{"id":1991,"date":"2019-10-04T03:22:30","date_gmt":"2019-10-04T03:22:30","guid":{"rendered":"http:\/\/kusuaks7\/?p=1596"},"modified":"2024-03-14T13:15:20","modified_gmt":"2024-03-14T13:15:20","slug":"six-bits-of-advice-for-data-scientists","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/six-bits-of-advice-for-data-scientists\/","title":{"rendered":"Six bits of advice for Data Scientists"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tTo err is human.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe important thing is to look at our mistakes. And learn from them.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA data scientist needs to be\u00a0critical\u00a0and always on a lookout of something that others miss.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBut sometimes in our day to day job and coding perse, we get lost in our train of thought and fail to look at the overall picture.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn the end,\u00a0our business partners have only hired us to generate value, and we won\u2019t be able to generate value unless we develop business critical thinking.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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1. Beware of the Clean Data Syndrome<\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nHow many times it happens that we start working straight on the data we get. Start creating models? Or even present descriptive analytics generated automatically to our business counterparts?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBut, do you ever ask \u2014\u00a0Does this data make sense?<\/strong>\n
Falsely assuming that the data is clean could lead you towards wrong Hypotheses.<\/blockquote>\nYou actually can discern a lot of important patterns by looking at discrepancies in the data.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFor example, if you notice that a particular column has more than 50% values missing, one might think about dropping the column.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBut\u00a0what if some data collection instrument has some error?\u00a0You could have helped the business to improve the process.<\/em><\/strong>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOr let us say you have a distribution of Male vs. Female as 90:10 in a Female Cosmetic business.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOne may assume clean data and show the results as it is or they can use common sense and ask their business partner if the labels are switched.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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2. Be Aware<\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWe all know fab.com. For those who don\u2019t, it is a website that sells \u201cCURATED HEALTH, FITNESS & WELLNESS PRODUCTS.\u201d\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBut it was not always so.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFab.com started up as fabulis.com,\u00a0a site to help gay men meet people.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOne of the site\u2019s popular features was the \u201cGay deal of the Day.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOne day the deal was for Hamburgers \u2014 and half of the buyers were women. Why were women on the site?<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe above fact caused the data team to realize that there was a market for selling goods to women.\u00a0<\/em><\/strong>So Fabulis.com changed its business model to fab.com as a sale site for designer products.\n
Be on the lookout for something out of the obvious. Be ready to ask questions. If you find something you may have hit gold.<\/blockquote>\nData can help a business to optimize revenue, but sometimes data has the power of changing the direction of the company as well.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAnother example,\u00a0Flickr started up as a Multiplayer game<\/a>. Only when the founders noticed that people were using it as a photo upload service, did they pivot to a photo-sharing app.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThere are countless examples as such.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTry to make a good example out of your company\u2019s business as well.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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3. Start Focusing on the right metrics<\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWhat do we want to optimize for?<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tMost of the businesses fail to answer this simple question.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tEvery business problem is a little different, and it should be optimized differently.<\/em><\/strong>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFor example, A website owner might ask you to optimize for active users.\u00a0But is it the\u00a0<\/em><\/strong>right metric<\/em><\/strong>?<\/em><\/strong>\u00a0It is just a vanity metric, which will always increase.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tI would instead try to optimize the percentage of users that are active to know how my product is performing.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAnother Example, We all have created classification models. A lot of time we have tried to increase accuracy for our models.\u00a0But do we want accuracy as a metric of our model performance?<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nWhat if we are predicting the number of asteroids that will hit the earth.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tJust say zero all the time. And you will be 99% accurate. My model can be reasonably accurate, but not at all valuable. A better metric would be the F score.\n
Designing a Data Science project is much more important than the modeling itself.<\/blockquote>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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4. Statistics Lie sometimes, maybe a lot of times<\/h3><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Source<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tBe critical of everything that gets quoted to you.\u00a0<\/em><\/strong>Statistics have been\u00a0used to lie\u00a0in advertisements, in workplaces and a lot of other marketing venues in the past. People will do anything to get sales or promotions.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFor example:\u00a0Do you remember Colgate\u2019s claim that 80% of dentists recommended their brand?<\/em><\/strong><\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThis statistic seems pretty good at first. All dentists use Colgate; I should too. Right?\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIt turns out that at the time of surveying the dentists, they could choose several brands \u2014 not just one. So other brands could be just as popular as Colgate.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe marketing department is just a myth creation machine. I can understand that.\n
The marketing department is just a myth creation machine.<\/blockquote>\nBut it is painful when you see such sort in research. For instance, the\u00a0Small Arms survey<\/a>\u00a0suggests that\u00a0for every 100 Americans, there are 120 guns.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nIt feels reasonable to assume that every American must be packing heat. Then there is another study that shows that\u00a0only 47 percent of households have guns in them<\/a>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAre you confused yet?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSome of the household pack tons of guns. And thus it won\u2019t be reasonable to say that every American is armed.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAlso\u00a0never trust a chart that doesn\u2019t label the Y-axis.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBelow chart was shown by\u00a0Rep. Jason Chaffetz<\/strong>\u00a0(R-UT) during a congressional hearing, to Planned Parenthood president, showing how abortions are going up and life-saving procedures are down.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tLook at the real picture with labeled axes, and one can see the lies being told\u2026\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAdd the fact that the U.S. Preventative Services Task Force changed its recommendation to get cancer screenings every two years, instead of every year, and one can even explain the decline in Cancer Screenings.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Be objective while seeing charts presented by politicians.<\/blockquote>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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5. The Long string rule of Probability<\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIt happened during the summer of 1913 in a Casino in Monaco.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tGamblers watched in amazement as a casino\u2019s roulette wheel landed on black\u00a026 times in a row.<\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAnd since the\u00a0probability\u00a0of a Red vs. Black is precisely half,\u00a0they were confident that red was \u201cdue\u201d.<\/em><\/strong>\u00a0It was a field day for the Casino \u2014 a perfect example of\u00a0Gambler\u2019s fallacy<\/a>, aka the Monte Carlo fallacy.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAnd this happens in real life.\u00a0People tend to avoid long strings of the same answer<\/em><\/strong><\/a>. Sometimes sacrificing accuracy of judgment for the sake of getting a pattern of decisions that look fairer or probable.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFor example,\u00a0an admissions officer may reject the next application if he has approved three applications in a row,\u00a0<\/em><\/strong>even if the application should have been accepted on merit.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tDon\u2019t give in to such fallacies.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe world works on probabilities. We are seven billion people, doing an event every second of our life.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tRare events are bound to happen. But don\u2019t put your money on them.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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6. Correlation does not imply Causation<\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nCan you believe this \u2014 Autism being caused by Organic food. Good. Or the fact is just the opposite.\u00a0Does Autism increase organic food sales?\u00a0Not really. Or maybe.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tJust because two variables move together in tandem doesn\u2019t necessarily mean that one causes the another.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Correlation does not imply causation.<\/blockquote>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIt is the Holy\u00a0Grail\u00a0of a Data scientist toolbox.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThere have been other hilarious examples for this in the past. Some of my favorites are:\n