Data Science vs Machine Learning

The concept of data science and machine learning is probably the most eclipsed topic that often confuses students and tech enthusiasts. Although they are a part of the domain of Artificial Intelligence, they are not the same. They are intertwined with each other, but they have their own set of individual characteristics. These are the contemporary trends in tech, and it is important to understand the striking differences between the two. In this article, we will inform you about the difference between Data Science and Machine Learning, so, you can differentiate between the two and select the one that suits you.

Data Science

Data Science is a combination of algorithm development, data interference, and technology to find solutions to complex analytic problems. At the centre of Data Science is data. Data science involves the processing and analysis of data that is generated for various insights that will help in numerous business purposes.

The main aim of Data Science is to examine the current and past data and make projections regarding the future. It provides solutions to questions that ask the ‘how’ and ‘what’ for a specific event. For example, while browsing through a few products or categories while being logged in on Myntra, data is generated. The data shall be used by Data Scientists/Analysts at the backend to understand your behaviour and then push several retargeted advertisements to get you to buy what you have been browsing. This is one of the least complex implementations of data science. Further, there are concepts like cart abandonment and others.

Data Science is more evolved than Machine Learning. Data in Data Science might not be derived from a mechanical process. Data can be manually stacked, and it might have almost nothing to do with learning, in general.

The main processes involved in data science are:

1. Data extraction
2. Data cleansing
3. Data analysis
4. Visualization
5. Actionable generation of insights
6. Distributed architecture
7. Automating machine learning
8. Data integration
9. Dashboards and BI
10. Data engineering
11. Automated and data-driven decisions

Also Read: Business Analytics v/s MS in Data Science: Which is better?

Inquisitiveness, curiosity, to the maximum limit is one of the few necessities of a data scientist. There are a great number of insights that are overlooked and not taken care of in a large pool of data. Data science provides a new path to decipher complexities like customer behaviour, operational shortcomings, supply chain cycles, predictive analysis, and many more. Implementation of data science is very important to hold on to old consumers and even fetch new ones to retain its position in the market.

Top colleges for Data Science

Candidates wanting to pursue Data Science can check out popular universities offering the same.

• Columbia University (CU)
• New York University (NYU)
• Carnegie Mellon University - USA (CMU)
• Northwestern University
• University of Cincinnati (UC)

Machine Learning

The main purpose of Machine Learning is to enable computers to learn on their own, by making necessary adjustments as and when required, without any intervention from humans. In fact, machine learning is a function of Artificial Intelligence that provides computers with the ability to learn from previous experience and make improvements without any human intervention.

It is a part of data science. Machine learning means that algorithms are dependent on data that are used as a training set, to fine-tune some algorithm parameters or some data model. This trespasses a number of techniques like regression, naïve Bayes, or supervised clustering. Data gets generated way too much, and it becomes tiring for a data scientist to work on it. That is exactly when Machine Learning pops into the scene.  Machine learning is the ability provided to a system that helps it learn the automated processing of data sets without any sort of human intervention. One of the simplest implementations includes the working of Netflix. You watch a series or a movie and then find another website recommending shows or movies of a similar genre.

Machine learning requires knowledge of probability and statistics. The technical skills required are coding, data evaluation, modeling skills, and many more. Data science requires aspects of machine learning for functionality.

Top colleges for Machine Learning

Candidates wanting to pursue Machine Learning can check out popular universities offering the same.

• University of Wollongong
• KTH Royal Institute of Technology (KTH) - Sweden
• Imperial College London - UK

Also Read

• MS in Data Science in USA: All you need to know
• Business Analytics v/s MS in Data Science: Which is better?