If<\/em> you have an analytical bend of mind, you may consider a career in the field of data science. Though there are many options to take your pick from, most aspirants have their eyes fixated on the coveted job of data scientists<\/strong>. But who exactly are data scientists<\/strong>? You may call them a new class of analytical data expert, who possess the technical skills necessary to solve complex problems. At the same time, these people also have the inquisitiveness to search for problems that need to be solved. Thus, when defining a data scientist<\/strong>, you can say it\u2019s someone who blends some expertise of a computer scientist, a mathematician, and a trend-spotter. What\u2019s the beauty of the field of data science is that it has numerous applications in various domains. Thus, you will find that there\u2019s a high demand for data scientists<\/strong> in both the business domain and the field of IT, which make these professionals highly sought-after and well-paid. No wonder why a majority of aspirants looking to make a mark in the field of data science aim to become data scientists<\/strong>.<\/p>\n

1- Who\u2019s a data scientist?<\/strong><\/h3>\n

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Even<\/em> a decade ago, data scientists<\/strong> weren\u2019t a hot property that they have become today. Perhaps the change in their fortunes signals how times have changed. This could be attributed to the massive amount of data that\u2019s getting generated almost every second today. And with the emergence of big data, companies and businesses too have changed their views on how they see data and even the ways they can leverage the pile of data that they have been sitting upon for quite some time now. After all, the data businesses collect these days or the ones their existing and potential customers willingly share via their website, special campaigns, social media accounts, etc. make up a bulky mass of unstructured information. No business worth its salt can ignore or forget this data anymore as it\u2019s nothing short of a virtual gold mine that can bring several benefits their way and give their revenue a significant boost. But it will happen only when someone digs into this pile of massive data and discovers business insights that no one considered looking for before. And this is where the data scientist <\/strong>comes into the picture.<\/p>\n

As these people have an intense intellectual curiosity and are deep thinkers, they interpret this data and try to draw useful insights from them. From making new discoveries and asking new questions to learning new things, data scientists<\/strong> are driven their originality and creativity to solve complex problems and indulge in their curiosity constantly. Thus, data scientists don\u2019t just make an observation with the complex reads from data. Rather, they seek to uncover the “truth” that lies hidden underneath the surface. For these professionals, problem-solving isn\u2019t merely a task. Rather, it\u2019s an intellectually-stimulating trip to find a solution. Thus, you will find data scientists<\/strong> designing and building new procedures for data modeling, and production utilizing algorithms, prototypes, predictive models, custom analysis etc to decode data and gather useful insights, which are then presented to tell a story to the stakeholders. These decision-makers can then use this insight to make data-driven, timely decisions that will help them take on challenges, if any, be better prepared to stay ahead of the competition, and even improve their bottom-line significantly. No wonder why Harvard Business Review called data scientist<\/strong> the 21^{st<\/sup> century\u2019s sexiest job.<\/p>\n}

Now that you have an overview of who a data scientist<\/strong> is and what kind of role he\/she plays, let\u2019s try to find what makes these professionals better than others working with data such as data analysts<\/strong>, financial analysts, and research analysts. But before we do that, it\u2019s important to take a closer look at what roles these professionals play.<\/p>\n

2- Who\u2019s a data analyst?<\/strong><\/h3>\n

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Along<\/em> with data scientists<\/strong>, data analysts<\/strong> too are in high demand. Together, they are often called as DSA (data science and analytics) job. According to Forbes, DSA job listings are expected to grow by almost 364,000 listings to touch the mark of approximately 2,720,000. Just like data scientist<\/strong> posts, those for data analysts<\/strong> too aren\u2019t the easiest positions to fill. Perhaps this explains what Forbes says about DSA jobs \u2013 they remain open for an average of 45 days, which is five days more than the market average.<\/p>\n

The role of a data analyst<\/strong> is the one that people often confuse with a data scientist. <\/strong>It\u2019s true that professionals in both these roles have a similarity as they work with data. Yet, the main difference between them arises based on what they do with the data.<\/p>\n

