There is a lot of overlap that exists between the fields of data science and data analytics, but there also are key differences to be aware of if you are considering a career in one of these fields. As the demand for professionals with data science expertise continues to rise across all industries, now is the best time to compare data science vs. data analytics.
What Is Data Science?
IBM defines data science as a broad discipline that focuses on specialized programming, machine learning, and advanced analytics to uncover trends and information within organizational data that can be used to create actionable insights that help the organization achieve its short-term and long-term goals. Data science is primarily used to enhance and improve the decision-making process within an organization, and it has become a key part of the strategic planning process for organizations of all sizes and across all industries.
What Is Data Analytics?
CompTIA defines data analytics as a discipline that uses statistical analysis and data analysis techniques to extract information from existing data. Data analytics professionals often harvest and evaluate data to create data visualization reports to help organizational leaders make strategic decisions that will help them reach their short-term and long-term goals. While data science offers a broader scope and focuses more on predictive modeling, data analytics focuses primarily on evaluating historical data to better understand current circumstances.
Key Differences Between Data Science and Data Analytics
Knowing the key differences between data science vs. data analytics can help you decide which qualifications to pursue as you determine your overall career goals.
These are the primary differences between data science and data analytics:
Scope and Focus
The scope and focus of both data science and data analytics vary significantly. While both fields focus on extracting data and gleaning actionable insights, the two fields focus on different types of data sets. For the most part, data science professionals are going to be focused on harvesting and analyzing current data to create predictive models for organizations to use during the decision-making process. On the other hand, data analysts will be working with historical data to glean insights that help them better understand the current circumstances.
Techniques and Tools
Data scientists and data analysts often use different techniques and tools to harvest data, analyze results, and create actionable insights for their organizations. Data scientists work primarily with statistical models, machine learning models, and algorithms, whereas data analysts use data visualization tools to contextualize historical data.
Output and Deliverables
The output and deliverables between these two fields also vary significantly. Data scientists are charged with analyzing current data to predict the future, so they generally focus on creating predictive models that showcase what might happen. On the other hand, data analysts create data visualization sets to help organizational leaders better understand the current circumstances based on historical data.
Skill Requirements
Both data scientists and data analysts will need to have a similar skill set, as they both have to be able to collect, analyze, and interpret data. However, the data science field is more heavily rooted in coding, requiring data scientists to be proficient in some leading programming languages, such as Python.
Problem-Solving Approach
Data scientists and data analysts often have to use a different problem-solving approach, largely because they are focused on two different types of data analysis. Data scientists are often looking to create models to predict what is to come, whereas data analysts are reviewing historical data. As such, their approach to problem-solving within their organizations will vary.
Overlapping Areas Between Data Science and Data Analytics
While there are distinct differences between the disciplines of data science and data analytics, there are overlapping areas. Both data scientists and data analysts work with large and complex data sets, and they must be familiar with advanced computer science technology, such as artificial intelligence, machine learning, and data analytics.
Career Opportunities in Data Science and Data Analytics
The Bureau of Labor Statistics (BLS) notes that the career opportunities for data scientists are on the rise, with the BLS anticipating a 36 percent increase in the number of data scientist jobs available between 2023 and 2033. This job growth rate is much faster than average when compared with all other occupations, highlighting the increased demand for professionals with data expertise across a wide range of fields and industries.
These are some of the career opportunities that you might consider if you choose to specialize in data science or data analytics:
Data Science Roles
Data science roles focus more on predictive modeling, which means that these professionals rely more heavily on machine learning and predictive analytics to complete their daily job responsibilities. Some of the leading data science roles include:
- Data Scientist — A data scientist is a professional who expertly uses analytical tools to extract meaningful insights from raw data that can be used within the decision-making process.
- Machine Learning Engineer — A machine learning engineer is a professional who works to develop new algorithms or improve existing algorithms to enable machine learning.
- Artificial Intelligence Specialist — An artificial intelligence specialist is a data science expert who works to develop and implement artificial intelligence technology.
- Data Engineer — A data engineer is a data science professional who oversees large data sets and specializes in database management.
- Computational Scientist — A computational scientist is a data science professional who leverages the power of advanced algorithms to create complex models and enact experiments that can aid in problem-solving within an organization.
Data Analytics Roles
These are some of the career opportunities that may be available to those who choose to specialize in data analytics:
- Data Analyst — Data analysts are business professionals who work to glean insights from data to identify opportunities to improve business operations within an organization.
- Business Analyst — Business analysts are experts in data analysis and are responsible for collecting and analyzing data to identify opportunities to increase profits and generate more revenue within an organization.
- Business Intelligence (BI) Analyst — Business intelligence analysts are similar to business analysts, though their position offers a broader scope. They often collect and analyze data to identify trends taking shape and provide organizational leaders with insights that can improve business decisions.
- Operations Analyst — Operations analysts are data analysts who focus primarily on collecting and analyzing data to identify opportunities to streamline operations, reduce costs, and improve efficiency within an organization.
- Marketing Analyst — A marketing analyst is a specialized professional who harvests and analyzes data to improve marketing efforts.
- Supply Chain Analyst — A supply chain analyst is a data analytics professional who analyzes data in hopes of gleaning insight into opportunities to improve the supply chain, reduce errors, and minimize inefficiencies within the system.
Educational Paths and Certifications
Depending on the career path you want to pursue, you will find different educational pathways and certification options to consider. Choosing the right degree program or certificate program can help you develop the specific skills and advanced knowledge required for your data-based career.
Data Science Education
If you are most interested in predictive modeling, you may want to consider a career in data science. A data science education will provide you with the knowledge and skills required for this field, allowing you to understand the scope of the field and your role within an organization. At Champlain College Online, we offer an Undergraduate Certificate in Data Science, a Graduate Certificate in Data Science, and an online Bachelor of Science in Computer Science degree, all of which allow you to explore the field of data science while developing relevant computer science skills.
Data Analytics Education
Suppose you have decided that you prefer to collect and analyze historical data to create relevant and contextualized data visualizations. In that case, you may want to consider a career in data analytics. Pursuing a specialized data analytics education can help you develop the advanced skills required to work in this field. At Champlain College Online, we offer an online Master of Science in Business Analytics degree program that allows you to develop the core competencies required to pursue a career as a business analyst.
Choosing the Right Path: Data Science or Data Analytics?
Ultimately, the choice between a career in data science or data analytics is yours. You will want to consider your strengths and interests in the field, your existing skill set, and your personal career goals before you determine which degree or certificate program is right for you.
Deciding Between Data Science vs. Data Analytics? Discover the Possibilities at Champlain College Online
Data-driven decision-making will continue to drive the business world in the years to come, which has increased the demand for professionals with data science expertise. At Champlain College Online, we offer an online Undergraduate Certificate in Data Science program that equips students with the technical skills and foundational knowledge required to specialize in this field. The curriculum for this program encompasses a wide range of relevant topics, including data visualization, statistical analysis, and coding languages, such as Python.
Request more information about our online degree and certificate programs today.
