Posted on : 15 Dec, 2020, 11:43:18 AM
In this 21st century technology-driven world, Machine Learning and Data Science are the most popular domains that are in significant demand. All the sci-fi discoveries, innovations that you see in your environment are a contribution from fields like Data Science, Artificial Intelligence (AI), and Machine Learning.
In this blog, we will discuss Data science v/s Machine Learning in-depth, with their importance and distinction between data science and machine learning.
The following points will be discussed:
Before, understanding the term, Data Science, let’s first understand How data science came into existence? Remember, those days when most of the data was stored in excel sheets?
Those days were simpler, because, the generated data was lesser and structured. Back then simple business intelligence tools were utilized to analyze and process the data.
But now the situation is different. Over 2.5 quintillion bytes of data are created every single day and this number is only increasing per second. By 2020, it’s estimated that 1.7MB of data will be produced every second for every person on earth.
Now, can you imagine how much data that is? How are we going to process this much data?
Also, the data generated these days is mostly unstructured or semi-structured, and simple BI tools are not compatible with the job. We need more complex and effective algorithms to process and extract useful insights from the data. That is why data science comes in.
Data Science is all about discovering findings from data, by exploring data at a minute level to mine and understand complex behaviors, trends, patterns, and inferences. It is about surfacing the needful insight that can enable companies to make smarter business decisions.
Let’s take an example; you have surely binge-watched on Netflix or Amazon Prime. Both data mines movie viewing patterns to find what drives user interest and utilize that to make decisions on which both produce.
Similarly, online shopping portals identify each customer’s shopping behavior by drawing out patterns from their database; this helps them make better marketing decisions.
Now that you understand what is data science? And its importance, move ahead and discuss What is machine learning?
The idea behind machine learning is that you teach machines by feeding data and allowing them to learn their own, without any human intervention.
Assume being a fresher, you have enrolled yourself in dance classes. Initially, your performance would be Poor as you don’t know anything. But as you start observing and pick up more information, you would get better. Observing is another way of collecting data.
Humans tend to learn from their observations and experiences, similarly, machines are also capable of learning on their own when they are fed a good amount of data. This is exactly how machine learning works.
Machine Learning begins with reading and observing the training data to find useful insights and patterns to build a model that predicts the correct outcome. The performance of the model is then evaluated by using the testing data set. This process is carried out until; the machine automatically learns and maps the input to correct output, without human intervention.
Moving on, let us understand the distinction between the two;
As an aspiring, Data scientist or Machine learner, there would be one question that strikes you the most, which is the most well-known language required learning these two?
Many programming languages are used like; Python, R, C++. But python holds a unique place among all.
Python is an object-oriented, open-source, adaptable, and simple to learn programming language. It has a rich arrangement for tools and libraries that make the assignments easy to do.
Data scientists have been utilizing python for quite a while and it has become a top choice among data scientists and developers. Various data science consulting firms are empowering their group of developers and data scientists to utilize python as a programming language.
A Data scientist's job is to manage a large pile of data, also known as Big data. With simple utilization and a huge arrangement of python libraries, python has become a top priority to deal with and manage big data.
It is easy to learn a language; new data scientists can learn and utilize it very easily. Also, it gives a lot of data mining tools for better handling of the data.
Not only that, it provides better analytics tools, which is a necessary part of Data Science.
Python has a huge community base and significant for deep learning. No wonder, it has empowered the data scientists to accomplish more in a short period.
Moving further, just like data scientists, python has been widely used in the field of machine learning as well.
Python is a widely used high-level programming language that combines remarkable power with very clear syntax. It comes with modules, classes, exceptions, very high-level dynamic data types, and dynamic typing.
Also, there are interfaces to many systems calls and libraries, as well as to various windowing systems. New built-in models can be easily written in C or C++.
Python offers simple and consistent readable codes that help developers to focus all their effort on solving an ML problem rather than focusing on the technical nuances of the language.
Also, Python offers an extensive selection of libraries and frameworks which saves time in Implementing ML algorithms provides a well-structured and well-tested environment for developers to come with the best coding solutions.
In short, Python is a widely accepted language for teaching and learning MI and Data science. And if you are planning to make your career in fields like data science or machine learning, learning Python will give you an upper hand.
Various researches predict that employment opportunities are growing and will be abundant in the future.
According to Glassdoor, the national average salary for a Data scientist is 9, 00,000 INR, for a machine, learner engineer is 7, 68,375 INR.
So, the future is going to be bright for both Data scientists and machine learners.
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