With<\/em> billions of devices connected to the web right now, a massive amount of data is being generated every single day. And millions of devices are predicted to join the league shortly, thus augmenting the production of such data to a higher level. Today, companies across industries are striving to leverage the data they capture from different sources to accomplish their business goals and they\u2019re constantly looking for skilled data science professionals to help them do it. In addition, the role of a data scientist is considered as the 21^{st<\/sup> century\u2019s hottest job<\/strong><\/em><\/a> by the HBR (Harvard Business Review).<\/span> Unquestionably, a data science professional not only earns a fattier pay packet than professionals at similar levels in other fields but also experiences a huge growth prospect.<\/p>\n}

The above facts should fascinate you whether you\u2019re a fresher or a working professional. In both these scenarios, now is probably the best time to learn data science<\/strong><\/em><\/a> to take your career to the next level, irrespective of whether you\u2019re looking to get your first job or trying to switch career or looking to climb the corporate ladder quickly.<\/p>\n

In this post, we\u2019re going to review the essentials of data science<\/strong> to help you get a clear understanding of the things you need to focus upon.<\/p>\n

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

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Put<\/em> simply, data science refers to the process of slicing through huge amounts of data, processing, and analyzing them to derive valuable insights that can help businesses take critical business decisions to complement their operations.<\/span> Data science professionals need to have or develop a wide array of skills to work on data and extract information from it.<\/p>\n

If you want to be a part of this elite league of professionals, you don\u2019t need to necessarily hold a Master\u2019s degree or a Ph.D. degree. Rather, you\u2019ve to have a robust understanding of the essentials of data science<\/strong>.<\/p>\n

2- Essential of data science explained<\/em><\/strong><\/h3>\n

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In<\/em> the earlier times, before the emergence of data science, the teams working with data were performing complex database tasks to empower corporate executives to optimize operational activities. Of course, this role is important, but today\u2019s savvy data science professionals need some more skills to be able to fully capitalize their data assets. Let\u2019s have a look at them.<\/p>\n

2.1- Critical thinking<\/em><\/h4>\n

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One<\/em> of the most important essentials of data science<\/strong> is the ability of critical thinking.<\/span> Data science professionals<\/strong> <\/em><\/a>have to understand a business problem to be able to identify what is critical to solve that problem and what can be ignored. Critical thinking isn\u2019t about analyzing things from a novice\u2019s point of view, but actually being able to evaluate a situation or problem from multiple points of view.<\/p>\n

2.2- Mathematics<\/em><\/h4>\n

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Put<\/em> simply, if you aren\u2019t proficient in or don\u2019t like mathematics, probably data science isn\u2019t a good career choice for you. Today\u2019s global organizations mostly work with clients who\u2019re trying to develop complicated operational and\/or financial models. Data science professionals need to work with large chunks of data and leverage their strong expertise in mathematics to create statistical models that may be used by the clients to develop or modify business strategies.<\/p>\n

2.3- Statistics<\/em><\/h4>\n

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Similar<\/em> to mathematics, statistics<\/strong><\/em><\/a> is another essential of data science<\/strong>. As a data science professional, you need to be familiar with distributions and statistical tests, among others.<\/span> This is also equally important for machine learning<\/strong><\/em><\/a>, but your statistics knowledge will be more required to understand when different methodologies are (or aren\u2019t) a feasible approach. Though statistics is important for any company, it\u2019s extremely crucial especially for data-driven organizations where business decisions are made mainly based on statistical models.<\/p>\n

2.4- Programming skills<\/em><\/h4>\n

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In<\/em> the data science field, regardless of the role or organization you\u2019re interviewing for, you\u2019re likely to be expected to have a robust understanding of the tools used in the trade, and programming skills are one of those essentials of data science<\/strong> without which you can never be a good data science professional.<\/p>\n

Today, Python<\/strong><\/em><\/a> has become the most preferred language in the data science domain with a significant following for R. To be a successful data science professional, your programming skills have to comprise both computational aspects like handling large chunks of data, cloud computing etc and statistical aspects like ability to work with statistical models such as clustering, optimization, regression, and decision trees, among others. It\u2019s also expected that you hold a good understanding of SQL (Structured Query Language) to understand relational databases better.<\/p>\n

