# The Difference Between Data Science And Machine Learning

Whether it is Apple’s Siri or Amazon’s Echo, Artificial Intelligence and machine learning is slowly taking over our lives as modern-day assistants. If you look at the larger picture, AI is also becoming a part of every growing business, with more people getting acquainted with technical terms like big data, data sciences, and machine learning, and using them to solve complex analytical problems.

With ample data to process, companies use data science techniques in discovering, understanding and analyzing the complex, raw data resting in their databases. Machine learning is a part of data science, uses algorithms and statistics to understand the extracted data. While both data science and machine learning differ in functionality and purpose, you may often confuse the two to be aspects of the same technology; this post aims to break down the difference between data science and machine learning and their applicability.

**Understanding Data Science**

Picture a scenario where you are asked to use technology and solve an imminent business problem. Where would you start? You’d probably start by identifying the problem first so that you get a clearer perspective of how to solve it. This is where data science fits the bill!

Data science is an extensive study of data. It is used for analyzing and processing data through algorithm developments, data inference to simplify complex analytical issues and extract information. Have you noticed how after you’ve looked at a particular product on Amazon, multiple ads of the same product pop up on your screen when you’re catching a show on YouTube or Netflix? That’s data science doing its job for you! In simpler terms, data science uses data, both in streaming and raw format to generate business value.

**Skills required in the field of data science**

To explore career prospects in data science, here are a couple of required skills:

**Expertise in mathematics**

There are multiple facets of data, including correlations, textures, and dimensions that need to be expressed mathematically or statistically. For building a data product and lending data insights, expertise in mathematics is given must.

**Hacking and technology expertise**

Breathe! By hacking, we don’t mean breaking into someone’s computer. It essentially means applying your ingenuity and creativity to manipulate technical knowledge and find solutions to build ideas and products for businesses.

**Strong strategy or business acumen**

Among the crucial skills for any data scientist is to be proficient in tactical business. It is necessary to be competent in tackling data to cogently offer a solution or offer a more cohesive narrative of a complex issue and a solution for the said problem.

**Understanding Machine learning **

Machine learning is a branch of Artificial Intelligence that enables a computer to learn automatically from experience with any kind of human intervention. The whole concept of machine learning revolves around determining answers to obstacles without human interference, which begins with understanding data from examples or direct experiences, analyse data patterns and make better decisions based on the deductions.

It is best used for problem-solving when there are extensive data and variables without using existing algorithms. For example, Google tends to optimize search results and pops up advertisements of products that are either similar to your taste or websites that you had previously visited. It studies the behavior of a user and shows results accordingly.

**Skills required for machine learning**

A professional interested in the field of machine learning needs to be skilled in the following:

**Expertise in probability and statistics**

A deep understanding of algorithms, expertise probability of drawing inferences from data and make predictive models, using statistics to understand p-values and solve confusion matrices are crucial in the field of machine learning.

**Knowledge of programming languages**

Machine learning without programming languages is as good as an empty glass! Extensive knowledge of programming languages like C++, Python, Java, R and more is crucial.

**Data modelling and evaluation skills**

Any machine learning process is incomplete without the evaluation of a given data model. To be skilled in machine learning, a professional needs to possess an understanding of how data modelling works, what accuracy measures would be appropriate for a given error and also have a working evaluation strategy.

**Additional skills**

Apart from these skills, being in sync with the latest development tools, algorithms, and theory can come handy too. Reading papers on Google Big Table, Google File System, Google Map-Reduce can be useful.

**Conclusion**

Machine learning is a component of data science; where data science as the larger picture comprises of big data, data learning, statistics and much more. Machine learning involves the use of programming and computational algorithms to arrive at a conclusion, whereas data science uses numbers and statistics to bring a result.

For companies that are more data-driven, switching to data science is a secret mantra for enhancing business and for targeting better returns on investments. Machine learning, on the other hand, in today’s date, is essential since it can solve intricate and complex computational problems by breaking them down into bits.

This article is an updated version of the article titled – **What Is The Difference Between Data Science And Machine Learning?**