What is data science and how is it used?

Today’s organizations can deal with large amounts of information from multiple sources. What do they do with it all? Data science can help them turn data into practical strategies that support their business goals.

Data science combines statistics, computer science, programming and specialized knowledge to solve problems.

The process starts with collecting and processing data before analyzing it for insights germane to organizational challenges. This analysis essentially turns raw data into clear actions that ideally support business goals.

Those who work with data may handle it in such forms as text, audio, video and images. Some companies handle data from online systems, payment tools and devices that automatically collect information, such as e-commerce records, medical files and social media posts.

The field draws upon several disciplines. Mathematics and statistics are used to interpret data and test hypotheses about patterns. Computer science skills are deployed to develop queries and systems for managing large datasets. Industry expertise, such as healthcare, finance, retail or manufacturing knowledge, is essential for contextualizing findings and aligning them with business goals.

A primary focus of the field is to clearly explain or illustrate complex ideas. Professionals in this role turn technical results into presentations that broad audiences can understand and that stakeholders and leaders can use for business decisions. Visual tools like charts and graphs help people quickly see important points, making data easier for leaders and teams to use. 

Data science follows a process to get value from data. This methodology describes what’s happening, finds out why things happen, predicts what might happen next or suggests the best actions to take.

The process is called OSEMN, which stands for:

• O: Obtaining data
• S: Scrubbing data
• E: Exploring data
• M: Modeling data
• N: Interpreting results

Project activities may include:

• Collecting information from multiple internal and external sources
• Streamlining, cleaning and organizing data for critical analysis, quality improvements and overall consistency
• Identifying patterns, relationships and connections between variables within the datasets
• Developing and building machine learning processes to automate work and identify patterns
• Testing model accuracy versus real-world scenarios and outcomes
• Creating visual representations through charts, graphs and dashboards
• Presenting results to decision-makers and other stakeholders

Cleaning data often takes the most time in these projects. This process ensures formats are consistent, removes unnecessary items and corrects errors or fills in missing information to maintain high data quality.

Different business sectors apply these techniques to address unique challenges. Healthcare teams may develop treatments based on clinical evidence and data, for example. Or they may monitor disease outbreak patterns across populations or enhance patient care through wearable devices that track vital signs.

Banks and financial institutions can use data in other ways. This can include detecting fraudulent transactions, assessing investment portfolio risks and deploying virtual assistants, sometimes called AI agents that can help customers with recurring questions.

In the telecommunications industry, insights can help providers interpret usage patterns. Insights into locations or customer preferences could reduce dropped calls and potentially boost customer satisfaction, which data workers can gather through surveys or other methods.

Retailers who analyze data can offer personalized product suggestions based on browsing history and purchase behavior, which could be beneficial to sales.

Data analysis can also help government agencies make policy choices informed by evidence. For example, politicians can track constituent feedback on services, and benefit administrators can catch fraudulent claims before payouts occur.

In the utilities industry, companies analyze smart meter readings to improve consumption patterns and customer satisfaction while optimizing workforce deployment.

Across these industries, their specific business needs can be addressed by using distinct data analysis methodologies:

• Descriptive work looks at past or current data, typically shown through pie charts, bar charts, line graphs and summary tables. For example, a flight booking service might use this approach to analyze daily ticket purchases to spot reservation spikes, point out slow sales days, and identify high-performing months.
• Diagnostic efforts dig deeper to understand why something occurred, applying techniques like drill-down analysis, data mining and correlation studies. For example, a flight booking app might investigate a particularly high-performing quarter to identify external factors, such as a large regional sporting event, that drove an increase in sales in a particular city. 
• Predictive approaches forecast future patterns using historical information through machine learning, pattern matching and statistical modeling. Computers learn to reverse-engineer cause-and-effect relationships in the information. For example, the flight company could predict booking patterns for the coming year and anticipate customer travel needs.
• Prescriptive methods suggest optimal responses by analyzing different options and their probable outcomes. They use techniques such as graph analysis, simulation, complex event processing and neural networks. The flight team might examine historical marketing campaigns to maximize the benefits of an upcoming sales boom, projecting outcomes for various spending levels across different marketing channels to reach larger groups of consumers.

Programming forms the foundation for analytical work in this field. People who work with data use programming languages to explore information, conduct statistical tests and build models that generate predictions. Open-source programming tools also offer users pre-built capabilities for statistics, machine learning and graphics creation, helping users complete tasks without coding.

Python^® is popular for its readable code and syntax, which provides useful data formats such as lists and dictionaries. These editorial formats can help users easily understand diverse data types. Because these pre-built modules simplify common analytical tasks without requiring entirely original code, Python’s accessibility makes it popular for users without an extensive coding background.

R offers another option with built-in support for statistics, machine learning and graphics generation. The language excels at statistical computing tasks, and it is optimized for visualizations.

Various other software applications help systematically manipulate and examine information. These programs can help extract relevant portions from larger datasets. These tools can also organize elements logically and prepare information for analysis. Machine learning frameworks help users build and refine models that identify meaningful patterns in complex data.

The field employs multiple other data analysis methods, including artificial intelligence, natural language processing, and specialized algorithms to process and make sense of various information types. Other popular frameworks support building neural networks (brainlike nodes that power AI computing structures) and training deep learning applications.

Data visualization platforms can help audiences understand dense information by converting numbers into well-designed charts or other formats that convey insights more clearly than detailed written reports. Available options include basic charts in spreadsheet software and advanced commercial products designed for data presentation. Some professionals use open-source libraries, such as JavaScript^® libraries, that integrate with analytical workflows. 

Cloud computing has transformed the approach to data projects by providing the flexibility and computational power required for complex analytical tasks. Machine learning algorithms often need many examples to spot reliable patterns, so working with that data may involve sorting through enormous collections of records. When accessible by the cloud, these datasets are readily available for analysis by AI or human evaluators.   

Cloud platforms address the challenges of working with large datasets by offering collaborative features that enable multiple teams to work on the same file. This helps teams that need access to identical information without downloading copies of the material. Cloud-based workspaces also let different departments examine the same datasets simultaneously from their own perspectives. For example, a marketing analyst and a finance data team member can look at the same information set and interpret it according to their respective departmental needs. The marketing analyst can examine customer purchasing habits, while the finance team can simultaneously examine spending patterns in the same transaction data.

Real-time updates in the cloud can also help avoid version conflicts. When one team member works using cloud tools, colleagues can review their material, suggest improvements, and track changes without worrying about overwriting each other’s contributions.

The benefits are evident in daily operations. For example, someone developing an analytical model no longer needs to email files back and forth with teammates or wait for someone else to finish before starting their own work. Everyone sees updates as they happen, with version tracking that records all changes. This is important when projects involve collaborators across different locations or time zones. Business stakeholders can check progress through dashboards without interrupting technical staff, while team members from other departments can explore findings using the same interface.

Cloud hosting can lessen time-wasting challenges associated with traditional software. Organizations using cloud services don’t have to spend time on installation, preference updates, configuration or other time-consuming maintenance. Some providers offer tools with visual interfaces that enable users to build models without using extensive coding. 

Interested in learning more about data science? University of Phoenix offers online technology degrees, including a Bachelor of Science in Data Science and Master of Science in Data Science.

Contact University of Phoenix for more information.

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