{"id":10604,"date":"2021-11-16T14:24:59","date_gmt":"2021-11-16T13:24:59","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/?p=10604"},"modified":"2023-05-17T14:36:33","modified_gmt":"2023-05-17T12:36:33","slug":"what-is-data-science","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-data-science\/","title":{"rendered":"What Is Data Science? A Comprehensive Introduction"},"content":{"rendered":"

What is data science? And what does a data scientist actually do? Here\u2019s your ultimate guide.<\/strong><\/p>\n

When it comes to decision-making, data is vital. This is true on a personal level, but absolutely essential on an organizational level\u2014an overwhelming majority of today\u2019s businesses and organizations rely on data-driven decision making and strategic plans in order to achieve their goals. <\/span><\/p>\n

So who deals with this, and how? There are a range of roles that work with data in order to glean insights from it: from data analyst, to data scientist, to data engineer<\/a>\u2014and more.\u00a0<\/span><\/p>\n

In this article, we\u2019ll focus on the fascinating field of data science.\u00a0 We\u2019ll ask the following questions:<\/span><\/p>\n

    \n
  1. What is data science?<\/a><\/strong><\/li>\n
  2. What is data science used for?\u00a0<\/a><\/strong><\/li>\n
  3. What does a data scientist do?<\/a><\/strong><\/li>\n
  4. What is the life cycle of a data science project?<\/a><\/strong><\/li>\n
  5. What tools do data scientists use?<\/a><\/strong><\/li>\n
  6. What skills do you need to become a data scientist?<\/a><\/strong><\/li>\n
  7. How do I become a data scientist?<\/a><\/strong><\/li>\n
  8. What are some of the best data science courses?<\/a><\/strong><\/li>\n
  9. What is the average data scientist salary?<\/a><\/strong><\/li>\n
  10. Key takeaways and further reading<\/a><\/strong><\/li>\n<\/ol>\n

    If you\u2019d like to skip ahead to any section, simply use the clickable menu. Now, let\u2019s get started!<\/span><\/p>\n

    1. What is data science?<\/span><\/h2>\n

    According to Japanese statistician Chikio Hayashi<\/a>, data science is a \u201cconcept to unify statistics, data analysis, informatics, and their related methods” in order to “understand and analyze actual phenomena\u201d in data.<\/span><\/p>\n

    So, what does this mean in plainer language? It means that data science is a multidisciplinary field that uses a variety of methods to make use of the vast amounts of data available to us and extract valuable insights that help drive future decision-making for individuals and organizations.<\/span><\/p>\n

    So, what are some of these disciplines, then? In data science, skills such as data analytics, machine learning, computer science and artificial intelligence are all employed in order for data scientists to research and identify areas of interest within their organization.\u00a0<\/span><\/p>\n

    2. What is data science used for?\u00a0<\/span><\/h2>\n

    Data science is used to provide the data-driven insights that help answer questions that stakeholders in an organization may have. But what does this really mean? In this section, we\u2019ll list some of the common business applications for data science.<\/span><\/p>\n

    Predicting individual consumer behavior<\/span><\/h3>\n

    In order to determine how much revenue a retailer can expect from an individual customer, data scientists will look to produce a metric known as \u2018customer life value\u2019, or CSV. Data scientists will make use of <\/span>predictive analytics<\/span><\/a> that can be applied to various aspects of the customer\u2019s retail experience, which build up an accurate picture for the retailer and allows them to offer greater customer personalization, enhancing the retail experience for the consumer.<\/span><\/p>\n

    Increasing security and protecting information<\/span><\/h3>\n

    As you can probably tell by now, a lot of big data collected and stored is often of a sensitive nature. <\/span><\/p>\n

    In order to maintain a user\u2019s trust and ensure data is used in an ethical manner, many organizations will incorporate a <\/span>data privacy<\/b> policy. Data science is used to increase the security of the organization and protect this sensitive information. <\/span><\/p>\n

    We see the word <\/span>encryption<\/b> being used a lot online. When data is encrypted, it means that the plain text\u2014like a user\u2019s address, for example\u2014is scrambled, using an encryption algorithm, into an unreadable format, otherwise known as <\/span>ciphertext<\/b>. Only authorized parties are able to \u201cunscramble\u201d this data for use with an <\/span>encryption key<\/b>.<\/span><\/p>\n

    Detecting fraud<\/span><\/h3>\n

    Fraud is rife in the banking and insurance industries (and other related industries), to the point that organizations will employ large teams to detect and resolve issues related to fraud. <\/span><\/p>\n

    However, instances of fraud are so frequent that a human team is often not enough to catch them all\u2014and this is where data science comes into play. Data scientists will use machine learning and predictive analytics to detect fraudulent transactions or claims, giving team members more time to focus on resolving issues and saving an organization both time and money.<\/span><\/p>\n

    3. What does a data scientist do?<\/span><\/h2>\n

    In short, a data scientist will collect, analyze, and interpret large amounts of data\u2014known as <\/span>big data<\/span>\u2014in order to provide insights into an organization\u2019s operations. They may develop statistical models that analyze these large amounts of data, detecting trends, patterns, relationships and outliers in datasets.\u00a0<\/span><\/p>\n

    So, what is big data? We cover it in detail <\/span>in our big data guide<\/span><\/a>, but in short: it\u2019s huge swathes of unstructured data that are generated whenever we do, well, anything! Think about how connected we are, using phones, social media, ATMs, and so on. Every time you interact with a piece of technology, a bit of data is created. <\/span><\/p>\n

    Now, think about how many other people are doing the same interaction with the same piece of technology. All these bits of data are being generated at once, and often in such a way that is too fast and complex to be processed in a traditional\u2014or structured\u2014manner.<\/span><\/p>\n

    Learn more:<\/b> Structured vs. Unstructured Data: What\u2019s the Difference?<\/span><\/a><\/p>\n

    Data scientists work with big data to make sense of the swathes of unstructured data, and to discover data insights and present them to stakeholders and decision-makers, who use these insights to identify business and operational risks, to predict consumer behavior, and to improve overall operations.<\/span><\/p>\n

    On a day-to-day basis, the specific tasks of a data scientist will be dependent on the type of organization they\u2019re working in, as well as the specific goals they\u2019re working towards. Broadly speaking, however, they\u2019ll likely encounter some\u2014or all\u2014of the following tasks and responsibilities:<\/span><\/p>\n