- September 21, 2022
Table of Contents
However, even then, they cannot be regarded as totally separate, since machine learning can be considered a data science technique. In this article, you will learn what these two fields are all about, and how they are interrelated.
Data science is a multidisciplinary field in which the goal is to extract valuable information from data. It’s geared towards helping organisations make better decisions and predictions, build effective strategies, and develop more advanced products. This requires domain expertise, a proficient knowledge of mathematics, and adequate programming skills.
Statistics is a field of mathematic that involves collecting, analysing, presenting and interpreting quantitative data. Statisticians focus on significance testing, diagnostic plotting, and normality distribution, etc. Data science, on the other hand, is a field which uses scientific methods and the newest technologies to extract knowledge from these data sets in various forms. It evolved naturally from academic statistics, providing us with automation, the use of different programming languages (such as Python), and allowing us to leverage machine-learning libraries (such as TensorFlow).
Of course, statisticians and data scientists have a lot in common. For example, they are both based in mathematics, they both analyse trends, and they both make predictions and prepare the results of their research for consumption by non-technical users. The biggest difference between them lies in the utilisation of new technologies.
Big Data refers to data that is generated rapidly and produced continuously in huge volumes, and which is often available in real-time. The amount of data collected is definitely too large and complex to be stored or processed by traditional tools. That’s why one of the biggest challenges in big data development is the efficient digestion of data – and this is where data science comes into play.
Data scientists apply machine learning algorithms to huge amounts of different types of data (like numbers, pictures, videos, texts, audio files, and much more) in order to create intelligent systems that can mimic the cognitive actions of human brains in order to derive meaningful insights from an increasingly complex flood of information.
Machine learning (ML) refers to a part of artificial intelligence (AI) that focuses on computer algorithms that are able to learn and improve their own accuracy gradually and automatically through experience, without any human assistance or being programmed to do so.
ML algorithms are designed to identify patterns in data and help people make better predictions and decisions.
Data science studies data and focuses on extracting meaning from it, while machine learning refers to a set of tools, technologies and methods for building models that are able to learn on their own without human intervention. Machine learning is often leveraged by data scientists, however, this is not always necessary – it all depends on your goals.
There are also two more concepts that are inseparably linked to data science and machine learning which we should look into now: predictive analytics and prescriptive analytics. The difference between them lies in the outcomes of their analyses. The first one provides you with raw information on what could happen in the future, so you can come up with an appropriate plan based on those predictions. The latter provides you with a few different action plans that are ready for implementation, which you can compare, select and then apply immediately. This option is definitely more advanced and can significantly accelerate business decision-making processes.
And last but not least – the techniques. Both data science and machine learning use a number of various technologies and operational methods. However, as I mentioned above, ML also refers to a set of techniques that is often leveraged by data scientists (apart from others, such as linear regression, decision trees or dimensionality reduction, etc.).
Machine learning itself often uses two types of techniques:
There’s also reinforcement learning, which doesn’t require any input/output data. Instead, it focuses on “finding a balance between the exploration (of uncharted territory) and exploitation (of current knowledge)”. This is utilised when we want to train a model on how to act in a changing environment, e.g., while training industrial robots or autonomous cars.
Both data science and machine learning are now used in making business intelligence (BI) strategies more effective at identifying hidden patterns and insights. They automate a lot of processes, allowing you to make faster and better decisions, detect anomalies, and prevent mistakes and financial disasters.
As a result, human experts are then free to focus on other tasks, including how to further improve the performance of these algorithms. So, if you’re interested in combining BI with data science and machine learning in order to achieve maximum efficiency.