Machine learning vs data science: What’s the difference?
Both machine learning and data science are fields that extract insights from data, but the methods vary significantly
Machine learning and data science are two of the most rapidly growing and in-demand fields of today's tech landscape. Both involve working with data, but each field has different goals and uses different methods to achieve these goals.
Machine learning is a subfield of artificial intelligence (AI) that focuses on building mathematical models that can learn from large quantities of data and make predictions or decisions without explicit programming. On the other hand, data science is an interdisciplinary field that encompasses various techniques and tools for working with data, such as statistics, machine learning, and programming.
What are the similarities between machine learning and data science?
Data science and machine learning are closely related fields that share a common goal of extracting insights and knowledge from data. Data science encompasses various techniques to achieve this goal, including statistical modelling, data visualisation, and A/B testing.
Machine learning, on the other hand, achieves this goal through the development and application of algorithms that learn from data and make predictions, or take actions, without explicit instructions.
There is a substantial overlap between data science and machine learning, with many data science projects involving machine learning algorithms. However, despite the similarities and overlaps between these two fields, each has distinct characteristics and uses.
What are the differences between machine learning and data science?
Machine learning is a technical field that demands solid background in mathematics and computer science. Data science, on the other hand, encompasses a broader range of skills, including domain knowledge, programming, and statistics. While data scientists may use machine learning, they may also rely on other methods to extract actionable insights from data.
While machine learning focuses on developing and applying predictive models, meanwhile, data science also includes tasks such as data cleansing, data visualisation, and data management, which are vital to obtaining high-quality insights from data.
While data science and machine learning are closely related disciplines, they have specific differences in what they involve, the processes and tools they use, and the required skills.
Of the two, data science is a broader field that encompasses machine learning and other techniques, while machine learning is a specific method of extracting insights.
Which processes do they each involve?
Data science projects involve many processes, including data collection and cleaning, data exploration, feature engineering, model selection and validation, and deployment.
Machine learning projects focus on the development and application of predictive models. The process typically includes selecting a model, training it on a labelled dataset, evaluating its performance, and tweaking the model as and when needed.
Which skills are required?
Data science requires a wide range of skills, including domain knowledge, programming, statistics, and data visualisation.
On the other hand, to forge a career in machine learning, one needs experience in mathematics and computer science. In particular, skills such as linear algebra, calculus, and probability theory are essential for understanding and implementing machine learning algorithms.
Both data science and machine learning are useful for a wide range of jobs and industries. Some examples of job titles that involve data science and machine learning include data scientist, machine learning engineer, data engineer, business intelligence analyst, data analyst, research scientist, and AI engineer.
How are machine learning and data science used across different industries?
In technology, data science and machine learning are used by engineers to build intelligent systems and improve user experience (UX). For example, Google uses machine learning to improve its search results, and Amazon uses it to personalise product recommendations.
Financial institutions, on the other hand, would use data science and machine learning to detect fraudulent transactions, predict stock prices, and identify potential investment opportunities.
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Similarly, in retail, companies analyse customer data and make personalised product recommendations, while also working to optimise the supply chain and logistics. In transport, meanwhile, machine learning is used to optimise traffic flow, prejudice maintenance and demand for a specific route.
Finally, data science and machine learning are widely used in research fields such as physics, genomics, biology and environmental science to analyse and understand complex data sets.
What are the most common applications of data science?
There’s an array of use cases for strong data science skills across various organisations. Among these include the following.
Predictive maintenance in manufacturing
By analysing sensor data from machinery, data scientists can predict when equipment is likely to fail, allowing businesses to schedule maintenance and reduce downtime.
Fraud detection
By analysing transaction data, data scientists can build models to identify unusual behavior that may indicate fraudulent activity and help with fraud detection and prevention.
Market segmentation
By analysing customer data, data scientists can segment a market and identify customer groups with similar characteristics, allowing businesses to target their marketing efforts effectively.
Sentiment analysis
Analysing text data by carrying out text mining can help a data scientist understand customers' perceptions of a particular product, service, and brand and help businesses to make decisions accordingly.
What are the most common applications of machine learning?
Similarly, machine learning is used across the entire economy, with varying sizes and types of organisations able to access machine learning services to achieve several business aims.
Image and speech recognition
Machine learning algorithms can be used to analyse images and audio data and recognise objects, people, and speech.
Recommendation systems:
Machine learning algorithms can be used to analyse user data and make personalised recommendations for products or content.
Natural language processing (NLP)
Machine learning models can be used for tasks like language translation, text summarisation, sentiment analysis, and much more.
Self-driving cars
Machine learning algorithms can be used to train cars to drive autonomously by analysing sensor data and making decisions about how to navigate the road.
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