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Data Science vs Data Analytics
Data science and data analytics can often be confused as the same field. In fact, many of us see professionals who collect, consolidate or analyze data as data professionals whose roles are the same.
However, expectations regarding frameworks and tools used in applications, the skills or the training needed may differ significantly between data science and data analytics roles. Professionals in both fields help their organizations gather insights through data with variations in pathways, methodology and business impact.
Data Science vs Analytics: Pathways
The educational and professional pathways for data science and data analytics have notable differences. Professionals have a wide range of options in the types of degrees and data-focused occupations they pursue, even if both fields require similar technical and problem-solving skills.
- Data science: Career paths that data science professionals typically pursue include data scientist, research scientist, machine learning engineer, data architect, data science consultant, AI engineer and more.
- Data analytics: The types of career paths that data analytics professionals can pursue with their degree typically include data analyst, market analyst, financial analyst, business intelligence analyst and more.
Wherever your tech career takes you, the online Master of Applied Data Science program will prepare you for a wide range of data-focused roles.
Data Science vs Analytics: Methodology
Another difference between data science and data analytics is the methodology and techniques each field relies on.
- Data science: Data scientists are tasked with uncovering hidden patterns within a collection of data sets. To do so, they often develop predictive models to solve complex problems, in addition to participating in data collection and data management processes. They also use techniques and methods from supervised and unsupervised learning, data mining and advanced statistical analysis to predict and understand future outcomes.
- Data analytics: Often the job of data analytics professionals is to provide insights based on past performance rather than predicting future outcomes. Data analysts perform summary statistical analyses, data cleaning and preparation and data visualization to interpret and present findings.
Data Science vs Analytics: Business Impact
One surprising difference between data science and data analytics is the impact each field may have on a given industry or particular business. This is because each of these two professions has a different end goal, even though both roles offer organizations the type of insight they may need to make data-informed decisions.
- Data science: Data scientists may construct processes and provide insights that can shape long-term decision making, while also developing frameworks and methods for a specific sector. This can include pricing strategy, budgeting for research and development or even timing the introduction of new products into markets.
- Data analytics: Organizations may rely on their data analytics team to gather insights that address specific business challenges. Examples include streamlining existing supply chains, understanding changes in quality control or adjusting prices in response to market shifts.