If<\/em> you\u2019re trying to step into the data science field and have gone through any job portal, most likely you\u2019ve observed that both data scientist<\/strong> and data analyst<\/strong> job positions are in high demand with impressive salaries. While both these positions share some similarities, there\u2019re significant differences in terms of basic requirements understanding which is necessary to select a path to follow. In this post, we\u2019ve outlined the fundamental requirements for both data scientist<\/strong> and data analyst<\/strong> positions to help you make an informed decision.<\/p>\n

1- Data scientist<\/em><\/strong><\/h3>\n

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In<\/em> general, data scientists<\/strong> design and develop new processes for data production and modeling. Apart from interpreting and performing product experiments and data studies, these people are tasked with developing predictive models, prototypes, algorithms, and custom analysis. They also need to be able to work with a wide range of techniques to deal with data including machine learning and data mining. To be able to perform all these, holding advanced degrees like a master\u2019s or Ph.D. is crucial to becoming a successful data scientist<\/strong>.<\/p>\n

2- Data analyst<\/em><\/strong><\/h3>\n

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Data analysts<\/strong><\/em> sift through massive amounts of data and generate reports and visualizations that explain the insights on what the data is hiding to help companies make strategic business decisions. In general, data analysts<\/strong> come with an undergraduate degree in science, technology, engineering, or math major, and sometimes hold an advanced degree. These professionals also have experience in science, math, programming, databases, predictive analytics, and modeling.<\/p>\n

3- Skills required to become a data scientist or a data analyst<\/em><\/strong><\/h3>\n

3.1- Data scientist<\/em><\/strong><\/h4>\n

If<\/em> you want to become a data scientist<\/strong>, you should focus on developing the following qualifications:<\/p>\n

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• Master\u2019s or Ph.D. in mathematics, statistics, or computer science<\/li>\n
• Ability to work with statistical computer languages like Python, R, SQL etc<\/li>\n
• Ability to develop and work with data architectures<\/li>\n
• Knowledge of machine learning techniques<\/li>\n
• Knowledge of advanced statistical concepts and techniques<\/li>\n
• Ability to work with web services<\/li>\n
• Ability to present\/visualize data for stakeholders<\/li>\n
• Strong verbal and written communication skills, and more<\/li>\n<\/ul>\n

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  3.2- Data analyst<\/em><\/strong><\/h4>\n

  To<\/em> become a successful data analyst<\/strong>, you should focus on the following:<\/p>\n

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  • Degree in statistics, mathematics, or business<\/li>\n
  • Ability to work with languages like Python, R, SQL etc<\/li>\n
  • A solid combination of intellectual curiosity, analytical skills, and reporting acumen<\/li>\n
  • A strong understanding of various emerging technologies<\/li>\n
  • Ability to work in agile development methodology<\/li>\n
  • Exceptional common on Office and Excel, and more<\/li>\n<\/ul>\n

    4- Data scientist vs. data analyst<\/em><\/strong><\/h3>\n

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    The<\/em> skills required to be either of a data scientist<\/strong> or a data analyst<\/strong> overlap in some areas. For instance, both the positions require some fundamental know-how of mathematics, knowledge of software engineering, understanding of algorithms, and good communication skills. The major difference is that a data scientist<\/strong> comes with all the skills of a data analyst<\/strong> together with solid business acumen and the ability to clearly communicate their findings in comprehensible formats to business stakeholders and IT leaders in such a manner that in can influence the way a business approaches a challenge.<\/p>\n