Data science is the process of extracting actionable insights from large amounts of data using tools like the scientific method, statistics, analytics, programming, and machine learning. The goal is to see patterns in the data that might be missed at a glance, pull useful information from that data, generate predictive insights, and use that information to increase business intelligence (BI) and make better business decisions.
Data scientist vs. data analyst vs. data engineer
Data science is a broad field with many players. You may hear these three terms used interchangeably to describe the role data science professionals take on in an organization, but they can actually represent different skill sets and requirements.
A data scientist focuses on questions that need to be answered in order to solve business problems and where the data needed to answer those questions can be found. They are responsible for sourcing, managing, and analyzing high volumes of unstructured data, so they must have the expertise to mine, clean, and present data as well. They communicate their results with decision makers so they can apply insights to their business strategy. They use machine learning to create models for predictive analytics.
Data analysts can share many of the same responsibilities as data scientists, but usually, they don’t have a background in programming and aren’t responsible for much of the statistical and predictive modeling and machine learning elements of data science. While data scientists determine what questions need to be answered on their own, data analysts are typically given questions to answer by business leaders.
A data engineer focuses more on data architecture, infrastructure, and flow, than on statistics, modeling, and analytics. They are responsible for developing, deploying, managing, and optimizing data pipelines so that data scientists and data analysts can query the data. They need strong programming skills so that they can design databases, oversee data warehousing, and set up data lakes.
Data science and business intelligence
Data science and business intelligence both help organizations make data-driven decisions, but they have some subtle differences. Business intelligence looks at past data to determine trends. Data science can model and predict future outcomes. You could say that while BI looks at the past and present, data science focuses more on the present and the future.
Why is data science important?
Data science enables and encourages organizations to make better decisions. By following the data science process, you can find the cause of a problem, perform studies on your data to understand the problem, model the data using algorithms to test potential solutions, and communicate your results with descriptive and easy-to-understand visuals like graphs and dashboards.
What you can do with data science
Detect anomalies like alerting to fraud
Classify everything from emails to inventory
Give recommendations based on past behavior to customers and employees
Share actionable insights through visualizations, reports, and dashboards
Automate common processes
Score and rank items
Enable recognition for faces, audio, videos, images, and text
Optimize content and processes to manage risks and increase rewards
Segment products or clientele
How does data science work?
Because data science is such a large field that deals with a variety of tasks, it can be difficult to narrow down exactly how each question is answered. Generally, the data science process, also known as the data science lifecycle, involves these steps:
Data scientists gather raw structured and unstructured data using many different methods from all the relevant sources available. Tasks include:
Data scientists examine the data to find patterns, ranges, and distributions of values and to check for biases. All of this information informs whether or not the data is suitable for predictive analytics, machine learning, and other analytical methodologies. Tasks include:
Clustering and classification
Data scientists perform functions to extract insights from the data. Tasks include:
Data scientists present their findings in data visualizations like reports and charts that make insights easy to understand. They help decision makers understand how findings will impact their business. Tasks include:
Domo’s data science tools allow data science experts and business users alike to prepare data and create predictive models. Beginners can use drag-and-drop functions built into the extract, transform, load (ETL) process, including classification, clustering, forecasting, and predictions. Experts can combine the power and convenience of the Domo ETL process with the precision of data science with embedded R and Python scripting tiles. And, Domo users can take advantage of Domo’s automated machine learning solution, powered by Amazon SageMaker, to rapidly determine the best machine learning model for their data and then share those insights with their teams.
How do different industries use data science?
Every organization across industries can benefit from the insights and opportunities that data science brings. Data science helps make processes more efficient and helps improve the customer experience. Here are a few examples:
The airline industry can use data science to predict travel disruptions. This helps make the experience better for employees and passengers. With data science insights, decision makers can schedule flights more efficiently, forecast flight delays, and personalize promotional offers.
Police departments can use data science to create statistical incident analysis tools. These tools help officers know when and where to deploy crucial resources.
Driverless car developers can use data science for real-time object detection.
Organizations in the healthcare industry can use data science to improve medical tools and detect and cure diseases.
Streaming services use data science to offer recommendations to viewers.
Financial institutions can use data science to detect fraud.
Shipping companies can use data science to create better routes and increase efficiency.
How will data science evolve in the future?
In the future, automated machine learning will be utilized more broadly to help enterprises achieve outcomes and understand the variants that drove impact. Data integration combined with domain knowledge tools will create even more opportunities to automate business processes.
Additionally, productionizing data science will become easier for business users and analysts, requiring less core computer science, advanced statistics, and linear algebra skills. Tools for data scientists will expand, but more solutions for citizen data scientists will encompass end-to-end workflows to accelerate the data life cycle.
New perspectives on artificial intelligence and machine learning
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