How is Machine Learning Important for Data Scientists?


Relationship between Data Science and Machine Learning:

As we all know that there is a huge fizz going over the terms Big data,Data science, Data Analysts, Data Warehouse, Data mining and many more like this. This is really a clear indication towards the fact that in the current era data plays an important role in influencing everyday activities of mankind. Everyday we are generating around 2. 5 quintillion( 10¹⁸) bytes of data including our text messages, images, emails, till data from autonomous cars, IOT devices etc. With such huge amount of data being available on hand, grasping useful information from this data can help each and every organization a lot, for getting a clear insight on several areas like, what can bring a boost for their organization’s revenue, which field requires focus, how to get more customer’s attention etc.

What is Data Science

Data Science is being defined in several ways for over a decade now, and the best way to answer the riddle is probably through a Venn diagram which was created by Hugh Conway in the year 2010, who explained this by a Venn diagram which consists of three circles including- maths and statistics, subject expertise (knowledge about the domain to abstract and calculate) and hacking skills. Surprisingly enough if you can do all three, you are already highly knowledgeable in the field of data science.

Connection between Data Science and Machine Learning:

Data science is built on a concept used to tackle big data and includes data cleansing, preparation, and analysis procedures. First, a data scientist collects data from a number of sources and then applies them to machine learning. He uses predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They struggle to understand data from a business point of view and are able to provide accurate predictions and insights that can be used to power critical business.

What skills make you a competent Data Scientist?

People who are interested in building a strong future career in this domain should master key skills in three domains: analytics, programming and domain knowledge. So if we go one step further, the following skills will help us carve out a niche as a data scientist:

1 Strong knowledge of programming languages like Python, SAS, R, Scal.

2 Hands-on experience in SQL database coding

3 Potential to work with unstructured data from various sources like video and social media

4 A good Understanding of multiple analytical functions

5 Awareness of machine learning

Responsibilities of a Data Analyst:

A data analyst is usually defined as a person who can do basic descriptive statistics, visualize data and communicate data points for deriving conclusions. He must have a basic understanding of statistics, a very good knowledge of databases, the ability to craft new views, and the perception to visualize the data. Data analytics can be defined as the basic level of data science.

How Machine Learning actually works:

Machine learning can be defined as the practice and knowledge of using algorithms to use data, learn from it and then eventually forecast future trends for that topic. Traditional machine learning software consists of statistical analysis and predictive analysis that spot patterns and catch hidden insights based on perceived data. Spell model serving has so much to offer if your business needs a machine learning system. A good example of machine learning application is Facebook. Facebook’s machine learning algorithms works by gathering behavioral information for every user using the social platform. This is purely based on the users past behaviours, the algorithm exactly predicts interests and recommends articles and notifications on the News Feed. in the same way, when Amazon recommends “You might also like” products, or when Netflix recommends movies based on past behaviors, machine learning is at work in the background.

Skills required for a good Data Scientist

Machine learning has a different perspective on statistics. Following are critical skills that can help you carve out a career in this fast-growing domain

1 You should have expertise in computer fundamentals

2 A deep knowledge of programming skills

3 A good knowledge of probability and statistics

4 A good grip on data modeling and evaluation skills

Is Data Science and Machine Learning belong to the same fields?

Many people get confused while defining the both Machine Learning and Data Science. Let’s make it a little easier to understand. As we know that data science is a broad term for multiple disciplines, and machine learning fits within data science. Machine learning deploys various techniques like regression and supervised clustering. On the contrary, ‘data’ in data science may or may not progress from a machine or a mechanical process. So, the main difference between the two is that data science as a broader term that not only focuses on algorithms and statistics but also takes care of the entire data processing and methodology.


Scope of Data Science and Machine Learning:

Data science, analytics, and machine learning are growing at an amazing pace and more and more companies are now looking for professionals who can screen through the goldmine of data and help them drive swift and progressive business decisions in a good way.

Machine learning, Data Science and Artificial Intelligence are some of the most in-demand and fast growing domains in the industry right now. A perfect combination of the right skill sets based on real-world experience can help you secure a strong career in these trending domains. IBM in its prediction says that by 2020, the number of jobs for all U. S. data professionals will increase by 364, 000 openings to 2, 720, 000. So this prediction makes data science such an exciting field and that skills will help professionals gain a strong foothold in this fast-growing domain.

Best Training for Data Science:

At Knowledgehut, students get opportunity in learning application of data science and deep learning algorithms through case studies and hands-on projects. So if you want to take a step back and look at these jobs, you’ll get guidance from an experienced machine learning engineer as your instructor in this training. Best part about this training is, you will get a certificate too on course completion.

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