Data Science vs Machine Learning

Data science involves a range of tasks, including data collection, data cleaning and preprocessing, data exploration and visualization, feature engineering, modeling, and deployment. The goal of data science is to extract insights from data and use them to make better decisions or solve complex problems.

Machine learning, on the other hand, is a specific subset of data science that focuses on the development of algorithms that can learn from data and make predictions or decisions. Machine learning algorithms can be supervised, unsupervised, or semi-supervised, and can be used for a variety of tasks, such as classification, regression, clustering, and reinforcement learning.

Data science and machine learning are related but distinct fields. Data science is a broader field that involves the use of statistical and computational techniques to extract knowledge and insights from data, while machine learning is a specific subset of data science that focuses on developing algorithms that can learn from data and make predictions or decisions without being explicitly programmed.

Focus:

Data science focuses on extracting insights and knowledge from data to inform decision-making or solve complex problems.

Machine learning focuses on developing algorithms that can learn from data and make predictions or decisions without being explicitly programmed.

Methods:

Data science involves a range of tasks, including data collection, data cleaning and preprocessing, data exploration and visualization, feature engineering, modeling, and deployment. These tasks may involve statistical, computational, or machine learning techniques.

Machine learning involves developing algorithms that can learn from data and make predictions or decisions. Machine learning algorithms can be supervised, unsupervised, or semi-supervised, and can be used for a variety of tasks, such as classification, regression, clustering, and reinforcement learning.

Applications:

Data science can be applied to a wide range of industries and domains, such as healthcare, finance, marketing, and social media. Data science applications can include predictive modeling, data visualization, natural language processing, and more.

Machine learning has a wide range of applications as well, such as image recognition, speech recognition, fraud detection, recommender systems, and autonomous driving. Machine learning algorithms can be applied to any domain where there is a large amount of data and a need for predictions or decisions.

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Machine Learning (ML)

Machine Learning (ML) is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.

Machine Learning (ML) applications

Below are just a few examples of machine learning you might encounter every day,

Speech Recognition 

It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting.

Customer Service  

Online chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.

Computer Vision 

This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry. 

Recommendation Engines 

Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.

Automated stock trading 

Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.

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