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The<\/em> increasing buzz around data science<\/em><\/strong><\/a> has already attracted a lot of computing enthusiasts and the demand for data science professionals seems to go up with each passing day. In addition, the gap between the availability of skilled professionals and the demand is quite substantial, which makes it even more of an exciting career path.<\/strong><\/em><\/a><\/p>\n In<\/em> this scenario, if you are planning to step into the field of data science, what steps should you follow for business<\/em><\/a> or career<\/em><\/a><\/strong>? Do you actually need to have a university degree to get a decent job in this field? In this post, we\u2019ve tried to identify whether a university degree is an absolute necessity or not.<\/p>\n But before we begin, here\u2019re some questions that you must ask yourself prior to proceeding ahead.<\/p>\n If your answer to all these is \u201cYes\u201d, then you\u2019re on the right track.<\/p>\n First of all, the simplest explanation of what data science entails is the process of accumulating, analyzing, and interpreting data<\/em> \u2013 all with the help of technology. So, what will you be actually doing as a data science professional?<\/span><\/p>\n <\/p>\n You\u2019ll<\/em> probably be solving different problems by implementing machine learning<\/a><\/strong><\/em>\u00a0to big data. Here\u2019re some examples:<\/p>\n You<\/em> will get to experience different kinds of problems and to be proficient, you need to have a solid foundation in statistics, math, and coding, apart from continuous learning and adapting. Let\u2019s have a look at these skills in a little detail.<\/span><\/p>\n <\/p>\n Statistics<\/em><\/strong><\/a> is the part of data science<\/strong> that deals with analyzing and interpreting data<\/em>. Having a robust understanding of the methods used in statistics will help you in measuring probability, which refers to the likelihood of events. These phenomena<\/strong><\/em><\/a> help interpreting the future technology (like AI, machine<\/strong> learning<\/strong><\/em><\/a>\u2026)<\/p>\n <\/p>\n Core<\/em> mathematical foundation acts as a base for learning other important skills needed to become a data science professional<\/em><\/strong><\/a>. You should be comfortable in dealing with mathematical concepts. These concepts;<\/p>\n If you\u2019ve a practical bent of mind, you may find math more enjoyable in the context of data science<\/em> where the data represents real-world concepts and spring board data science review<\/strong><\/em><\/a>.<\/p>\n <\/p>\n You<\/em> can only comprehend a well-categorized chunk of data if you know the language in which data communicate.<\/strong> A good coder may not be a data science professional<\/em>, but a data science professional<\/em> is surely a good coder.<\/span> You can have an excellent future when you attend the data science in 6 weeks<\/strong><\/em><\/a> with well known professional lecturer in Silicon Valley.<\/strong><\/em><\/a><\/p>\n Apart<\/em> from the above, there\u2019re some obvious skills like thorough knowledge of database, data munging (popularly called data wrangling<\/strong>), machine learning<\/strong> etc, which are needed to become a data science professional.<\/strong> While getting a university degree in data science<\/em> is a fast and sure way to step into the field, there\u2019re some institutions and companies that offer online courses on both data science specializations and fundamentals(such as Magnimind Academy<\/strong><\/em><\/a>). When it comes to cracking a job interview in data science<\/em>, you may find that some companies clearly mention that they\u2019ll only encourage candidates with university degrees. However, a lot of companies<\/strong> prefer to hire candidates who have adequate knowledge in the field even when they don\u2019t have a university degree.<\/span><\/p>\n .\u00a0 .\u00a0 .<\/p>\n\n
Probable job responsibilities<\/strong><\/h3>\n
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Key skills<\/strong><\/h3>\n
1-<\/strong> Statistics<\/strong><\/em><\/h4>\n
2-<\/strong> Mathematics<\/strong><\/em><\/h4>\n
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3-<\/strong> Coding in Bootcamp<\/strong><\/em><\/h4>\n
Conclusion<\/strong><\/h3>\n