{"id":12575,"date":"2022-01-01T21:53:17","date_gmt":"2022-01-02T03:53:17","guid":{"rendered":"http:\/\/www.designandexecute.com\/designs\/?p=12575"},"modified":"2022-01-20T22:36:14","modified_gmt":"2022-01-21T04:36:14","slug":"data-science-vs-software-engineering","status":"publish","type":"post","link":"https:\/\/www.designandexecute.com\/designs\/data-science-vs-software-engineering\/","title":{"rendered":"Data Science vs Software Engineering"},"content":{"rendered":"

Data Science vs Software Engineering \u2013 Exploring Projects<\/strong><\/h1>\n
\n\n\n\n\n\n\n\n\n\n\n\n
<\/th>\nSoftware Engineering<\/strong><\/th>\nData Science<\/strong><\/th>\n<\/tr>\n<\/thead>\n
Project Feasibility<\/strong><\/td>\nGenerally known upfront whether a project is executable<\/td>\nMight not be known until late project phases<\/td>\n<\/tr>\n
Focus<\/strong><\/td>\nDelivering functioning software systems<\/td>\nDelivering actionable insights<\/td>\n<\/tr>\n
Longest Phase<\/strong><\/td>\nDevelopment (coding)<\/td>\nData preparation<\/td>\n<\/tr>\n
Scope<\/strong><\/td>\nLargely defined by stakeholders and product managers<\/td>\nSomewhat define-able by stakeholders and product managers but also needs to be uncovered based on what the data scientists discover<\/td>\n<\/tr>\n
Task Estimation<\/strong><\/td>\nTask completion time is generally estimate-able<\/td>\nThe time required to deliver many steps are unknown<\/td>\n<\/tr>\n
Progress<\/strong>\u00a0Tracking<\/strong><\/td>\nSomewhat definitive through metrics like\u00a0number of features<\/em>\u00a0or\u00a0story points complete<\/em><\/td>\nMore ambiguous. Example: Being 50% done with a model doesn\u2019t mean anything.<\/td>\n<\/tr>\n
Knowing it works<\/strong><\/td>\nMostly binary. Software either works per the specifications or it does not (e.g. the user interface loads or it doesn\u2019t)<\/td>\nMany shades of gray. Given a model, one person can say it is working and another could say it is not. Both can be right given their frame of reference.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

Software Engineering follows the SDLC<\/strong><\/h3>\n

This is the cycles followed by waterfall or agile teams to deliver shippable functioning software.<\/p>\n

\"\"<\/a>
Software Development Lifecycle<\/figcaption><\/figure>\n

Data Science follows many frameworks<\/strong><\/h3>\n

CRISP-DM Life Cycle<\/strong><\/h3>\n

The\u00a0CR<\/strong>oss\u00a0I<\/strong>ndustry\u00a0S<\/strong>tandard\u00a0P<\/strong>rocess for\u00a0D<\/strong>ata\u00a0M<\/strong>ining (CRISP-DM<\/em>) is a process model with six phases that naturally describes the\u00a0data science life cycle<\/a>. It\u2019s like a set of guardrails to help you plan, organize, and implement your data science (or machine learning)\u00a0project.<\/p>\n

    \n
  1. Business understanding\u00a0\u2013 What does the business need?<\/li>\n
  2. Data understanding\u00a0\u2013 What data do we have \/ need? Is it clean?<\/li>\n
  3. Data preparation\u00a0\u2013 How do we organize the data for modeling?<\/li>\n
  4. Modeling\u00a0\u2013 What modeling techniques should we apply?<\/li>\n
  5. Evaluation\u00a0\u2013 Which model best meets the business objectives?<\/li>\n
  6. Deployment\u00a0\u2013 How do stakeholders access the results?<\/li>\n<\/ol>\n
    \"\"<\/a>
    CRISP DM Life Cycle. Source: Wikimedia<\/figcaption><\/figure>\n

    BADIR\u00a0Life Cycle<\/strong><\/h3>\n

    BADIR seeks to enhance chance of sucess in Data Science.\u00a0 98% of Data Science project fail due to lack of usage by business stake holders.\u00a0 They do not use the production models because it does not answer their questions<\/strong>.\u00a0 BADIR is hypothesis driven and hyper focused to ensure it answers the business critical questions as its primary objective.<\/p>\n

    Narrow the scope. <\/strong>Frequently, organizations have grand visions of what they want to accomplish with analytics. They launch large data initiatives that eventually crumble under the weight of their own ambitious scopes before any value can be generated. Rather than going high and wide with your analytics (boiling the ocean), it generally makes more sense to go start-to-finish with a more targeted, narrow focus. A limited scope may sound counterintuitive, but you\u2019re more likely to generate insights and business value faster, which can\u00a0build momentum<\/a>\u00a0for future, expanded analytics efforts.<\/p>\n

    BADIR delivers 20X+ impact by focusing on top drivers and guaranteed actionable results.<\/p>\n

    \"\"<\/a>
    BADIR Framework<\/figcaption><\/figure>\n

    https:\/\/aryng.com\/aryng-BADIR-advantage<\/a><\/p>\n

    \"A
    Brent Dykes – Data Marathon<\/figcaption><\/figure>\n

    THE EVALUATION PHASE HAS SUB CATEGORIES<\/h3>\n
      \n
    1. Data visualization.<\/strong>\u00a0To monitor business performance, your data must be visualized in reports and dashboards. By sharing this summarized information throughout your organization, managers and employees will be able to observe how different aspects of the business are performing.<\/li>\n
    2. Data analysis.\u00a0<\/strong>To gain deeper insights into the business, your people will need to explore the data for potential issues or opportunities. An iterative process of data discovery will help your organization unlock insights that can lead to enhanced business performance.<\/li>\n
    3. Insight communication.\u00a0<\/strong>To ensure the insights drive the right decisions and actions, they must be communicated effectively.\u00a0Data storytelling<\/a>\u00a0opens the audience\u2019s minds to new possibilities, using engaging narratives and clear visuals to explain key insights.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

      Data Science vs Software Engineering \u2013 Exploring Projects Software Engineering Data Science Project Feasibility Generally known upfront whether a project is executable Might not be known until late project phases Focus Delivering functioning software systems Delivering actionable insights Longest Phase Development (coding) Data preparation Scope Largely defined by stakeholders and product managers Somewhat define-able by […]<\/p>\n","protected":false},"author":2,"featured_media":12579,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[32,31],"tags":[],"class_list":["post-12575","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bi-dashboards-analytics","category-bi-data-warehouse"],"jetpack_featured_media_url":"https:\/\/www.designandexecute.com\/designs\/wp-content\/uploads\/2022\/01\/crisp-dm-wikicommons.jpg","_links":{"self":[{"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/posts\/12575","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/comments?post=12575"}],"version-history":[{"count":5,"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/posts\/12575\/revisions"}],"predecessor-version":[{"id":12736,"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/posts\/12575\/revisions\/12736"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/media\/12579"}],"wp:attachment":[{"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/media?parent=12575"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/categories?post=12575"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.designandexecute.com\/designs\/wp-json\/wp\/v2\/tags?post=12575"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}