A data analyst\u2019s<\/strong> primary role is to assemble, categorize, and study data to offer business insight. Typically, data analysts<\/strong> are concerned with cleansing, aggregating, managing, and abstracting data in addition to conducting a variety of analytical studies on that data. Here\u2019s a peek into some of these responsibilities that would help you know what they actually mean:<\/p>\n

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• Cleansing: This refers to the procedure of checking data accuracy and quality by recognizing and then removing biased or incorrect data from a database.<\/li>\n
• Aggregating: This is the compilation of information from multiple data sources to organize combined datasets for data processing.<\/li>\n
• Managing: This involves planning data processes as well as executing and maintaining them to ensure safe storage of data and information assets.<\/li>\n
• Abstracting: It\u2019s the process of removing a dataset\u2019s characteristics to reduce it to a set of essential characteristics, which in turn would help in more efficient data processing.<\/li>\n<\/ul>\n

  By leveraging additional software engineering and ML (machine learning) skills, a data scientist builds upon a data analyst\u2019s<\/strong> core competencies. Thus, you will find data scientists actively exploring unique ways to use existing and new algorithmic, statistical, predictive, artificial intelligence (AI), and machine learning (ML) tools and techniques to discover valuable and significant patterns in data and convert these into information for the company or organization.<\/p>\n

  Perhaps you now understand that though the two job titles and even some roles may be deceptively similar, being a data scientist<\/strong> is better than being a data analyst<\/strong>.<\/p>\n

  3- Who\u2019s a financial analyst?<\/strong><\/h3>\n

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  A<\/em> financial analyst is responsible for collecting and organizing financial information followed by its analysis after which S\/he would create presentations and offer recommendations, which will get shared with a company\u2019s clients or the stakeholders.<\/p>\n

  The primary responsibility of financial analysts is to create financial models that can forecast the result of specific business decisions. To do this the right way, these professionals need to collect a large pile of financial data while also considering factors such as earlier transactions having a similar nature, financial market trends, etc. Based on where a financial analyst works, his\/her role can differ a lot. For example, a financial analyst working in an investment bank will be much more focused on helping with deals and mergers while the one working for an insurance company would be more concerned with the risks involved in different lines of insurance, how they would affect premiums, etc.<\/p>\n

  The difference between a financial analyst and a data analyst<\/strong> is that the former works with large number of datasets that come from a wide range of sources such as customers, operations, safety etc. and analyzing them and turning them into recommendations and takeaways for management or clients. The main difference between a financial analyst and a data analyst<\/strong> is that while the former only works with financial and accounting figures, the latter works with a wide variety of numbers from diverse industries.<\/p>\n

  Perhaps this gives you a clearer picture of why being a data scientist<\/strong> is better than being a financial analyst.<\/p>\n

  4- Who\u2019s a<\/strong> research analyst?<\/strong><\/h3>\n

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  This<\/em> is a professional who is responsible for researching, examining, interpreting and presenting data related to operations, markets, economics, accounting\/finance, customers, and other information related to the field s\/he works in. Typically, a research analyst is extremely analytical, quantitative, and logical apart from being adept in handling data.<\/p>\n

  Almost every industry engages research analysts though they are more commonly found in some specific industries like the financial services industry, retail industry, etc. A broad job category is covered by the research analysts, especially in the areas of operations research, market research, and industry research. The role of an operations research analyst is to study particular aspects of an organization\u2019s business processes and work out means to improve them. For a market research analyst, the responsibilities include studying the markets to help businesses understand what kind of demand exists for services or products. For an industry analyst, the job entails researching on a specific company or specific industry in addition to keeping track of new developments and trends in an industry. In brief, the key responsibility of a research analyst is to research and find ways to improve the operations of the business or company s\/he is involved with.<\/p>\n

  Thus, the role of a research analyst is limited as compared to that of a data analyst<\/strong>. And since it\u2019s better to be a data scientist<\/strong> than a data analyst<\/strong>, you can infer that the post of a data scientist<\/strong> is much more coveted than that of a research analyst.<\/p>\n