Data Science vs Machine Learning

Data Science vs Machine Learning

The big data market is forecasted to grow from USD 28.65 Billion in 2016 to USD 66.79 Billion by 2021, at a High Compound Annual Growth Rate (CAGR) of 18.45%. We are hearing a lot about Data Science and Machine learning, and the buzz around them is obvious in the market. . With growing mobile data traffic, cloud-computing traffic and adoption of new technologies, there is a progressive demand for professionals who can sift through this goldmine of exponentially increasing data and help in informed decision-making.

Very often though we land up using the terms data science, data analytics and machine learning synonymously. It is worth exploring the difference between data science and machine learning in detail.

What is Data Science?

“Work that takes more programming skills than most statisticians have, and more statistics skills than a programmer has.”

Data science is a multidisciplinary field that includes aspects such as data analytics, data engineering, machine learning, predictive analytics, and more. It involves the collection, and transformation of large amounts of data, collectively known as big data. Simply put, data science is responsible for bringing structure to big data. Data analytics and machine learning are two of the many tools and processes that data science uses to make sense of random data clusters.

A data scientist, therefore, can provide accurate predictions and insights, which, in turn, can power critical business decisions. To understand a little more about the skills a data scientist uses, it will be worthwhile to study this Venn Diagram created by Hugh Conway, that has since seen many other versions from several data scientists.

data science vs machine learning

As the diagram shows, an extraordinary data scientist needs to be at the cusp of subject expertise, statistical knowledge as well as hacking skills. Data science actually is quite a multidisciplinary field that requires the following skills.

Requisite Skills for Data Scientists:

  • Statistics

  • Data mining

  • Data visualization

  • Unstructured data management techniques

  • Programming languages such as R and Python

  • Understand SQL databases

  • Use big data tools like Hadoop, Hive

What is Machine Learning?

Machine learning refers to a group of techniques used by data scientists that allow computers to learn from data. Traditional machine learning software is made up of statistical analysis as well as predictive analysis.

Machine learning lends itself to test many solutions from the available data and finding the best fit for the problem at hand. You can rely on machine learning to make predictions about complex topics, effectively. Little surprise then that machine learning finds application in a wide variety of industries such as:

  • Healthcare

  • Image and speech recognition

  • Product recommendations

  • Self-driving cars

  • Online fraud detection, and more

A relatable example to do with machine learning is social media. Think of your Facebook feed with recommendations of stories/videos based on your past behavior.

The various components of machine learning include:

  • Supervised Machine Learning

  • Unsupervised machine learning

  • Semi-supervised machine learning

  • Reinforcement machine learning

Skills Needed for Machine Learning Engineers include:

  • Fundamentals of Computer science

  • Statistical modeling

  • Data evaluation

  • Understanding and application of algorithms

  • Natural language processing

  • Data architecture design

To sum up, the difference between data science vs machine learning broadly include:

Data Science Machine Learning
It deals with extracting useful insights from data that aid decision making It is a field of data science that enables the machine to learn from past experiences.
It is a broad term that includes various steps from creating a model to deploying the model in problem solving It is used in the data modeling phase of problem solving
It can work with raw, structured, and unstructured data. It largely requires structured data to work on.

With both data scientists and machine learning engineers in demand, a detailed understanding of their roles can go a long way in helping you choose the career path of your interest. For additional information, you may have a look at the courses that we offer at Xebia Academy.

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