How<\/em> can a non-profit organization best use its available marketing budget to enhance its potential operations further? How can a business sort through customers\u2019 purchasing data to develop a marketing plan to rise above the competition? These questions become even more important when you consider the seemingly-infinite amount of data that can be sorted, interpreted, and implemented for a diverse range of purposes. For this reason, people should compare the data by learning data science.<\/strong><\/em><\/a><\/p>\n

1- What is a data scientist?<\/em><\/strong><\/h3>\n

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A data scientist<\/strong><\/em> is a trained individual who can accumulate, organize, and analyze data, thus helping businesses <\/strong><\/em><\/a>from every walk of industry make informed decisions. These high-tech breeds work extensively with massive amounts of structured as well as unstructured data to derive valuable insights in order to meet specific business goals and needs. Essentially, data scientists<\/strong> wear multiple hats. They\u2019re part computer scientist, part mathematician, part analyst, and part trend-spotter, apart from having some critical non-technical skills as well. We\u2019ll delve deeper into this later, but first let\u2019s have a look at the common industries that are being benefitted by data scientists<\/strong>.<\/p>\n

2- Industries able to leverage the power of data science<\/em><\/strong><\/h3>\n

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Each<\/em> industry comes with its own big data profile that can be analyzed by a data scientist<\/strong>.<\/span> Here\u2019re some of the common industries that can leverage big data.<\/p>\n

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• Government:<\/strong> Here, security and compliance are two prime concerns for data scientist<\/strong> They help governments to monitor overall performance, support constituents, and develop decisions. Data science training<\/strong><\/em><\/a> is appropriate for these.<\/li>\n
• Business:<\/strong> In today\u2019s competitive business <\/strong>landscape, it\u2019s simply impossible for a business to thrive without leveraging the power of big data. From customer loyalty to production errors to inventory management to efficiency improvement \u2013 analysis of business data is needed to excel.<\/li>\n
• Healthcare:<\/strong> Today, healthcare facilities are heavily dependent on electronic medical records with assurance of adherence to security and compliance. Here, data scientists<\/strong> can aid in improving health services by analyzing big data.<\/li>\n
• Telecommunication:<\/strong> Every electronic device gathers data and it needs to be stored, managed, interpreted and analyzed to improve product and\/or service quality, eliminate bugs, and enhance customer experience.<\/li>\n
• Science<\/em><\/a>:<\/strong> Though data has always been a key area of interest to scientists, emergence of data science<\/strong> empowers these professionals to gather, maintain, and analyze data through experiments.<\/li>\n
• Finance:<\/strong> For a functioning business in the finance domain, it\u2019s imperative to analyze massive amount of data on different kinds of transactions and accounts. Like the government sector, maintaining security and compliance of sensitive data is one of the key responsibilities of a data scientist <\/strong>working in finance and related companies.<\/li>\n<\/ul>\n

  Apart from these, other notable industries, which are on the constant look out for data scientists<\/strong>, include social networking, ecommerce, smart appliances, and utility providers, among others.<\/p>\n

  3- Key responsibilities of a data scientist<\/em><\/strong><\/h3>\n

  Though<\/em> the key responsibilities of a data scientist<\/strong> depend on the project he\/she is working on, it can be said that all of them are based on big data or complicated inputs. And all these responsibilities need a deep curiosity in order to be performed accurately. Let\u2019s have a look at the common responsibilities of a data scientist<\/strong>, regardless of the nature and volume of the business.<\/p>\n

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  • Analytics<\/em><\/a>:<\/strong> Analytical roles are imperative to data scientists<\/strong><\/em><\/a> where he\/she designs, implements, and assesses advanced statistical models as well as approaches that are related to the business\u2019s most critical issues. Here, statistical and econometric models are developed encompassing different problems related to pattern analysis, clustering, simulations, projections, sampling etc.<\/li>\n
  • Strategy:<\/strong> A data scientist <\/strong>helps in developing business strategies to form new approaches by analyzing customer trends and behaviors etc \u2013 which are essential for product performance optimization and increasing revenue. Here, advanced statistical techniques are implemented to gain actionable insights. Sometimes, unstructured datasets are also analyzed by the data science professional to create manageable analytical processes.<\/li>\n<\/ul>\n

    Apart from these two, a data scientist <\/strong>has to stay updated about relevant industry\u2019s trends continually to provide useful recommendations to the business.<\/strong> Value-based programs and strategic initiatives are two of the key areas that are such a professional focuses upon. It\u2019s important to understand that a data scientist\u2019s<\/strong> role has to be collaborative. It means in order to solve complex business issues, he\/she has to closely work with other teams like the IT department, product managers, data engineers, data analytics team etc.<\/p>\n

