You<\/em> may have already heard that data science<\/strong> is a fast-moving, exciting field that pays really well and a numerous number of aspiring candidates are trying to step into this field. All the hypes revolving around this field can trigger some common questions like \u201cWhat is data science<\/strong>?\u201d, \u201cWhat are the skills required to enter data science<\/strong>?\u201d etc. To help you establish context, here\u2019s our effort toward demystifying data science<\/strong>. The following discussion should help you make an informed decision.<\/p>\n

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

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The world<\/em> is becoming a digital space day-by-day and businesses across the globe are dealing with massive amounts of data every day. Businesses have started to realize that this massive amount of data is of no use until they become able to use it to their advantage. Data science<\/strong> is the field that offers a perfect blend of various high-end tools and technologies that help businesses discover patterns hidden in that data. So, you can consider data science<\/strong> as a field with the objective of making business predictions and decisions by analyzing massive amounts of data.<\/p>\n

The entire data science process can be distributed in the following stages:<\/p>\n

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• Data capture<\/li>\n
• Maintenance of data<\/li>\n
• Processing of data<\/li>\n
• Data analyzing<\/li>\n
• Communication of the findings<\/li>\n<\/ul>\n

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

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  When<\/em> it comes to demystifying data science<\/strong>, it\u2019s important to understand the key components of data science. Let\u2019s have a look.<\/p>\n

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  • Various types of raw datasets<\/li>\n
  • Computer programming language<\/li>\n
  • Statistics and probability<\/li>\n
  • Machine learning<\/li>\n
  • Big data<\/li>\n<\/ul>\n

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

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    Probably<\/em> you\u2019ve already heard that the job role of data scientist has been declared as the hottest one of the 21^{st<\/sup> century. These days, data scientists have become crucial assets for almost any organization. These professionals are data-driven individuals with robust technical skills and are capable of working with large amounts of information to derive actionable insights that help to build business strategies in their companies.<\/p>\n}

    4- Different roles in the data science field<\/em><\/strong><\/h3>\n

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    Over<\/em> the last decade, a variety of job positions associated with data science<\/strong> have emerged. Some of the most common ones include data scientist, data analyst, and data engineer. Let\u2019s have a quick look at the skills required to get into these positions to help you chalk out your future career path.<\/p>\n

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    • Data scientist: Strong mathematical and statistical skills, programming skills, data visualization, storytelling, among others.<\/li>\n
    • Data analyst: Mathematical and statistical skills, programming skills, data wrangling, data visualization, among others.<\/li>\n
    • Data engineer: Databases, programming languages, frameworks, among others.<\/li>\n<\/ul>\n

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

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      If<\/em> you\u2019re interested to step in the data science<\/strong> field, it\u2019s important to understand that to become a successful professional in this field, you\u2019d need to acquire a diverse skillset. This is an ever-evolving field and it\u2019s not possible to learn everything. But, if you can master the basics, you\u2019ll surely be in a position to learn anything as and when required. And that\u2019s probably the best way to succeed in fast-developing field like data science<\/strong>.<\/p>\n