Data science vs Data Analytics: What they are and how to use them

A growing number of businesses are coming to recognize the value of data science and data analytics. Both disciplines can help enterprises draw insights from big data, but organizations should understand the scope and use cases for each. Let's take a look at each field and discover how they can be used separately and together to benefit your business.

What is data science?

Data science is the practice of analyzing data mathematically to discover patterns, insights, and relationships. It requires expertise in clustering techniques, data mining, and machine learning, among other areas. An enterprise can use data science to improve business outcomes, empower stakeholders to make data-driven decisions, and facilitate self-service access to data resources. For example, a financial services organization might use data science to create models to determine how to reduce churn.

What is data analytics?

Data analytics encompasses a variety of methods for manipulating raw data to obtain meaningful insights. Enterprises can use data analytics to find the answers to important business questions. For example, they might analyze the performance of individual members of a sales team to suggest which employee is best suited for a specific customer or sales project.

The difference between data science and data analytics

Data science involves identifying objectives for data, finding the data required to attain them, strategically manipulating data so it can be analyzed, and sifting through large datasets to discover new insights and make recommendations. Data analytics, says Stitch co-founder and Talend SVP Jake Stein, "is about iteratively asking questions. The answer to any given question is often viewed only once and used to inform the next question on our way answering a fundamental business question or solving a problem."

In other words, data scientists work with data to find new questions and unexpected insights, while data analysts try to obtain answers to questions an organization knows it has.

Data science and data analytics are closely related. Data scientists help orchestrate many of the steps leading to data analytics, as well as the various analytics techniques used to achieve business goals, and data analysts often rely on the work of data scientists to analyze data.

How to use data science and data analytics

Enterprises in almost any industry can benefit from data science and data analytics.

• Marketing: Organizations can use data analytics to enhance their marketing efforts by, for instance, discovering how to best target particular customer demographics. Data science is required to build a machine learning model that data analysts can employ, find the proper training data for it, and put the model in production.
• Sales: Sales managers need the ability to isolate the performance of individual locations or team members. Data analytics can deliver this granular insight across useful parameters such as sales type, location, product, and promotions. Data scientists can build solutions so sales prospects are dynamically routed to the sales team members who best meet the prospects' particular needs.
• Finance: Financial services organizations can use data analytics with machine learning techniques to better predict hedge fund or stock market performance. Data scientists can prepare relevant data for analysts to use — a task that includes regularly updating data models with overnight data about investments' recent performance or hedge fund opportunities.

Careers: data scientist and data analyst

Data scientists and data analysts perform fundamentally different jobs.

• Skills: Data analysts should be well-versed in statistics, math, spreadsheet tools, data visualizations, and programming. Data scientists must possess these skills and also be familiar with cognitive computing models, data architecture, big data platforms, and open source technologies. Data scientists and data analysts both benefit from relevant business knowledge and the social skills necessary to communicate with nontechnical stakeholders.
• Responsibilities: Data scientists prepare data for analytics or applications, create analytics models, integrate data, and find new opportunities for using data to drive business outcomes. Data analysts are responsible for obtaining insights from data through analysis. They write queries in code, use statistical methods, and devise and implement metrics for data related to business goals.
• Salary: Data scientists are in greater demand than data analysts, and they typically earn six-figure salaries. Analysts often start out in the mid-five-figure range, but they can obtain six-figure salaries with experience.

Data science and data analytics unite in the data warehouse

Despite their different responsibilities, data scientists and data analysts both benefit from using a data warehouse. Data warehouses provide a centralized repository for information and allow data scientists and data analysts to easily access the information they need. Data scientists might require data from the warehouse to build predictive models for fraud detection solutions, while data analysts might use the information in the data warehouse to determine trends that could impact the success of future marketing campaigns.

Stitch provides a simple data pipeline for replicating data from external sources to all of the major cloud data warehouses. Learn more about Stitch's capabilities and how a data pipeline and a data warehouse can help both data scientists and data analysts by trying Stitch today.