{"id":10157,"date":"2018-07-06T00:00:00","date_gmt":"2018-07-06T05:00:00","guid":{"rendered":"https:\/\/threecloud.wpengine.com\/post\/3-key-factors-to-any-successful-data-science-project\/"},"modified":"2022-11-30T09:12:58","modified_gmt":"2022-11-30T15:12:58","slug":"3-key-factors-to-any-successful-data-science-project","status":"publish","type":"post","link":"https:\/\/3cloudsolutions.com\/resources\/3-key-factors-to-any-successful-data-science-project\/","title":{"rendered":"3 Key Factors to Any Successful Data Science Project"},"content":{"rendered":"<p>Are you in the process of or looking to implement data science projects in your organization? If you\u2019re just starting out, today I\u2019d like to give you the 3 key factors to make any data science project successful.<\/p>\n<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/yyT0_J1zJWc\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p><strong>1. Ask a sharp question of your data.<\/strong> It\u2019s imperative to ask a question that has a very specific answer to it for our model to be able to give us that specific answer. In other words, ask an obscure question and you\u2019ll get an obscure answer.<\/p>\n<p>For example, in a customer churn scenario, we can ask \u2018Is this customer going to cancel their subscription in the next 3 months?\u2019 There is a specific answer here that the model can determine and give back to us. If you make it more obscure, the model may get confused and it won\u2019t be as accurate as you\u2019d like.<\/p>\n<p><strong>2. Prepare your data.<\/strong> I\u2019m sure you\u2019ve heard of \u2018garbage in, garbage out\u2019, right? This applies to a data science or machine learning project as well. The data coming in needs to be as clean as we can get it, so we can pass it through that model, train the model and get accurate results out.<\/p>\n<p>One example is to look for columns that have rows that don\u2019t match the type the columns should hold. If it\u2019s primarily text type columns and we have rows with numbers that don\u2019t make sense, that will throw the model off.<\/p>\n<p>Also, get rid of missing data. If there are columns that are only 10% populated, there\u2019s not going to be much use to our model to be able to do some predictions.<\/p>\n<p>Another point in data preparation is the model needs a table of numbers and words. To run a model, we can consume all kinds of data \u2013 unstructured video or audio files or maybe determine sentiment that goes inside of those for instance. What we need to do in the model layer is take that unstructured data and somehow map it into a table, so we can do analysis on it, train our models and produce accurate models for predicting outcomes.<\/p>\n<p>We also need to create features that are going to best help answer our question. For instance, we may have a couple columns in our data set, maybe a start and end time, but really the column that helps us predict or answer the question would be the duration between these two.<\/p>\n<p>Features is just a calculation between multiple columns in our data set that give us the exact number or word that we\u2019re looking for to run through our model and to train it and then be able to answer questions of that.<\/p>\n<p><strong>3. The last step is to create and train a model that can answer your question.<\/strong> After all the work in steps one and two, we need to pick a model and train it with some of that data, preferably some historical data that we have with those answers in them, and then create a model that we can pass data to answer questions moving forward.<\/p>\n<p>So, focus on these key factors; put that model into use, get some ROI on that, which will then turn it into a successful project.<\/p>\n<p>Need further help? Our expert team and solution offerings can help your business with any Azure product or service, including Managed Services offerings. Contact us at 888-8AZURE or\u00a0 <a href=\"mailto:sales@3cloudsolutions.com\">sales@3cloudsolutions.com<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Are you in the process of or looking to implement data science projects in your&mldr;<\/p>\n","protected":false},"author":21,"featured_media":9522,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[260],"tags":[],"class_list":["post-10157","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai","topics-blog"],"acf":[],"_links":{"self":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/10157","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/comments?post=10157"}],"version-history":[{"count":0,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/10157\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media\/9522"}],"wp:attachment":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media?parent=10157"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/categories?post=10157"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/tags?post=10157"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}