What is the difference between Data Science, Machine Learning, and Big Data?

I will be covering the following topics in order to make you understand the similarities and differences between them.

Data Science

Data Science is a mix of various tools, statistics, maths, algorithms, and machine learning principles with the goal to obtain patterns from the data and to add value to the business. To get insights from data Data Science deals with many things such as data collection, cleaning, analysis, visualization, model creation, model validation, prediction, and more.

We live in the digital world and deal with huge amounts of data in order to give insights to businesses. However, as everyone knows is that the data is nothing by itself. That is why Data Science comes to the stage to help for taking meaningful action of data with Data Science tools.

Generally, frameworks that are supportive structures are here to help derive value from data. For example, companies gather data regarding various aspects of their customers to predict their customers’ actions. Of course, there will be thousands of questions related to customers and Data Science using statistics can help the company to resolve problems. Statistics will assist in finding a correlation between variables, hypothesis testing, data analysis…etc. After data cleaning, the data becomes ready for data creation.

Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

It is a scientific field in order to find patterns in data and create a model. These models can learn from past data to predict the future. Of course, we need to create many models to see which work and which do not work.

Shortly, the primary goal is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Big Data

According to IBM, we create 2.5 Quintillion (2.5 × 1018) bytes of data every day!

Big data does not mean only having a large amount of data, it is the tools and mechanisms that we need to work on complex and fastly changing.

Big data involves the three ‘V’s – Volume, Variety, and Velocity –

  1. Volume: The size of data requires specialized infrastructure to acquire, store, and analyze it. It is now not uncommon for large companies to have Terabytes – and even Petabytes – of data in storage devices and on servers. This data helps to shape the future of a company and its actions, all while tracking progress.
  2. Velocity: It refers to the speed at which the data is generated.  For example, real-time analysis is crucial. Any delay will reduce the value of the data and its analysis for business.
  3. Variety: In the past, data collection and delivery were different. Once taking the shape of database files – such as Excel, CSV, and access – it is now being presented in non-traditional forms such as video, text, pdf, and graphics on social media. It means that there needed more work and requires more analytical skills.

In conclusion, data science’s goal is to derive actionable insights from data. Machine learning is based on artificial intelligence that is utilized by data science to teach the machines the ability to learn.

Volume, variety, and velocity are the three important points that differentiate big data from conventional data.

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