Data Science vs Machine Learning

Both data science and machine learning technologies can create smarter businesses that are more informed about their targeted customers resulting in better products and services. Yet, companies that have taken advantage of business process outsourcing gain a unique and distinct advantage into customer perception and behavior when they incorporate machine learning processes. This enables these companies to refine operations and make crucial adjustments to their customer’s journey.

What is Data Science?

Data science incorporates a number of technological fields to apply scientific algorithms, methods, and systems capable of extracting valuable knowledge from both structured and unstructured data. This data is mined from business statistics, analytical theories, and other data-rich sources including:

  • Retail and manufacturing data
  • Corporate financial data
  • Health care systems
  • Direct marketing platforms
  • Internet search engines

The end goal of data science applications is to uncover patterns and discover relationships within data sets. Information gleaned from data mining can help corporations and small- and medium-sized businesses streamline marketing campaigns by detecting sales or manufacturing trends.

A data scientist reveals significant marketing and sales facts to first rule out anomalies and exceptions that would introduce error into business decisions. Next, an in-depth analysis is made of economic patterns, business relationships, and customer trends. These theoretical techniques use information science, statistics, and mathematics to reveal a picture of market segments, customer spending, and other business-related phenomena.

Machine Learning Takes Data to New Heights

Machine learning uses artificial intelligence (AI) to take data mining to another level, accomplishing what is impossible for the pure, theoretical science that is the foundation of data analysis. Complicated business issues are solved due to the adaptive nature of today’s machine learning platforms. While machine learning systems also work with large data sets, this technology has the ability to learn and increase its own knowledge by intuitively re-using data.

Machine learning algorithms are highly complex, having the ability to process facial recognition data, natural speech patterns, and even analyze sentiment, attitudes, and belief systems of both customers and competitors. The technology gleans Big Data from across multiple platforms including Facebook posts, YouTube videos, instant messaging, emails, Instagram photos, and so on, and assimilates this data into specific machine learning protocols.

When you implement machine learning strategies within your business operations, you glean vital insights that can guide management in making actionable refinements to sales and operations. Here are other ways companies can leverage outsourced machine learning development:

  • Learn from massive amounts of data to guide sales and marketing strategies
  • Able to rapidly analyze data to predict real-time market sector variations
  • Can analyze past customer behavior and predict future buying trends
  • Identify high-risk medical patients or risky supply chain entities
  • Avoid duplicate or inaccurate data in business operations
  • Manage financial portfolios using trade algorithms
  • Detect fraud scenarios and improve accounting accuracy

The difference between data science and machine learning is an important consideration when choosing the best data technology solutions for your business or organization. Partnering with Sequent Solutions gives you access to a global network of data experts.

Sequent Solutions Can Propel Your Business

If you’re unsure of which technology can propel your business operations, provide B2B lead generation, and create B2C sales strategies, talk with our team of professionals based in Eastern Europe, one of the fastest-growing technology think-tanks in the world. Fill out your form below.

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