<\/p>\n

Data<\/em> scientists<\/strong> earn substantially more money than data analysts<\/strong>. On an average, the starting base salary of a data scientist<\/strong> is around $110,000 while for a data analyst<\/strong>, it stays around $65,000.<\/span>\u00a0 However, the salary of the latter depends on the type of the analyst they\u2019re \u2013 market research analyst, financial analyst, or operations analyst, among others. Learning data science<\/strong> <\/em><\/a>is the first step for these jobs.<\/p>\n

5- Difference by job roles<\/em><\/strong><\/h3>\n

<\/p>\n

Both<\/em> the groups are divided further based on their job roles.<\/p>\n

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• Data scientists<\/strong> are offered job roles like data developers, data researchers, data business and data creative people<\/span><\/li>\n
• Data analysts<\/strong> are categorized into roles like database administrators, data architects, operations and analytics engineers<\/li>\n<\/ul>\n

  Which career is best for you \u2013 Data scientist or data analyst?<\/em><\/strong><\/h3>\n

  <\/p>\n

  When<\/em> you need to determine whether a data scientist<\/strong> or a data analyst<\/strong> career path<\/em> would be the best for you, how will you proceed? We\u2019ve already talked about the skills that are required to excel in both the positions, but there\u2019re some other key factors that you should consider when choosing one of these two. These include your personal interests, your preferred career path, and your background. When you select the ones, you may know data science in 6 weeks.<\/strong><\/a><\/em><\/p>\n

  1- Personal interests matter a lot<\/em><\/strong><\/h4>\n

  <\/p>\n

  Do<\/em> you have a keen interest in statistics and numbers? Or, is it computer science and business that keep you excited?<\/p>\n

  While a data scientist<\/strong> needs to have solid understanding of computer science, statistics, and mathematics, he\/she also needs to have good business acumen. Apart from having robust presentation and communication skills, you need to be able to find opportunities, risks and trends in the data if your aim is to become a data scientist<\/strong>. In addition, communicating the findings in easy-to-understand formats should be one your key fortes.<\/p>\n

  On the other hand, work of a data analyst<\/strong> heavily encompasses programming, statistics, and numbers. They almost exclusively work in databases to reveal data points from complicated and sometimes, disparate sources. Also, a robust understanding of the industry they\u2019re working in is something crucial for a data analyst<\/strong>.<\/p>\n

  2- Career path holds its fair share too<\/em><\/strong><\/h4>\n

  <\/p>\n

  Where<\/em> do you want to see yourself in the distant future? Apart from job responsibilities, as the level of values added by data scientist<\/strong>s and data analyst<\/strong>s differ significantly, so do their compensations.<\/p>\n

  Data scientists<\/strong>, who\u2019re typically graduate degree holders, usually have advanced skillsets and come with more working experience. They are generally considered to be more senior that data analysts<\/strong>. As a result, data scientists<\/strong> receive healthier pay packets than data analysis professionals. And they can earn a yearly compensation between $110,000 and $163,500.<\/span><\/p>\n

  This compensation range comes down to $77,500 and $118,750 for data analysts<\/strong>. However, as their work encompasses databases mainly, they can increase their seniority and in turn, compensation by learning programming skills that are considered crucial in the domain. Once a data analyst<\/strong> gains substantial experience and acquires an advanced degree, he\/she can easily move into better positions with increased compensations.<\/p>\n

  3- Don\u2019t ignore your background<\/em><\/strong><\/h4>\n

  <\/p>\n

  Though<\/em> the positions of data scientists<\/strong> and data analysts<\/strong> may look somewhat similar, it\u2019s the background, in terms of both educational and professional, that acts as one of the key factors when it comes to choosing one of them.<\/p>\n

  For a data scientist<\/strong>, a PhD or Master\u2019s degree in mathematics, computer science, or statistics is desired. Add to it the desired professional experiences like working in statistical computer languages, working with data mining and statistical techniques, creating and working with data architectures, 5 to 7 years of experience in building statistical models and manipulating datasets, experience in using web services, and experience in working with distributed computing\/data tools, among others.<\/p>\n

  At their core, most data analysts<\/strong> require a degree in statistics, mathematics, or business with an analytical bend of mind. Desired experiences usually include working with languages like Python, R etc, and working in agile development methodology etc.<\/p>\n

  Both the positions of data scientist<\/strong> and data analyst<\/strong> are considered highly coveted in today\u2019s job landscape.<\/span> You can certainly go for either one. Just be sure to consider the above factors to excel in your chosen trajectory.<\/p>\n

  Where the roles intersect<\/em><\/strong><\/h3>\n

  <\/p>\n

  Though<\/em> data scientists<\/strong> and data analysts<\/strong> aren\u2019t two interchangeable roles, they hold a fundamental overlapping point \u2013 both of them draw insights from data. In the business acumen context, data scientists<\/strong> hold a richer skillset and have a deeper familiarity with advanced statistical modeling, Hadoop, machine learning than their counterparts in the data analysis domain. However, both professionals are capable of transforming data into insightful answers needed by business owners to take informed and better decisions. But the difference lies in their approaches and in the answers. Typically, a data scientist<\/strong> can help a business by formulating new questions that help it drive forward while a data analyst<\/strong> is able to answer critical business questions.<\/p>\n

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

  <\/p>\n

  Today,<\/em> there\u2019re lots of ways to become a data science professional, but the ideal move should be solidifying your educational background first, in terms of obtaining a Master\u2019s or Bachelor\u2019s degree. And then, there\u2019re other ways that can help you sharpen your data science skills. Ideally, before you dive into a higher-education program, you should try to figure out the industry you\u2019ll be working in to identify the most critical software, skills, and tools.<\/p>\n

  Whether you\u2019ll be working as a data scientist<\/strong> or a data analyst<\/strong>, some business domain expertise will be required that will vary based on the industry. For instance, if you\u2019re working in marketing, education, or business, you\u2019ll require a different skillset than if you work in science, healthcare, or government. Once you\u2019ve chalked out your desired industry needs, just do some research and you\u2019ll find an array of professional development courses, bootcamps<\/em><\/strong><\/a> and online classes that can help you learn and hone the requisite skills. Apart from these, there\u2019re data science certifications available as well that can strengthen your resume and in turn, help you get a healthier pay packet. Data science bootcamp\u00a0in Bay\u00a0Area <\/strong><\/a>might provide these options.<\/p>\n