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Monday, March 4, 2024

Data science vs. machine learning: What’s the difference?

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. This post will dive deeper into the nuances of each field.

What is data science?

Data science is a broad, multidisciplinary field that extracts value from today’s massive data sets. It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve. Data science solves a business problem by understanding the problem, knowing the data that’s required, and analyzing the data to help solve the real-world problem.

What is machine learning?

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. It requires data science tools to first clean, prepare and analyze unstructured big data. Machine learning can then “learn” from the data to create insights that improve performance or inform predictions.

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. It processes enormous amounts of data a human wouldn’t be able to work through in a lifetime and evolves as more data is processed.

Challenges of data science

Across most companies, finding, cleaning and preparing the proper data for analysis can take up to 80% of a data scientist’s day. While it can be tedious, it’s critical to get it right.

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored.

One challenge in applying data science is to identify pertinent business issues. For example, is the problem related to declining revenue or production bottlenecks? Are you looking for a pattern you suspect is there, but that’s hard to detect? Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics.

How data science evolved

With the increase in data from social media, e-commerce sites, internet searches, customer surveys and elsewhere, a new field of study based on big data emerged. Those vast datasets, which continue to increase, let organizations monitor buying patterns and behaviors and make predictions.

Because the datasets are unstructured, though, it can be complicated and time-consuming to interpret the data for decision-making. That’s where data science comes in.

The term data science was first used in the 1960s when it was interchangeable with the phrase “computer science.” “Data science” was first used as an independent discipline in 2001. Both data science and machine learning are used by data engineers and in almost every industry.

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. It’s also necessary to understand data cleaning and processing techniques. Because data analysts often build machine learning models, programming and AI knowledge are also valuable.  as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. It’s also necessary to understand data cleaning and processing techniques. Because data analysts often build machine learning models, programming and AI knowledge are also valuable.

Data science use cases

Data science is widely used in industry and government, where it helps drive profits, innovate products and services, improve infrastructure and public systems and more.

Some examples of data science use cases include:

  • An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app.
  • A manufacturer developed powerful, 3D-printed sensors to guide driverless vehicles.
  • A police department’s statistical incident analysis tool helps determine when and where to deploy officers for the most efficient crime prevention.
  • An AI-based medical assessment platform analyzes medical records to determine a patient’s risk of stroke and predict treatment plan success rates.
  • Healthcare companies are using data science for breast cancer prediction and other uses.
  • One ride-hailing transportation company uses big data analytics to predict supply and demand, so they can have drivers at the most popular locations in real time. The company also uses data science in forecasting, global intelligence, mapping, pricing and other business decisions.
  • An e-commerce conglomeration uses predictive analytics in its recommendation engine.
  • An online hospitality company uses data science to ensure diversity in its hiring practices, improve search capabilities and determine host preferences, among other meaningful insights. The company made its data open-source, and trains and empowers employees to take advantage of data-driven insights.
  • A major online media company uses data science to develop personalized content, enhance marketing through targeted ads and continuously update music streams, among other automation decisions.

The evolution of machine learning

The start of machine learning, and the name itself, came about in the 1950s. In 1950, data scientist Alan Turing proposed what we now call the Turing Test, which asked the question, “Can machines think?” The test is whether a machine can engage in conversation without a human realizing it’s a machine. On a broader level, it asks if machines can demonstrate human intelligence. This led to the theory and development of AI.

IBM computer scientist Arthur Samuel coined the phrase “machine learning” in 1952. He wrote a checkers-playing program that same year. In 1962, a checkers master played against the machine learning program on an IBM 7094 computer, and the computer won.

Today, machine learning has evolved to the point that engineers need to know applied mathematics, computer programming, statistical methods, probability concepts, data structure and other computer science fundamentals, and big data tools such as Hadoop and Hive. It’s unnecessary to know SQL, as programs are written in R, Java, SAS and other programming languages. Python is the most common programming language used in machine learning.

Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. It can recognize complex patterns in text, images, sounds, and other data and create accurate insights and predictions. Deep learning algorithms are neural networks modeled after the human brain.

Subcategories of machine learning

Some of the most commonly used machine learning algorithms include linear regression, logistic regression, decision tree, Support Vector Machine (SVM) algorithm, Naïve Bayes algorithm and KNN algorithm. These can be supervised learning, unsupervised learning or reinforced/reinforcement learning.

Machine learning engineers can specialize in natural language processing and computer vision, become software engineers focused on machine learning and more.

Challenges of machine learning

There are some ethical concerns regarding machine learning, such as privacy and how data is used. Unstructured data has been gathered from social media sites without the users’ knowledge or consent. Although license agreements might specify how that data can be used, many social media users don’t read that fine print.

Another problem is that we don’t always know how machine learning algorithms work and “make decisions.” One solution to that may be releasing machine learning programs as open-source, so that people can check source code.

Some machine-learning models have used datasets with biased data, which passes through to the machine-learning outcomes. Accountability in machine learning refers to how much a person can see and correct the algorithm and who is responsible if there are problems with the outcome.

Some people worry that AI and machine learning will eliminate jobs. While it may change the types of jobs that are available, machine learning is expected to create new and different positions. In many instances, it handles routine, repetitive work, freeing humans to move on to jobs requiring more creativity and having a higher impact.

Some machine learning use cases

Well-known companies using machine learning include social media platforms, which gather large amounts of data and then use a person’s previous behavior to forecast and predict their interests and desires. The platforms then use that information and predictive modeling to recommend relevant products, services or articles.

On-demand video subscription companies and their recommendation engines are another example of machine learning use, as is the rapid development of self-driving cars. Other companies using machine learning are tech companies, cloud computing platforms, athletic clothing and equipment companies, electric vehicle manufacturers, space aviation companies, and many others.

Data science, machine learning and IBM

Practicing data science comes with challenges. There can be fragmented data, a short supply of data science skills, and tools, practices, and frameworks to choose between that have rigid IT standards for training and deployment. It can also be challenging to operationalize ML models that have unclear accuracy and predictions that are difficult to audit.

IBM’s data science and AI lifecycle product portfolio is built upon our longstanding commitment to open-source technologies. It includes a range of capabilities that enable enterprises to unlock the value of their data in new ways.

IBM data science tools and solutions can help you accelerate AI-driven innovation with:

  • A simplified MLOps lifecycle with a collaborative platform for building, training, and deploying machine learning models
  • The ability to run any AI model with a flexible deployment
  • Trusted and explainable AI due to generative AI powered by (newly added) foundation models (Visit watsonx.ai to learn more)

In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes. Moreover, you’ll be able to manage and govern the AI lifecycle with MLOps, optimize business decisions with prescriptive analytics, and accelerate time to value with visual modeling tools.

Learn more about data science with IBM

Product Marketing Manager, Data Science & MLOps

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