Both Data Analysts and Data Scientists work with data. The difference between these two related fields is what is done with this data.

Data Science

Data science is a multidisciplinary field that is more concerned with the questions that need to be asked to find possible avenues of study rather than the actual answers to these questions. Those with a background in data science perform open-ended research and modeling. This field provides the scaffolding for the various analyses an organization will then perform.

The day-to-day tasks of a Data Scientist typically involve data wrangling, which is the act of cleaning and organizing data to make it more easily used. In addition, Data Scientists work with statistical modeling to uncover relationships between variables. They also tend to have a solid background with computer programming languages like SQL, Python, and R, which allow them to perform efficient analysis of large datasets.

Data Analytics

While data science involves posing questions about data, Data analytics is primarily focused on finding answers to those questions. This involves devising methods to process, clean, and organize structured data so that statistical analyses can be run and key insights can be found. By finding answers to these questions, a Data Analyst is able to produce actionable insights that can yield immediate improvements. This process involves using a combination of computer programming, statistical analysis, and data visualization software like Tableau.

The daily tasks of a Data Analyst differ from those of a Data Scientist. Data Analysts often work with the leaders of organizations or companies to pinpoint their informational needs. They then retrieve data from both primary and secondary sources before cleaning and organizing it. By using programming tools like Python, R, and SQL, Data Analysts then analyze the data, hoping to locate patterns or trends. Once analysis is complete, the findings can then be translated into data visualizations, which present the insights in a visually engaging storyline that can be understood by a non-technical audience.

Which is the Career for You?

For those who are deciding between a career in data analytics and data science, there are several important factors to consider. Educational background is one contributing factor. Since Data Analysts are typically concerned with examining large sets of data to spot trends, create visualizations, and offer strategic business decisions, they often have an undergraduate degree in fields like engineering, math, or science, as well as an advanced degree in an analytics-related field. On the other hand, Data Scientists who are tasked with creating new processes for data modeling and production tend to have a more technical background, which often includes training in computer science and math.

Although job prospects in these two professions overlap, different professional tracks are available for students of data science versus those with a background in data analytics. Both career paths draw from advanced degrees: a Master’s in Business Data Analytics and a Master of Science in Data Science. Those with a background in data science often pursue a career as a Database Developer, Machine Learning Engineer, Business Intelligence Developer, or Data Engineer. For those who studied data analytics, career options such as Quantitative Analyst, Data Analytics Consultant, Marketing Analyst, and IT Systems Analyst are commonly pursued.

Another consideration for those who are trying to decide between pursuing a job in data analytics versus data science is the salary in each profession. Pay rates are high in both fields for those with training. Data Science and Data Analytics were listed by the World Economic Forum Future of Jobs Report 2020 as two of the most highest-paying and most in-demand professions in any industry.

In 2020, Data Analysts made between $83K and $143K. Data Analysts who learn additional programming skills like Python and R can increase their expected salary. The U.S. Bureau of Labor Statistics (BLS) estimates that there will be a 27% increase in available Data Analyst jobs by 2026.

Data Scientists who often have more advanced training and skills, as well as a graduate degree, are often compensated for their work with higher salaries, which fall between $105K and $181K, with a mean salary of $114K. Data Scientists also have many opportunities for career advancement into senior roles like Data Engineer or Data Architect. According to the BLS, data science jobs are experiencing a growth trend that will result in a 19% increase in professional openings by 2026.

In Conclusion

While there are important differences between data analytics and data science, the good news is that there’s no need to choose between these fields unless you wish to pursue a specific career path. Those with a passion for gathering, organizing, analyzing, and reflecting on datasets have many career options available, and can also change professional focus with training and continued education. For example, Data Analysts can transition into Data Scientist roles by studying topics like statistics, artificial intelligence, and data management. Most business professionals who leverage data for their organizations rely on key concepts, techniques, and skills from both data analytics and data science.

Both data analytics and data science continue to play an important role in shaping the future of how humans store, organize, retrieve, and make sense of big data. The demand for qualified professionals in both fields continues to grow, and likely will keep increasing as more raw data accumulates. In the coming years, data analytics is expected to drastically change the way business is conducted and decisions are made. In addition, data science roles are likely to become more specialized as companies, businesses, and organizations rely more heavily on new technologies such as AI for computing data. As more efficient and effective methods for turning data into insight are perfected, both data science and data analytics will continue to shape the way we store data, work with this information, and ultimately use it to improve our lives.

Start Learning Data Analytics with Hands-On Classes

For those looking to learn more about data analytics or data science, Noble Desktop has you covered. Noble’s Data Analytics courses and Data Science classes are available for interested learners. In addition, you can locate other Data Analytics and Data Science courses in your area using Noble Desktop’s Classes Near Me tool. These include courses in coding languages like Python, SQL, and R along with software like Power BI, Tableau, and Excel. If you’re new to working with data, you will also have the chance to learn foundational techniques for analysis and visualization.

If you are passionate about working with data and are looking for an intensive educational experience, bootcamps in Data Analytics or Data Science are a great option. These rigorous courses are taught by industry experts and offer hands-on training for participants who want to sharpen their data analytics, data visualizations, data science, or Python skills. Enrolling in a bootcamp is a great first step toward a high-paying career working with big data.