Data Science vs Machine Learning: Key Differences Explained

Despite the expected overlap in the recent years, data science and machine learning are two distinct and distinct fields. Each handles the extraction of insights and value from data matrices in organizations in different ways. Boundaries need to be set between fields to ensure the proper functioning of data technologies and to help the professionals of the future. With the rapid adoption of AI, including technologies such as ChatGPT and Perplexity AI, the question still stands as to how data science and machine learning diverge in their techniques, applications, business intelligence, and business decisions.

 This article outlines the core divergences, as well as the points of intersection, between data science and machine learning, including their differences, their similarities, and their effects on technology and business in the contemporary world.

What is Data Science?

Data science refers to the extraction of insights from an organization's internal and external data. It includes data collection, deep learning, data wrangling, data cleaning and preprocessing, exploratory data analysis (EDA), model building and statistical analysis, data visualization and reporting, and finally, deployment and maintenance.

Data science integrates various disciplines, including statistics, computer science, big data, programming languages, as well as data engineering, to clean raw data, unify and harmonize it, and extract actionable insights using data manipulation techniques.

What is Machine Learning?

Machine learning is a subset of artificial intelligence that focuses on developing systems capable of self-learning and improving continuously without being programmed explicitly. It uses statistical methods, models, and ML algorithms to read patterns, identify them, and make decisions or predictions based on them. 

Key components of machine learning techniques include algorithms, training data, and model evaluations.

Is Machine Learning Part of Data Science?

Although the two terms may be used interchangeably, machine learning and data science are distinct fields with their own applications and focus areas.

Machine learning is considered a subset of data science, which encompasses several tasks, from data collection to visualization. Data scientists rely on various techniques, such as statistical modeling and business analytics, to make data-driven decisions.

Machine learning is a technique designed for data analysis and prediction. However, the capability to continuously learn and reduce or eliminate the need for explicit human intervention distinguishes machine learning from data science.

For example, PayPal and Stripe use machine learning models, including Random Forest and Gradient Boosting, to automatically detect fraudulent transactions. Analysts train these models on large volumes of data sets to identify patterns, such as geographic trends and anomalous spending behaviors (Source: PayPal, Stripe).

Data science enables Netflix to study behaviors like which genres are favored, audience preferences, and viewing times, and assists in defining the content strategy. Machine learning subsequently takes over to automate audience recommendations, predicting what series or movies to suggest with large datasets without manual intervention from Netflix staff using predictive modeling. (Source: Netflix).

Both data science and machine learning focus on data processing. Organizations often encounter unstructured data across various sources and formats, including APIs, sensors, CRMs, POS, emails, and transcripts. Duplicity and missing values, often leading to incorrect insights, are common issues with unstructured data.

Data Science vs Machine Learning: Key Differences

Data science is all about solving problems with the help of data. For example, “Why did sales drop last quarter?” Or “What type of customers are likely to buy this product?” Data science is concerned with the “what” and “why.”

Machine learning, on the other hand, focuses on the how. It enables data-driven solutions by identifying patterns, building predictive models, and automating decision-making processes. For instance, machine learning answers how to forecast customer churn, how to optimize pricing to boost sales, or how to classify customers based on their behavior.

Netflix relies significantly on data science to understand the nuances and subtleties of its audience. It analyzes vast amounts of user data to discover that users in urban centers tend to watch shows with shorter episode durations, or customers in the suburbs are more likely to binge-watch. This information gives Netflix insights into what future trends it should acquire or how it should promote its shows.

Once these insights are derived, machine learning automates the process. Netflix’s uses machine learning, collaborative filtering and neural networks for recommendations and to eliminate the need for constant human intervention in acting on insights. 

Data Science vs Machine Learning in a Nutshell

The key difference between data science and machine learning lies in their roles: data science focuses on understanding "what" and "why" through analysis and insights. In contrast, machine learning operationalizes these insights by automating predictions and actions. 

Together, they complement each other, with data science driving strategic decisions and machine learning ensuring seamless experience. 

Discover the Top 5 Problems in Data Science and How MLWorks Fixes Them

Applications of Data Science

Many end-users and industries leverage data science due to its transformative impact on business decisions and operational outcomes.​

Let us take a look at how data science is being used across key sectors:

Healthcare and Lifesciences

• IBM Watson Health: Employs data science to understand millions of medical records and assists clinicians in diagnosing intricate diseases like cancer with higher precision. (Source: The New York Times).
• Pfizer: Uses data analytics to streamline the COVID-19 vaccine rollout and focuses on many other new drug development projects. (Source: Pfizer).
• Other examples include more effective early public health detection, personalized care plans, and public health resource and hospital management.
  Why It Matters: In healthcare, data science is about saving lives, improving outcomes, and minimizing costs by facilitating better care delivery. 

Finance

• JPMorgan Chase: Uses real-time fraud detection and transaction monitoring data science tools, which enhance instant payment systems' security. (Source: American Banker). 
• FICO: Uses advanced data science for credit scoring and risk analysis for loans on disparate unstructured data.  (Source: FICO).
• Other examples include other risk management, algorithmic trading, and customer segmentation.
  Why It Matters: In finance, data science supports quicker, more precise fraud detection, risk management, and consumer decision assistance. 

Retail and E-commerce

• Walmart: Utilizes data science on demand forecasting and inventory management to ensure that products are available when and where consumers need them. (Source: Walmart).
• Amazon: Uses behavioral data modeling for automated recommendations, which enhances the engagement of consumers. (Source: Amazon Science).
• Other examples include: personalized marketing, dynamic pricing, supply chain operations, and cross-priced to-gate systems.
  Why It Matters: In retail, data science improves customer satisfaction by anticipating needs, personalizing offers, and enhancing operational flexibility. 

