Data science and machine learning (ML) make it possible to obtain meaningful information from a mass of data. However, the terms are not synonyms. In fact, machine learning, data science, and data analytics are different fields with different goals and skills. This article explains the difference between Data Science vs Machine Learning.
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What is Data Science?
Data science is an interdisciplinary science that analyzes, visualizes and interprets differences in data in order to answer questions and gain new knowledge for companies and their decisions. Data science therefore also appears as an umbrella term, combining statistics, programming, data analysis and artificial intelligence. The data to be analyzed comes from various channels and grows rapidly, so that its analysis exceeds human capabilities, at least without special Tools and techniques.
Therefore, to work in this field, you need a know how of technical skills. One must be familiar with programming languages and computer science, but also with statistics, mathematics and data visualization. In addition, it is important to have a research-oriented mind, to be able to identify gaps in knowledge and formulate questions that can help fill them.
Data science is an integral part of many industries today. Working with data helps companies better understand their customers, optimize business processes and offer better products. Instead of relying on the highly subjective opinion of one person, they have facts and figures at their disposal.
What is Machine Learning?
Machine learning is a branch of computer science. It deals with the question of how computers can solve problems using raw data as a basis for decision-making without humans having to program them explicitly. It is therefore about technologies for data generation and Extraction to develop so that the machine learns independently. Here, there are supervised, unsupervised and reinforcement learning methods. Each of these types of ML has its advantages and disadvantages. Learning is done by applying algorithms to data. Each of these ML groups uses different algorithms. These are instructions for performing a Process. They are applied to a database to recognize patterns and "learn" from them. Algorithms can thus manage to simulate the functioning of a living human brain. They analyze huge amounts of data and extract patterns and rules from it.
To deploy algorithms, monitor their performance, and find better parameters for training them, we need a scientific discipline that explains how to do it right. Machine learning studies how to build a data generation and extraction model that is appropriate for a particular data set, but can also be useful for other data sets. In the end result, machine learning provides a high-quality model that produces reproducible results. This is efficient to ensure business success.
Data Science vs Machine Learning
Data science aims to derive meaning and insights from data using a scientific approach. In contrast, machine learning is about developing methods that use raw data to make decisions to improve performance or make predictions. Therefore, machine learning is a branch of the artificial intelligence. Data science, on the other hand, is a generic term for technological disciplines such as machine learning or artificial intelligence.
In the last few years, machine learning and artificial intelligence have been (AI) dominated parts of data science and play a critical role in the Data analysis and business intelligence. Machine learning automates the process of data analysis and goes beyond it to make predictions based on the collection and analysis of large amounts of data about specific populations. Models and algorithms are developed for this purpose.
Machine learning does not replace data science, however, but is one of the many tools in a data scientist's belt. For machine learning to work, it takes a skilled data scientist who can organize the unstructured data and apply the right generation tools to make full use of the numbers.
|Data Science||Machine Learning|
|Destination||aims to derive meaning and insight from data using a scientific approach. This knowledge is then applied in companies, government agencies and other institutions to increase profits, innovate products and services, make better decisions and automate and optimize processes.||A subfield of artificial intelligence concerned with understanding and developing methods that "learn." These methods use data to improve performance on a range of tasks. Machine learning algorithms use artificial intelligence to create a model based on sample data, called training data, to make predictions or decisions without being explicitly programmed to do so.|
|Skills/Tools||Machine learning tools, coding skills (Python/R), statistics, SQL/NoSQL, data wrangling, data visualization.||Programming skills (Python, SQL, Java), statistics and probability, prototyping, data modeling.|
|Scope||Broader scope; does not focus only on statistics and algorithms;|
Generic term for data acquisition, data cleaning, data investigation, anomaly detection, prediction of probabilities, extraction of data;
Data in Data Science may or may not be processed using Machine Learning.
|Focus on machine learning algorithms; subfield of artificial intelligence;|
Includes supervised, unsupervised and semi-supervised learning, use cases: Analyzing spam emails, developing chat bots, analyzing customer behavior.
|Destinatione||Report based on key data, visualization by means of graphics, charts||Modeling events through the use of real data.|
Data Science vs. Machine Learning using the example
It is important for insurers to detect fraud as early as possible to minimize losses. Here, Data Science helps pools, primary insurers, health insurers and reinsurers to achieve the necessary level of protection and avoid financial losses. Data Scientists improve the level of customer security in this regard. They monitor as well as analyze customer data and detect suspicious as well as malicious transactions. Machine learning and data science can complement each other in data extraction.
The key steps in the fraud detection process are:
- Collect a large number of data samples to train and test the machine learning model.
- Training the model to make predictions
- Testing the accuracy of the results and deployment
The result, for example, is a system that withholds further transactions if a large number of transactions suddenly appear on a customer's account. The account holder must then verify these transactions himself. Such systems help customers keep track of their account movements.
Which is better data science or machine learning?
A company cannot have one without the other. Both are a part of each other. After all, machines cannot gain experience without data, and data can always be better analyzed when processed using the standards of data science as a generic term. In the future, specialists such as data scientists and machine learning engineers will need to have at least a working understanding of each other's field for generating data to improve the quality of their work. As artificial intelligence (AI) becomes increasingly important to the success of real-world businesses, both data science and machine learning are taking center stage.
Machine learning is a natural fit - for example, for data-driven fields like healthcare. In healthcare, ML helps analyze, categorize and organize healthcare data. ML systems help hospitals and other medical facilities provide better service to patients in terms of scheduling, document access, and medical care.
Data science enables organizations to efficiently understand vast amounts of data from multiple sources and gain valuable insights to make smarter data-driven decisions. Data Science is widely used in various industries, including industrial companies, healthcare, banking, insurers, and the public sector.
A machine learning model consists of mathematical functions that recognize certain types of patterns. Users train a model on a set of data and provide it with an algorithm to reason and learn from that data. This model can be used to analyze data and make predictions. For example, an ML model can recognize certain patterns in a mass of documents for extraction.