2.5- Machine learning<\/em><\/h4>\n

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Industries<\/em> across the globe are moving pretty fast toward adopting machine learning<\/strong> <\/em><\/a>and artificial intelligence<\/strong><\/em><\/a> to rise above the competition. When you\u2019re working for an organization that deals with large amounts of data or where the product is particularly data-driven, you\u2019ve to be familiar with different machine learning methods.<\/p>\n

While it\u2019s true that some of the techniques used in machine learning can be implemented using Python or R libraries, a good knowledge about machine learning methods is still important. You don\u2019t need to necessarily become an expert in the field but you need to understand the broader concepts and when it\u2019s appropriate to implement different techniques.<\/p>\n

2.6- Data visualization<\/em><\/h4>\n

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Ability<\/em> to visualize data is another essential of data science<\/strong> that aspiring data science professionals have to master. Data visualization is incredibly important for businesses where the management heavily depends on their data science professionals to make data-driven decisions.<\/p>\n

The data captured by companies needs to be translated into an easily understandable format because people naturally understand information in forms of graphs and charts more readily compared to raw data. As a data science professional, you\u2019ve to be able to accomplish this goal using tools like D3.js, Tableau<\/strong><\/em><\/a> etc. Data visualization provides companies with the ability to directly work with data. They can quickly gain insights that would help them to consider business opportunities and increase profitability.<\/p>\n

2.7- Data wrangling<\/em><\/h4>\n

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Usually,<\/em> the data captured by businesses is messy and highly difficult to work with. Without having expertise on how to handle imperfections in that data, it\u2019s impossible for anyone to make use of it, and that\u2019s exactly why data wrangling is another essential of data science<\/strong>.<\/span> Some examples of these imperfections include inconsistent data formatting, inconsistent string formatting, and missing values. This\u2019ll be extremely important if you work in an organization where you\u2019re an initial data hire, or in a data-driven organization where the product isn\u2019t data-related because it has grown rapidly without giving much focus on data cleanliness.<\/p>\n

2.8- Business intuition<\/em><\/h4>\n

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In<\/em> general, the traits good data science professionals demonstrate are similar to that of a good problem solver.<\/span> They look at business problems from many perspectives to understand what they\u2019re supposed to do to solve them. At some point of the interview process, you\u2019re most likely to be asked about some complex problems. For example, you may be asked about a data-driven product that the company wants to develop, or a test it may want to run etc. It\u2019s crucial to have the ability to think about when do approximations make sense, what methods to be used etc.<\/p>\n

2.9- Business acumen<\/em><\/h4>\n

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As<\/em> a data science professional, you need to have a robust understanding of the relevant industry you\u2019re working in.<\/span> That\u2019s why business acumen is considered to be one of the essentials of data science<\/strong>. For example, you\u2019ve to be able to understand the problems that are crucial to solve for your company in order to accomplish its business goals, apart from identifying new business opportunities. To be able to successfully do this, you must understand how the business operates so that you can channel your efforts toward the right direction.<\/p>\n

2.10- Communication skills<\/em><\/h4>\n

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In<\/em> today\u2019s tech landscape, virtually nothing is performed in a vacuum \u2013 there\u2019re at least some integrations between teams, applications, systems etc. In the data science field, the ability of storytelling through data is immensely important.<\/span> Being at the crossroads of data, technology, and business, data science professionals have to be able to tell a story effectively to each of the decision makers.<\/p>\n

You should understand that presenting just a table of data isn\u2019t as effective as communicating the insights you\u2019ve derived from that data in a storytelling format. Most decision makers aren\u2019t interested in knowing what you\u2019ve analyzed. Instead, they\u2019re actually interested in knowing how the insights can positively impact their business.<\/p>\n

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

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If<\/em> you want to be a good data science professional,<\/strong><\/em><\/a> your first priority needs to be the focus on mastering the above essentials of data science<\/strong>. Remember that companies try to find someone who has the ability to develop useful models, and the intelligence to deal with data processing apart from knowing how the models work, understanding the business problems clearly, and more.<\/p>\n

Once you\u2019ve developed the above essentials of data science<\/strong>, your education shouldn\u2019t end there. Keep on learning new technologies and tools, as and when you can. This\u2019ll not only make you able to keep pace with industry trends, but will give your resume a boost as well. And whenever you\u2019re learning something new, apart from the classroom learning, you should keep on practicing as much as you can to strengthen your understanding of the concepts.<\/p>\n