Manufacturing

• Manufacturing General Electric (GE): Uses predictive analytics in IIoT to reduce the frequency of unscheduled equipment downtimes. (Source: GE Aerospace).
• Boeing: Utilizes data-driven methodologies to enhance quality control and reduce product defects in its manufacturing processes. (Source: Quality Magazine).
• Other examples include: optimizing supply chains and improving safety through real-time production monitoring.​
  Why It Matters: In manufacturing, data science contributes to better and more safe operational continuity by minimizing downtime and increasing quality and efficiency.

Applications of Machine Learning

The adoption of machine learning is rising in various industries, owing to advancements in the development of new models and decreases in initial starting costs. The machine learning market is projected to expand at a 34.8% CAGR by the end of 2030 (Source: Grand View Research). 

Let us take a look at some of the applications of machine learning:

Healthcare & Lifesciences

• Zebra Medical Vision: Machine Learning analyzes complex medical imaging like CT scans and MRIs for disease detection, and helps clinicians make faster and more accurate diagnostic decisions. (Source: Harvard Digital Innovation and Transformation).
• Dragon Medical One: Automating clinical documentation using natural language processing technology saves clinicians time and reduces documentation errors. (Source: Nuance).
• Other examples include: EHR analysis to predict patient risk, optimize treatment plans, and resource allocation.
• Why It Matters: Machine Learning in healthcare defines the future of clinical documentation and transforms the delivery of care, greater accuracy and greater clinician-patient interaction time, and attention.

Finance

• Bloomberg: Uses NLP-powered machine learning to analyze investor sentiment from news, earnings calls, and social media, predicting market trends with improved accuracy. (Source: PyQuant Newsletter).
• Bank of America’s Erica: Erica functions as a customer service agent and a virtual financial advisor, processing routine transactions and providing customer assistance 24/7. (Source: AIM Research).
• Other examples include:  Automated portfolio management, real-time fraud and regulatory compliance detection, and adherence to regulatory guidelines.
• Why It Matters: In finance, machine learning provides real-time insights and analyses, lowering fraud risks and increasing customer engagement, making it safer and more intelligent. ​

Retail and E-commerce

• Amazon: Leverages convolutional neural networks (CNNs) in visual search and image recognition, facilitates product discovery, and makes it easier to find products. (Source)
• Target: Utilizes other ML applications, such as anomaly detection and high demand forecasting, to optimize inventory and supply chain management. (Source: Progressive Grocer).
• Other examples include: pricing automation, personalized marketing, and predictive logistics. 
• Why it matters: In retail, machine learning improves customer satisfaction and experience through personalization and enhances supply chain efficiency and responsiveness.

Manufacturing

• BlueScope Steel: Uses machine learning to predict maintenance by assessing pressure and vibration metrics to help avoid expensive breakdowns. (Source: Siemens Blog)
• DHL: Employs ML-based logistics control and Resilience 360 to manage delivery disruptions caused by natural events, closed routes, and other events. (Source: Procurement Magazine).
• Other examples include:  adaptive robotics and real-time quality control.
• Why it matters: Applying machine learning in manufacturing saves money and improves product quality by reducing downtime and enhancing operational resilience.

Data Science vs. Machine Learning: Which is Better?

Whether data science or machine learning is better is akin to asking which is more critical for a business, vision or action? Both are indispensable and work in tandem to drive progress.

Data Science provides the framework and strategy

Data Science creates the framework and strategy. It captures the entire data lifecycle: collection, cleaning, exploration, analysis, and visualization. Data Scientists shine a light on the insights encapsulated in the business problems and facilitate decision-making.

Pros: Expansive view, robust analytical base, and strategic vision driving.

Cons: Actionable outcome delivery can be slow, and quality data dependence. 

Machine Learning focuses on execution and automation

Machine Learning pivots on execution and automation. As a data science subset, ML creates models allowing systems to learn from data and make autonomous decisions, thus minimizing human interaction. 

Pros: Scalability, automation, and continuous learning, along with the ability to make real-time predictions. 

Cons: Well-prepared data is critical, ongoing model adjustments are required, and context can be missing.

Both are complementary and essential

Data Science and Machine Learning are two sides of the same coin. Data Science defines the problem and prepares the data, while Machine Learning builds predictive models that automate and optimize processes. Together, they ignite innovation and intelligence. This is the competitive edge derived from the synergy of automated machine learning and data science insights.

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FAQs

1. Data science vs machine learning–what is the primary difference?

Data science focuses on extracting insights and solving problems using data, while machine learning enables systems to learn and make predictions autonomously.

2. Can machine learning exist without data science?

No. Machine learning relies on data science for data preparation, cleaning, and insights to build and train effective models.

3. Which is better for businesses: data science or machine learning?

Both are essential data science defines the framework and strategy, while machine learning enhances predictions and automates processes.

4. How do Data Science and Machine Learning work together? 

Data Science and Machine Learning work together to turn unprocessed data and complex problems into smart and automated business decisions. The first part of the process is data science. It gathers, cleans, analyzes, and visualizes the data to find and extract vital patterns, trends, and insights that drive the ‘what is going on’ and ‘why’ questions. After laying down the science insights, the predictive models and algorithms that machine learning create and operationalizes decide and predict on automated data streams independently without human intervention.

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Tredence