10%

Try : Insurtech, Application Development

Edtech(5)

Events(34)

Interviews(10)

Life@mantra(11)

Logistics(1)

Strategy(14)

Testing(8)

Android(46)

Backend(29)

Dev Ops(3)

Enterprise Solution(22)

Frontend(28)

iOS(41)

Javascript(13)

Augmented Reality(17)

Customer Journey(12)

Design(17)

User Experience(38)

AI in Insurance(31)

Insurtech(59)

Product Innovation(37)

Solutions(15)

E-health(3)

HealthTech(9)

mHealth(3)

Telehealth Care(1)

Telemedicine(3)

Artificial Intelligence(110)

Bitcoin(7)

Blockchain(18)

Cognitive Computing(7)

Computer Vision(8)

Data Science(14)

FinTech(45)

Banking(1)

Intelligent Automation(26)

Machine Learning(46)

Natural Language Processing(13)

The Importance of Machine Learning for Data Scientists

By :
3 minutes, 7 seconds read

The concept of Machine Learning, Artificial Intelligence (AI), Big Data has been around for a while. But the ability to apply algorithms and mathematical calculations to big data is gathering momentum only recently.     

In this article we will discuss the importance of Machine Learning and why every Data Scientist must master it.

What is Machine Learning?

Simply put, we’re contributing to Machine Learning through our day to day interactions on the internet. Whether you search your coffee maker on Amazon, “top tips to lose weight” In Google, or “friends” in Facebook you see Machine Learning in action, but you don’t realize it.

It is the Machine Learning technology that lets Google, Amazon, and Facebook search engine offer relevant recommendations to the user.

These companies are able to keep tabs on your day to day activity, search behavior and shopping preference with the help of ML technology.

Machine Learning is also one of the main components of Artificial Intelligence.

Who is a Data Scientist?

Before assessing the importance of Machine Learning for Data Scientists, here’s a brief note on who Data Scientists are. We’ll also discuss how one can become a Data Scientist.

Data Scientists draw meaningful information from a huge volume of data. They identify patterns and help build tools like AI-powered chatbots, CRMs, etc. to automate certain processes in a company.

With a sound knowledge of different Machine Learning techniques and contemporary technologies like Python, SAS, R, and SQL/NoSQL database, Data Scientists perform in-depth statistical analysis.

The role of Data Scientist might sound like that of Data Analyst, but, in fact, they are different.

Difference between a Data Scientist and a Data Analyst

  • Data scientist predicts future based on past patterns. Whereas, a Data Analyst curates meaningful insights from data.
  • Data scientist’s work involves “estimation” (or prediction) unknown facts; while an analyst investigates the known facts.
  • Data Analyst’s job is more geared towards businesses. Data Scientists’ work is integral to innovations and technological advances.

Why Machine Learning is So Important for a Data Scientist?

In a near future, process automation will superimpose most of the human-work in manufacturing. To match human capabilities, devices need to be intelligent and Machine Learning is at the core of AI.

Data Scientists must understand Machine Learning for quality predictions and estimations. This can help machines to take right decisions and smarter actions in real time with zero human intervention.

Machine Learning is transforming how data mining and interpretation work. It has replaced traditional statistical techniques with the more accurate automatic sets of generic methods. 

Hence it is imperative for Data Scientists to acquire skills at Machine Learning.

4 Must Have Skills Required to Become a Machine Learning Expert

To become an expert at Machine Learning every Data Scientists must have the following 4 skills.

  1. Thorough knowledge and expertise in computer fundamentals. For example, computer organization, system architecture and layers, and application software.
  2. Knowledge of probability is very important because Data Scientists’ work involves a lot of estimation. Analyzing statistics is another area that they need to focus on.
  3. Data modeling for analyzing various data objects and how they interact with each other.
  4. Programming skills and a sound knowledge of programming languages like python and R. A quest for learning new database languages like NoSQL apart from traditional SQL and Oracle.

Conclusion

Data is the new oil.

IBM predicts that the global demand for Data Scientists will rise 28% by 2020. Finance, Insurance, Professional services and IT sectors will cover 59% of the Data Science and Analytics job demand.

In the coming future, Machine Learning is going to be one of the best solutions to analyze high volumes of data. Therefore, Data Scientists must acquire an in-depth knowledge of Machine Learning to boost their productivity.   

This article is contributed to Mantra Labs by Jenny Hayat. Jenny is an established blogger and content writer for business, career, education, investment, money-making ideas and more.

Cancel

Knowledge thats worth delivered in your inbox

Personalization in Mobile UX

By :
4 minutes read