{"id":237,"date":"2024-07-08T12:33:26","date_gmt":"2024-07-08T07:33:26","guid":{"rendered":"https:\/\/augurytech.co.uk\/courses\/?p=237"},"modified":"2024-10-12T08:30:31","modified_gmt":"2024-10-12T03:30:31","slug":"data-science-vs-machine-learning","status":"publish","type":"post","link":"https:\/\/augurytech.co.uk\/courses\/artificial-intelligence\/data-science-vs-machine-learning\/","title":{"rendered":"Data Science vs Machine Learning"},"content":{"rendered":"\n

In today\u2019s tech-driven world, terms like \u201cData Science<\/strong>\u201d and \u201cMachine Learning<\/strong>\u201d are often used interchangeably. However, they are distinct fields with unique roles and applications.<\/p>\n\n\n\n

\n

Data Science<\/h2>\n\n\n\n

Data Science is a broad field that focuses on extracting insights and knowledge from data. It involves various techniques and tools to analyze large sets of data, both structured (like spreadsheets) and unstructured (like text and images). Data scientists use programming, statistics, and domain knowledge to solve complex problems.<\/p>\n\n\n\n

Key Components of Data Science:<\/h3>\n\n\n\n
    \n
  • Data Collection:<\/strong> Gathering data from various sources.<\/li>\n\n\n\n
  • Data Cleaning:<\/strong> Removing errors and inconsistencies from the data.<\/li>\n\n\n\n
  • Data Analysis:<\/strong> Using statistical methods to understand the data.<\/li>\n\n\n\n
  • Data Visualization:<\/strong> Creating charts and graphs to present findings.<\/li>\n\n\n\n
  • Predictive Modeling:<\/strong> Making predictions based on data patterns.<\/li>\n<\/ul>\n\n\n\n
    <\/div>\n\n\n\n

    Suppose you want to have a team analyze your dataset to gain insights. The output of a data science project is a set of insights that can help you make business decisions.<\/strong><\/p>\n\n\n\n

    So, a team might come up with conclusions like:<\/p>\n\n\n\n

      \n
    • “Hey, did you know if you have two houses of a similar size, they’ve got a similar square footage? If the house has three bedrooms, then they cost a lot more than the house of two bedrooms, even if the square of this is the same.”<\/li>\n\n\n\n
    • Did you know that newly renovated homes have a 15% premium? This can help you make decisions such as, given a similar square footage, do you want to build a two-bedroom or three-bedroom home to maximize value?<\/li>\n\n\n\n
    • Is it worth an investment to renovate a home in the hope that the renovation increases the price you can sell a house for?<\/li>\n<\/ul>\n\n\n\n

      The output of a data science project is a set of insights that can help you make business decisions,<\/strong> such as what type of house to build or whether to invest in renovation.<\/p>\n\n\n\n

      <\/div>\n\n\n\n

      Machine learning<\/h2>\n\n\n\n

      Machine Learning (ML) is a subset of Artificial Intelligence (AI) that focuses on building systems that can learn from data and improve over time without being explicitly programmed. In other words, ML algorithms use data to make predictions or decisions.<\/p>\n<\/div>\n\n\n\n

      \n

      Machine Learning:<\/strong> “Field of study that gives computers the ability to learn without being explicitly programmed.<\/em>” – Arthur Samuel (1959)<\/strong><\/p>\n\n\n\n

      A machine learning project will often result in a piece of software that runs, and outputs B given A.<\/p>\n\n\n\n

      Fundamental Components of Machine Learning:<\/h3>\n\n\n\n
        \n
      • Algorithms:<\/strong> Step-by-step procedures for calculations.<\/li>\n\n\n\n
      • Training Data:<\/strong> Data used to teach the algorithm.<\/li>\n\n\n\n
      • Model:<\/strong> The output of the training process, which can make predictions.<\/li>\n\n\n\n
      • Evaluation:<\/strong> Testing the model to see how well it performs.<\/li>\n\n\n\n
      • Deployment:<\/strong> Using the model in real-world applications.<\/li>\n<\/ul>\n<\/div>\n\n\n\n
        <\/div>\n\n\n\n
        \n\n\n\n
        <\/div>\n\n\n\n
        \n

        How They Work Together<\/h2>\n\n\n\n

        While Data Science and Machine Learning are different<\/strong>, they often work together. Data Science provides the data and insights needed for Machine Learning models. For example, a data scientist might analyze customer data to find patterns and then use Machine Learning to predict future customer behaviour.<\/p>\n\n\n\n

        Key Differences<\/h3>\n\n\n\n
          \n
        • Scope:<\/strong> Data Science<\/em> is broader and includes various techniques for data analysis, while Machine Learning<\/em> is specifically about creating algorithms that learn from data.<\/li>\n\n\n\n
        • Focus:<\/strong> Data Science <\/em>focuses on understanding and interpreting data, whereas Machine Learning<\/em> focuses on making predictions and decisions based on data.<\/li>\n\n\n\n
        • Tools:<\/strong> Data scientists <\/em>use Python, R, and SQL for data analysis. Machine Learning engineers<\/em> use frameworks like TensorFlow and PyTorch to build models.<\/li>\n<\/ul>\n<\/div>\n\n\n\n
          <\/div>\n\n\n\n
          \n

          Running Artificial Intelligence System<\/h3>\n\n\n\n

          A software that automatically returns output B for input A.<\/p>\n\n\n\n

          If you have an AI system running, serving dozens or hundreds of thousands or millions of users, that’s usually a machine learning system.<\/p>\n<\/div>\n\n\n\n

          <\/div>\n\n\n\n
          <\/div>\n\n\n\n
          \n

          Example: in the online ad industry<\/h4>\n\n\n\n

          Large platforms have AI that quickly tells them what’s the Ad you’re most likely to click on. This is a machine learning system. It inputs information about the user and about the Ad and outputs whether the user will click on the Ad or not.
          These systems run 24\/7 and drive ad revenue for these platforms.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

          In today\u2019s tech-driven world, terms like \u201cData Science\u201d and \u201cMachine Learning\u201d are often used interchangeably. However, they are distinct fields with unique roles and applications. Data Science Data Science is a broad field that focuses on extracting insights and knowledge from data. It involves various techniques and tools to analyze large sets of data, both […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"artificial-intelligence","format":"standard","meta":{"_seopress_robots_primary_cat":"2","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"categories":[2],"tags":[],"class_list":{"0":"post-237","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-artificial-intelligence"},"_links":{"self":[{"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/posts\/237","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/comments?post=237"}],"version-history":[{"count":29,"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/posts\/237\/revisions"}],"predecessor-version":[{"id":1194,"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/posts\/237\/revisions\/1194"}],"wp:attachment":[{"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/media?parent=237"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/categories?post=237"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/augurytech.co.uk\/courses\/wp-json\/wp\/v2\/tags?post=237"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}