What is Data Science?

Data science is a field of study bringing together multiple disciplines such as data mining, statistics, machine learning, analytics, and programming. Data science enables companies to understand enormous amounts of raw data from many sources. The field uses techniques such as Artificial Intelligence (AI) and Machine Learning (ML) to derive valuable insights from the information to make smarter data-driven decisions.

How Data Science Works

A data scientist uses specific steps to build, deliver and maintain data science products. Key techniques include:

• Data Extraction: The process used to pull the information from multiple data sources.

   

• Data Transformation: A technique for transforming the information into a simplified format.

   

• Data Mining: The process used to identify patterns in the data.

   

• Predictive Analytics: A technique for predicting future events based on data.

   

• AI and ML Tools: Advanced tools and techniques to analyze information.

   

• Visualization: A set of techniques for displaying the information in an easy-to-understand format.

   

Here’s how the high-level process works:

A data scientist uses data extraction and data transformation techniques to gather data from the raw source and transform it into a structured format. This makes the information easier to work with.

The next step is data mining, which applies algorithms to find anomalies, patterns and correlations within the data.

Next, predictive analytics applies modeling techniques to determine future performance based on current and historical data. Afterward, AI and ML tools perfect the decision model presented under predictive analytics.

Lastly, scientists use data visualization tools to display the information in a visual format, making it easier to understand and interpret.  

How Data Science Is Applied

Data science is valuable for many industries, including retail, manufacturing, healthcare and financial services. Specifically:

• Cybersecurity: Data science is used in cybersecurity to detect intrusions, identify vulnerabilities in systems or predict malicious activity.

   

• Financial Trading: Data science techniques can predict future stock prices to identify trading opportunities. It can also detect fraud such as insider trading, price manipulation, money laundering or market manipulation.

   

• Investments: Data science techniques can help manage portfolio risk.

   

• Healthcare: The healthcare field uses data science to improve the quality of care. The industry also uses it to help prevent disease and find cures for diseases. Data scientists are using data science techniques to build models for predicting which patients will be at the risk of an illness or which patients will be at a high risk of death if they contract a particular disease.

   

• Retailers: Retailers use the practice to predict customer needs. They are also using it to make recommendations for customers and to make data-driven decisions about store layout and product placement. The retail industry uses the practice to detect fraudulent transactions and prevent theft.

   

• Transportation: The transportation field uses the process to determine the best routes and plan for traffic jams. 

   

Data Science Benefits

Data science has many benefits making it a valuable practice across many industries. These benefits include: 

Empowering Management and Officers to Make Better Decisions: Data science aids management in making better decisions. For example, deciding what products to manufacture or where to open a new store. Other decisions might include determining which warehouse to distribute inventory from, or when to order supplies. Data science makes these decisions more accurate, faster, and more efficient. The practice also empowers front-line workers with the data and tools they need to do their jobs better. This can be in the form of better inventory management and order tracking or data-driven coaching and feedback.

Providing Insight into Operations: Data science provides insight into operations by using past events as a guide for future events. For example, data science can examine how cars flow through parking lots on weekends to determine how many people visit the store on average during those times of the day. This information is useful in planning staffing levels for those times or deciding which products should appear at those times.

Reducing Costs: Data science reduces costs by optimizing operations to use fewer resources without sacrificing quality or performance. An example is inventory management systems for determining which warehouse should distribute inventory based on factors such as shipping costs and inventory levels at each warehouse. Another example is logistics systems for optimizing delivery routes to reduce gas consumption while maintaining efficient deliveries.

Identifying Opportunities: Data science helps identify opportunities by providing accurate information about consumer behavior. This information helps plan marketing campaigns.

Improving Quality: Data science improves quality. An example is automated equipment maintenance systems for monitoring and adjusting equipment based on data collected from sensors surrounding the equipment. The system uses those adjustments to improve performance without requiring an employee to make the adjustments.