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Data Analytics vs. Data Science: A Breakdown

While data analysts and data scientists both go with data, the main difference lies in what they do with it. Data analytics analyze large data sets to identify trends, develop charts, and create visual presentations to aid businesses to make more strategic decisions. Data science, on the other hand, design and build new methods for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Data Analytics

The responsibility of data analysts can differ across industries and companies, but fundamentally, data analysts ’ appropriate data to draw meaningful insights and explain problems.

They analyze well-defined sets of data using an arsenal of various tools to answer real business requirements: e.g., why sales dropped in a particular quarter, why a marketing campaign fared better in some regions, how internal attrition affects revenue, etc.

How to Prepare for SAS Advanced Analytics Professional (A00-226) Certification Exam?

Data analysts have a variety of fields and titles, including but not restricted to database analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to deliver quantitative decisions to non-technical colleagues or clients.

1. Typical Background

Data analysts can have a background in mathematics and statistics, or they can complete a non-quantitative education by learning the tools required to make decisions with numbers.

2. Skills and Tools

Top data analyst skills cover data mining/data warehouse, data modeling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis.

3. Roles and Responsibilities of Data Analysts

Data analysts are often responsible for creating and managing data systems and databases, using statistical tools to understand data sets, and preparing reports that effectively communicate trends, patterns, and predictions based on relevant findings.

Data Science

Data scientists, on the other hand, consider the unknown by asking questions, writing algorithms, and building statistical models. The main distinction between a data analyst and a data scientist is heavy coding.

Data scientists can arrange infinite sets of data using multiple tools at the same time, and build their automation systems and frameworks.

1. Typical Background

A data scientist as someone who has scientific and statistical information, hacking skills, and substantive expertise.

2. Skills and Tools

These involve machine learning, software development, Hadoop, Java, data mining/data warehouse, data analysis, python, and object-oriented programming.

3. Roles and Responsibilities of Data Scientists

Data scientists are typically tasked with producing data modeling processes, as well as building algorithms and predictive models to obtain the information required by an organization to solve complicated problems.

Choosing Between a Data Analytics and Data Science Career

Once you have a strong opinion of the differences between data analytics and data science and can identify what each career entails, you can start evaluating which path is the right fit for you.

To determine which way is best aligned with your personal and professional goals, you should consider three key factors:

  • Your educational and professional background
  • Your interests
  • Your desired career trajectory

1. Consider Your Background

While data analysts and data scientists are related in many methods, their differences are rooted in their professional and educational backgrounds, including the Master of Science in Computer Science and Master of Science in Data Scientist field.

Data analysts examine large data sets to recognize trends, develop charts, and create visual presentations to aid businesses to make more decisive decisions. To adjust their education with these tasks, analysts typically seek an undergraduate degree in a science, technology, engineering, or math major, and sometimes even an advanced degree. They also attempt out experience in math, science, programming, databases, modeling, and predictive analytics.

Data scientists, on the other hand, are more emphasis on designing and forming new processes for data modeling and production. Also, because they use a variety of techniques to comb through data, including data mining and machine learning, an advanced degree, such as a master’s or Ph.D., is essential for professional advancement.

Big Data-Related Job Roles

No matter which path you take, thinking through your current and desired amount of education and experience should aid you in narrowing down your options.

2. Recognize Your Interests in Data Analytics or Data Science

Do numbers and statistics move you, or do your passions continue into computer science and business?

Data analysts love numbers, statistics, and programming. As the guards for their organization’s data, they work almost exclusively in databases to uncover data points from multiple and often disparate sources. Data analysts should also have a thorough understanding of the industry they work in. If this seems like you, then a data analytics role may be the best professional access for your interests.

Data scientists are required to have a combination of math, statistics, and computer science, as well as an interest in and knowledge of the business world. If this information better aligns with your background and experience, perhaps a role as a data scientist is the right pick for you.

Either way, knowing which career matches your interests will help you get a better idea of the kind of work that you will enjoy and likely excel at. Be assured to take the time and think through this part of the comparison, as aligning your employment with your interests can go a long way in keeping you satisfied in your career for years to come.

3. Consider Your Desired Salary and Career Path

Different levels of experience are needed for data scientists and data analysts, resulting in varying degrees of compensation for these roles.

The average salary for a Data Analyst is $60,136, according to Salary Guide.

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Since these professionals serve mainly in databases, however, they can increase their payrolls by learning additional programming skills, such as R and Python. However, data analysts with more than ten years of experience often maximize their earning potential and move on to other jobs.

Data scientists, who have typically earned a graduate degree, boast an advanced skill set, and are often more experienced are considered more senior than data analysts. As such, they are often better paid for their work. According to the Salary Survey, The average salary for a Data Scientist, IT, is $88,779.

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The career trajectory for professionals in data science is accurate as well, with many possibilities for advancement to senior roles such as data architect or data engineer.

Data Analytics vs. Data Science: Which Is Right for You?

Data analysts and data scientists have deceptively similar job titles, given the many differences in role responsibilities, educational requirements, and career trajectory.

No matter how you look at it, that qualified individuals for data-focused careers are highly coveted in today’s job market, thanks to businesses’ intense need to make sense of and capitalize on their data.

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Once you have examined factors like your background, personal interests, and desired salary, you can choose which career is the right fit for you and get begun on your path to success.

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