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Data science and machine learning: their differences and how they are used in the enterprise

People often confuse data science and machine learning,  but they are in fact separate entities, despite what the memes say. Let’s make clear what differences there are between data science and machine learning and give some examples of how each is used in business settings.

Is data science the same as machine learning?

Data science and machine learning are similar but not the same thing. Data science is a broad category of work that deals with data and computing. Machine learning falls into that category, but not all data science is machine learning. It’s like how all squares are rectangles but not all rectangles are squares. All machine learning is data science, but not all data science is machine learning. 

What is data science?

Data science includes programming skills and knowledge of mathematics and statistics with the goal of gaining meaningful insights from data. Data analysis, information engineering, artificial intelligence, and machine learning all fall under the category of data science. 

What is machine learning?

Machine learning is actually a type of data analysis with automated analytical model building. As a branch of artificial intelligence, machine learning is based on the notion that systems can learn from datasets, identify patterns within them, and make decisions without human control. In machine learning projects, a data scientist builds a model programmed to find patterns with certain rules. Then, the model is fed training data to analyze in order to quality-control the results. Once it is properly trained, the machine learning model is ready to perform its function without the help of humans.

How is data science used in the enterprise?

Data science has a wide range of uses, involving all parts of the enterprise from marketing to finance. Data science has proved its value, and data scientists are always finding new ways to implement solutions in the enterprise. The most data-driven businesses tend to win, so companies today cannot expect to be successful without leveraging data. Here are a few of the ways data science is being used in the enterprise.

  • Product Development: There is a lot of information that needs to go into product decisions. Data science makes it easier to analyze all the relevant data to come to the best conclusion possible. Data science makes product development not only more efficient but also metrics-based, a smart way to conduct business. 
  • Price Optimization: Keeping prices competitive is crucial in industries such as ecommerce. Data science can be used to scrape prices from competing sites and implement dynamic pricing to keep prices lower than the competition. 
  • Product recommendations: Recommended products often drive upsells on retail sites, and these are made using data science to analyze customer interactions with the website to glean behavioral trends and make recommendations.
  • Customer Segmentation: Data analysis can be used to segment customers into different audiences. Companies have been segmenting customers for decades, but with data science, it is becoming a more robust practice.

How is machine learning used in the enterprise?

Machine learning is a more recent development in business. Some companies are just beginning to fully grasp the potential for machine learning at the enterprise level. The possibilities really are endless for machine learning use cases. Some business processes or decisions up until recently required humans to crunch numbers and review data; they can now be done using artificial intelligence algorithms. Here are some of the popular ways companies are using machine learning, but remember, there are always new solutions being developed. 

  • Fraud Detection: Models can be trained to analyze transaction details in real time and classify them as either legitimate or fraudulent, alerting the team when there is suspicious activity. 
  • Medical Diagnosis: Machine learning is now being used in healthcare diagnostics to identify patterns in images and other data. ML models can analyze MRIs, CAT scans, physician notes, and more.
  • Demand Forecasting: Predictive models can make forecasts for future demand as well as other business metrics such as customer churn, customer retention, and sales forecasts.
  • Image and Speech Recognition: Companies like Google use image recognition to classify images and for reverse image search and speech recognition for their virtual digital assistants and voice activated applications. 

Algorithmia can help

Machine learning and data science are important innovations in the business world. Algorithmia understands the value of implementing machine learning at the enterprise level, which is why we created the AI Layer

The AI Layer allows data scientists to focus on training models rather than infrastructure and deployment challenges. Machine learning models can be difficult to get into production, but with the AI Layer in place from the beginning, productionizing ML is painless.

The AI Layer empowers ML leadership, data scientists, and devops teams to deploy and serve machine learning models quickly, giving them valuable time back for focusing on evaluating model output and health. 

Currently, data scientists are spending the majority of their time on infrastructure tasks—not their core roles. The AI Layer is a serverless microservices architecture that makes deploying, serving, and scaling challenge-free.

Get a demo of the AI Layer to see how it can benefit your organization.

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