By Cooper Nelson
If you are interested in becoming a data analyst or data scientist, it’s essential to understand the differences between these two related but distinct careers.
Data analytics refers to assessing information to find trends, patterns or other evidence that can help an organization solve a particular problem, increase operational efficiency, save money or reach some other goal. Analytics projects often require communicating findings to the decision-makers in a company or organization.
Data analysts often need to develop charts and other visuals that communicate and support their findings.
According to the U.S. Bureau of Labor Statistics (BLS), analytics is a growing field because companies use the findings of analysts to make informed strategic decisions, manage risks, develop budget forecasts and better define targets of marketing campaigns.
Data analysts try to find answers to questions in large data sets, but the approach can differ depending on the project.
Here’s a look at the four primary types of data analytics:
When applying for a job as a data analyst, it can be helpful to be proficient in all four types of analysis.
Organizations and businesses use data analytics for a wide range of purposes. Here are some of the most common applications for data analytics.
Those are some of the most common applications for data analytics. However, companies rely on data analysts for other purposes as well. If you work in the field of data analytics, your focus may vary depending on your employer’s needs and industry.
The specific duties of data analysts depend on the type of organization they work for and the extent to which the organization adopts data-driven strategies and decision-making. Key responsibilities of data analysts include:
Data analysts present insights that can affect the high-level decisions of a company. This role and the responsibilities require a solid educational foundation in technology as well as certain skills. If you’re interested in a career in data analytics, you can pursue a technology degree to gain the right knowledge.
BLS notes that because few schools offer degrees in data analytics, students may want to consider similar degree programs. You might also consider an education in a related field of specialization, such as cybersecurity. Earning a Bachelor of Science in Cybersecurity and then enrolling in a Master of Science in Cybersecurity can further enhance your knowledge in cybersecurity. With the right education, you’ll improve your ability to demonstrate deeper skills and specialized knowledge for applying data analytics in a targeted way.
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Data science involves the design and creation of data modeling techniques and processes. The goal of a data scientist is to collect and organize information into forms that are useful for analysis or other purposes.
The data science toolbox includes algorithms and mathematical models that analyze and interpret data sets automatically. The goal of data scientists is to use these tools to create data-driven solutions for businesses or organizations.
In other words, data scientists lay the groundwork for all the different types of data analysis and data usage within a company or organization.
Data science is a broad and developing field, and data scientists can focus on any of the following specialties:
Regardless of their specialty, data scientists need to develop knowledge of both mathematics and computer systems.
Data science has a wide range of applications, and data scientists can specialize in a variety of areas, depending on the needs of their employer. That said, data scientists always focus on facilitating data-driven activities for their employers or clients. Here are some common foci in the field of data science:
The duties of data scientists depend on their area of focus. Data scientists can work, for example, in the finance or medical industries or at academic institutions. As more companies adopt data-driven operations, these professionals are in high demand.
Daily duties of data scientists depend on their employer’s needs, industry and reliance on data analysis.
Here are some typical duties that data scientists may need to handle daily:
Data professionals should consider training that teaches them how to handle both the technical and analytical aspects of their job.
Specific degrees can help enhance your preparation for both data science and data analytics careers. You can start on your career path by enrolling in a Bachelor of Science in Data Science degree program, which focuses on data mining and modeling, data programming languages, statistical analysis and related subjects.
While most data scientist entry-level positions require only a bachelor’s degree, qualified candidates are in high demand. If leadership in technology is a career path that interests you, a Master of Information Systems may be beneficial.
Data science focuses on collecting and shaping raw data via modeling techniques and processes. Data analytics focuses on identifying patterns and trends that lead to problem-solving or predictive insights.
This answer depends on your interests. Do you like developing algorithms and models to manage or interpret data? Then data science is for you. Do you prefer evaluating data to answer questions or solve problems? That’s analytics.
According to the BLS, the national average salary range for data scientists and mathematical science occupations in May 2020 was $52,950 to $165,230.
Operations research analysts (or data analysts), meanwhile, earned between $48,050 and $144,330 in May 2020, according to BLS.
The salary ranges are not specific to students or graduates of University of Phoenix. Actual outcomes vary based on multiple factors, including prior work experience, geographic location and other factors specific to the individual. University of Phoenix does not guarantee employment, salary level or career advancement. BLS data is geographically based. Information for a specific state/city can be researched on the BLS website.
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