data science vs machine learning top differences to know

data science vs machine learning top differences to know

Data Science

Overview

In a world filled with technology and machines, Machine Learning and Data Science are the two technologies that plays a very crucial role in making the life much easier. Both these technologies are interdependent in nature. 

To be more precise, Machine Learning is a branch of Artificial Intelligence that makes the overall process simple. Now, let us get into the topic and get a deeper understanding. 

Data Science outline

In recent years, the demand for Data Science analytics is huge. Taking up this sector is quite challenging but the factor for interest always persists. Python online course makes your sustainability long in the industry. 

These are professionals who are capable of cracking complex data issues with their knowledge. There specialization cannot be constrained to any one sector as they are specialists in medicine, video processing, speech, and many more. 

Data Science is a various techniques for data analytics. Some of the technical skills involved are mathematics, analytical skills, communication skills, various programming languages and the list goes on.  

This technology is very influential over business and futurist analysis over environment as well due to the stats that is gathered using this. 

Data Science

Real-time implementation of Data Science

As you know Data Science involves numerous techniques for the proper analysis of data, its usage is also abundant in the world.

Some of the top examples are airline route mapping, gaming, advanced image recognition, augmented reality, internet search, target advertising, website recommendation, and even healthcare. 

Machine learning framework

In machine learning, there is no need for any explicit programming instead the machine learns everything on its own. When you get suggestions while watching movies on Netflix or listening to songs on YouTube Machine Learning comes into the big picture. 

This application provides the system ability to automatic learning along with improved experience. It is possible to generate any program by the integration of both input and output of the program. 

Machine Learning’s usage can be witnessed in the top-rated companies in order to give the user a better experience. 

Real-time example of Machine Learning

The most prevalent example is Amazon deploying Machine Learning for giving out recommendations in product choice according to their predicted preferences and Netflix, which is the most utilized platform to watch series amidst the younger generations makes use of this technique in providing better suggestions to the users for both movies and shows. 

The usage of Machine Learning can be witnessed in making various predictions or calculated suggestions that rely upon the information that require human intelligence. Machine learning has been used for waving off many struggles experienced by people with disabilities. Some of them are speech recognition technology that translates spoken words into text format, 

Difference Between Data Science Vs Machine Learning

Data Science Vs Machine learning

Data Science and Machine Learning are bound to each other. Machine Learning acts as a newly blooming technique in the market. 

When comparison is done between the salary paid for the developers Machine Learning Engineers are slightly paid high. However, the opportunity is in abundant for the Data Scientist.

This may be because a Machine Learning Engineer is associated with Artificial Intelligence which is a high-end technology. You can get more information regarding how to become a Data Scientist here. 

Data Science is deployed in the creation of insights from dealing with data such as understanding of the requirements, extraction of data and many more whereas Machine Learning is used for the  accurate classification with the help of historical data and mathematical models.

Data Science for beginners has horizontal scalable systems in order to handle humonius data whereas GPUs are chosen for the intensive vector operations. 

The input data generated in Data Science are human consumable that is to be analysed by humans. In Machine learning input data are transformed for the algorithms used.  

Components are present for managing unstructured data that are raw and in Machine Learning complexity is build-up with mathematical concepts and algorithms. 

Career path of a Data Scientist 

Even though the chance of getting into the field of Data Science is a bit difficult, the opportunities out there is abundant in nature. 

According to IBM’s prediction is by the year 2020, Data Scientist professionals will rise up to 2.73 million from 364,000. The whole process involves various Data Science tools to make the task simple. 

Data Science scope is considered to be among the fascinating profiles prevailing in the IT sector. Though the Data Scientist needs to acquire a lot of knowledge and skills to sustain in the sector, the job still has some magical factor to drag people. There are availability of both online and offline Data Science courses in Crampete. 

There are numerous roles that as a Data Scientist you can take up namely speech analytics, video processing, material simulation, image processing, medicine simulations and many more. It is indeed necessary to invest your time for the thorough preparation in order to become a Data scientist. 

In India, a Data Scientist gets a lump amount of salary that average around $20,000 annually. Choose Crampete to join our Data Science online course for your own benefit and learn at your new place. The roles that are to be taken up by a Data Scientist are:

Selecting features, optimizing classifiers, obtaining and recognizing of data, data mining, trends pinpoint,patterns in complex data sets, usage of Deep Learning frameworks such as Tensorflow, Keras and Theano.  

Data Science Online Course

Career path of a Machine Learning

If you are choosing Machine Learning as the career then it begins with Engineer position who’s responsible for the development of applications for the performance of common tasks without the occurrence of any errors.

The next level is becoming an Architect who indulges in the process of designing and developing prototypes for the application that is being developed. 

The various roles applicable to pursue by a Machine Learning Engineer are Engineer, Senior Machine Learning Engineer, Lead Engineer, MAchine Learning Engineer Front office, Back office, Data Scientist, PRincipal Engineer, Data Scientist IT, and many more. 

This background requires a strong foundation in the core knowledge of concepts over Computer Science, and Mathematics along with Statistics.

A Machine Learning Engineer earns a pay of $100,956 annually according to a top Ameican website Payscale.com. The field comprises of candidates with young and fresh mind as there is none with experience over 10 years. 

In India, the average pay for this field is $12,931 as per the information related to salary mentioned in US’s website Glassdoor.com.

As a Machine Learning Engineer you will getting both vertical and horizontal growth. However, the salary may vary but it will be quite a good amount when compared to other sectors. 

Opportunities

If we look into the job perspectives of a Data Scientist, you will level up to the next stage as senior data scientist. A Data Science salary in India goes beyond $20,000 annually that is quite a good pay. 

The demand for Data Science techies is doubled this year with a handsome salary. Youths are attracted towards the sector due to  its wide usage.  

As per the statistics gathered from Indeed job site shows its massive popularity in the market. Out of top 25 job profile, 13 is bagged by Machine Learning with a growth rate of 344% last year. 

Summary

“Strive for progress not perfection”. 

By this time, you would have got a clear picture of both Data Science and Machine Learning. Hence, to conclude Machine learning is an approach to accomplish Artificial Intelligence that is nothing but a part of Data Science for the futuristic calculations with the help of patterns and algorithms. 

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