The world is generating increasingly-massive amounts of data. In order to stay competitive in this data-driven economy, a business has to focus on optimizing data usage. They’re employing data science and data analytics to best use that data.
Data science is a broad field that includes data analytics, data engineering, and machine learning. Data science and data analytics both involve working with data to gain insights. The difference is how the data is used in each.
Data scientists design and build processes for data modeling and production. They use prototypes, algorithms, predictive models, and custom analysis.
Data analysts examine large datasets to identify trends, then present those learnings to help businesses make more strategic decisions.
Data science is “a broad term that refers to a multidisciplinary field which has the goal of learning new insights from real-world data through the application of statistical and computational techniques. That includes methods such as data gathering profiling, wrangling, modeling, and interpretation.”
The process of data science is all-encompassing and starts with defining project goals based on business needs. It continues through the technical processes, like collecting, cleaning, and analyzing data. Then, the data scientist develops analytical models and algorithms using programming languages, machine learning, statistical modeling, or other methods to run the data through. At the end of the process, they share the insights using data visualization and dashboards.
What does a data scientist do?
Data scientists use tools and processes like algorithms and statistics to combine, prepare, and analyze large datasets. Some common job functions and responsibilities of data scientists include:
Working with business stakeholders to define goals for analysis and develop data governance policies
Determining data collection and storing processes, data integration systems, and data repositories
Using BI or data analytics tools to explore large data sets
Transforming raw data into clean information
Building analytical models and algorithms, then iterate on them through testing
What is data analytics?
Data analytics includes examining datasets to extract value and find meaningful insights, then use that information to solve problems and answer questions. Business use cases of data analytics can include determining actionable insights regarding sales, marketing, and product development or examining why a campaign failed to meet its goals. Data analytics can be used to answer larger, organizational questions like why the rate of employee turnover is high.
What does a data analyst do?
Data analysts need mathematical, technical, and interpersonal skills, as they have to communicate their findings to business teams. Technical skills they may need include data mining, data modeling, programming languages, statistical analysis, database management, and data analysis. Some common job functions and responsibilities for data analysts include:
Designing, maintaining, and improving data integration systems and processes
Use data analytics, statistical analysis, or BI tools to build apps, interpret data, and find insights
Prepare dashboards and reports for business stakeholders to communicate predictions, patterns, and trends
Key differences between data science and data analytics
Data science deals more with the future, while data analytics deals more with the past. For example, data scientists build models that can predict future outcomes, while data analysts analyze past data to gain insights that inform decisions. Additional differences between these include their purpose, scope and skills, and approach.
Real life examples of data science and data analytics
We can turn data into actionable insights to help our people make smarter decisions and build stronger connections with customers.
Rocío Rufilanchas
Global Chief People Officer, astara
astara
In order to create better customer experiences, astara needed to understand their customers' opinions and preferences better. They decided to use customer data to inform business decisions. To improve the decision-making process, astara used data tools, including Tableau, to visualize mobility and vehicle ownership models trends.Stronger customer connections helped automotive company astara
increase turnover by 300% in six years.
NBCUniversal
NBCUniversal
used a data analytics and visualization tool to analyze data across multiple business systems. They were also able to increase data access with Tableau Analytics, making insights more accessible to employees. With data-driven personalization, they’re able to deepen their relationship with fans during the Olympics, “NBCUniversal gathers data across multiple touchpoints to customize digital experiences for Team USA fans. This way, fans can more easily follow their favorite events and athletes and consume content in their channels of choice.”
National Aeronautics and Space Administration (NASA)
NASA
launched the STEM Gateway internship management platform, an online community portal. It includes modules and apps used to automate and streamline outreach, engagement, and recruitment of the next generation of STEM leadership.
“Integrated reports and dashboards built using Tableau and CRM Analytics
give the team a set of business analytics tools to roll up results, spot trends, and pinpoint catalysts driving the internship program and other NASA STEM engagements.” NASA uses that information to learn what demographics are missing from the talent pool and trace those gaps back to barriers in the application process and create reports.
Combine data science with data analytics
Data science teams and data analytics teams work together frequently. You’ve seen how using both data science and data analytics can help organizations succeed and solve problems more quickly and efficiently. For the best results, employ multiple types of data science in the same project, including data analytics.
Both teams work together to define a problem. Data analytics teams may help frame the problem based on historical data while data scientists may suggest approaches When handling data, the responsibility of the data analyst may include cleaning and processing it. Once the data is ready, the data scientist can use it for model building. The models and algorithms built by the data scientist may be tested, interpreted, summarized, and shared by the data analyst.
The power of data in digital transformations
Organizations who undergo digital transformations will find value in working with data science and analytics teams. Juniper Networks, for example, made the most of their data potential during a multi-year, multi-phase journey when they moved to Tableau Cloud.
They've focused on “the digitization of assets and the use of data analytics, machine learning, and artificial intelligence to automate processes, increase productivity, and reduce expenses, all while keeping data secure.” This has helped them:
Eliminate 2,000 hours of lost user productivity per month
Save over $250,000 through license monitoring and governance
Improve dashboard performance by 50%
Reduce time spent on upgrades and maintenance by 84%
Increase efficiency with a 90% decline in IT support requests
As the organization grew in terms of data, requirements also grew, got expensive and we needed a platform that grew with our needs that would scale.
Suman Shanthakumar
Senior Data Warehouse Architect, Juniper Networks
Data science and data analytics resources
Data science as a field is incredibly broad and nuanced. It’s always evolving, therefore there will always be more to uncover. Want to learn more about data science and data analytics? Check out our other resources to get more information on how to make the most of your data.
For detailed explanations on specific types of charts, graphs, and more, visit Tableau’s reference library of visual analytics. Discover the Data Scientist Path to understand how to derive valuable insights for large and varied data sets. Follow the Analyst Path to learn how data analysts are responsible for supporting their organization’s lines of business and delivering valuable insights from data. Or start your data literacy journey with our free e-learning offers.
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