. It is about twenty-five minutes, and every output below was produced by running the code. Here is what to do today, on every merge you write. Print the row count immediately before and immediately after it. A left merge must not change the row count, and if it did, the right-hand table has the key more than once and your totals have just gone up. The short version: merge pairs rows from two frames wherever their keys match, and the number of rows that come out depends on how many times each key appears on each side. One key twice on the right is the idea, so it gets the picture. The original carries a diagram here. In words: On the left a single row is drawn as a wide box, holding the key Desk and the value 880. To its right stands a small lookup table with two rows, and both of those rows carry the same key, Desk. Two lines run from the single left-hand row, one to each of the two matching lookup rows, so the one row is paired twice. On the far right the result is drawn as two separate output rows, and both of them contain Desk and 880; the value 880 is ringed in amber in each of them to show that it is the same original figure appearing twice. One row went in and two came out, without anything being added to the left-hand table. Every output on this page is real. Sixteen orders totalling 9,890 and a three-row product table, the same tables used across this whole set of guides, merged in pandas 3.0.2 with the results copied back. If you know SQL joins , this is the same operation with different words, and the two failure modes are identical. 1. merge in one line Two frames, one shared column, one call. orders.merge(products, on="product", how="left") order_id prod

On Nvidia's earnings call Wednesday, CEO Jensen Huang casually announced the company had "achieved AGI," one of the tech industry's ultimate goals some of its biggest players have spent years chasing. Almost immediately, Huang dismissed the coveted milestone as "senseless." He's right. For the supposed finish line of the AI race, there is no consensus […]

Meta might be on the hook for $17.1 billion and a host of app changes under a new kids safety settlement, but it's already spinning the deal to its advantage. After years of being the national punching bag for social media harms, Meta has reached a settlement with 47 US states and several districts and […]

By using a sustainable testing strategy, you can skip unnecessary tests, ensure failing fast and early, and only run tests affected by code changes. Tracking energy use per test and using static code analysis can help spot inefficiencies and guide optimization efforts. By Ben Linders