    4- Future scope for data scientists<\/em><\/strong><\/h3>\n

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    Now<\/em> that you know why these professionals are in massive demand, it\u2019s important to see whether there exist an adequate number of data scientists<\/strong>. The truth is, the number of these professionals is just a handful, while the demand for them seems to be increasing by the day. As businesses lean more and more toward machine learning<\/strong><\/em><\/a> and artificial intelligence<\/strong><\/em><\/a>, there\u2019ll be more jobs than available experts to fill them, and perhaps this is the reason why data science has become one of the fastest growing tech employment fields today.<\/p>\n

    5- How to become a data scientist?<\/em><\/strong><\/h3>\n

    To<\/em> begin with, you need to have a robust acumen of sophisticated visualization and adequate knowledge of statistical techniques that are used to derive forward-looking insights.<\/span> So, what are the critical skills and attributes of a data scientist<\/strong>? Let\u2019s have a look.<\/p>\n

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    • Coding<\/em><\/a>:<\/strong> You should know how to write codes and should be comfortable dealing with an array of programming tasks. These programming skills should encircle different aspects \u2013 from handling large volumes of data, dealing with real-time data to managing unstructured data, managing cloud computing and working with statistical models \u2013 everything should be taken care of. Python is the coding language typically used in data science<\/strong> roles, together with Perl, Java, C\/C++ etc.<\/li>\n
    • Critical thinking:<\/strong> It\u2019s one of those skills that would set you apart from the pack. Critical thinking enables data scientists<\/strong> to implement objective analysis of facts on a given problem or fact before they can render judgments or formulate opinions. A business decision or problem has to be understood clearly before a model, which is critical to resolving the issue or supporting the decision, can be developed. It\u2019s all about evaluating a situation or problem from multiple points of view, and not about looking at things with the eyes wide open.<\/li>\n
    • Mathematics:<\/strong> If you don\u2019t like or aren\u2019t proficient in mathematics, probably becoming a data scientist<\/strong> shouldn\u2019t be your career goal. You\u2019ve already learned that massive amounts of data are what you will have to work with in this field. Thus, it\u2019s imperative for you to leverage deep knowledge in mathematics to develop statistically relevant, complex operational or financial models that are capable of shifting or developing key business strategies.<\/li>\n
    • AI and machine learning<\/em><\/a>:<\/strong> Adequate knowledge of high-end techniques like reinforcement learning, neural networks, supervised machine learning,<\/strong> natural language processing, adversarial learning etc are a must for a data scientist<\/strong>. As huge volumes of data are being constantly collected, these cutting-edge technologies help a data science professional to make the data speak what\u2019s needed by itself.<\/li>\n
    • Communication:<\/strong> Regardless of how advanced a technology is, there\u2019s always some integration between data, applications, systems and humans. A good data scientist<\/strong> should be able to communicate properly with different people using data as a major attribute. Type of communication can vary to a great extent \u2013 from the challenges with data quality<\/strong><\/em>,<\/a> to technology and computational resources to distill complex technical information into accurate, easy-to-understand format \u2013 everything should be properly communicated to help business leaders direct business processes.<\/li>\n<\/ul>\n

      6- Education requirements:<\/em><\/strong><\/h3>\n

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      Though<\/em> there\u2019re different paths to become a data scientist<\/strong>, it\u2019s absolutely impossible to land into the field without a bachelor\u2019s degree. In addition, if your aim is to get an advanced leadership position, having a doctorate or a master\u2019s degree should be your best bet. There\u2019re data science degrees offered by some schools that can empower you with the skills necessary to process and analyze a complex set of massive data. Most of these programs boast of an analytical and creative element, apart from technical aspects related to analysis techniques, statistics, computers, and more. Some of the common fields of degrees that can help you become a data scientist<\/strong> include statistics, computer science, mathematics, economics<\/em> etc.<\/p>\n

      Final takeaway<\/em><\/strong><\/h3>\n

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      Before<\/em> you delve deeper into your endeavor of becoming a data scientist<\/strong>, it\u2019s important to understand that you\u2019ll have to work in different settings, with different teams and in collaboration. The actual work environment can vary largely based on the organization and the nature of business you\u2019ll work for. There\u2019re lots of satisfying factors to becoming a data scientist<\/strong> like having a unique yet challenging career, options for working for a diverse range of companies, getting engaged in interesting and unique subjects and topics that offer you a wide perspective, and working with the latest technologies<\/em> – among others. On the flip side, there\u2019re some clear drawbacks too. For instance, the technologies you\u2019ll be using will be evolving constantly, which means you may find extreme variety of software and systems, which you\u2019ll have to learn on a constant basis. However, as data science<\/strong> is required by almost every organization and business across the globe, and all of them are increasingly relying on data for developing strategies, the need for data scientists<\/strong> will become all the more important, making the demand in data<\/strong><\/em><\/a> increasing steadily in the near future.<\/span><\/p>\n