The field of Data Science and Data Scientist is emerging all the time and will be having an upper-hand for the next 10 years. So obviously there will be a demand for Data Scientists in the next decade, and Data Science jobs will one of the best jobs in the future.

One of the main reasons for people to hit on Data Science is that the lucrative salary that Data Scientist bags, the future requires Data Science a lot.

If you want to build a career in the field of Data Science then you need to work to have a better foundation of the subject, once you’re strong in the basics then you’re all set to go. Here we Demystifying the Job Role of a Data Scientist, roles and responsibilities.

Who is a Data Scientist?

Most commonly people presume that Data Scientists are deep thinkers with intellectual ideas, but Data Scientist is one who has an attitude to learn new things by asking questions, inventing/discovering new things, and learning from that.

Breaking complex problems and solving them is part of their daily work, one of the biggest misconceptions about to become a Data Scientist is that to have a masters in Science and Mathematics, but this leads to missing the fact that Data Science is a multidisciplinary field.

A focused study in academics will certainly help to become a Data Scientist but doesn’t promise to land a job. For example, a highly qualified Ph.D. Mathematician would still need to pick some programming language to perform those tasks.

Data Analytics and Machine Learning

Analytics, the term has gained quick popularity these days where people interpreted it to critical/logical thinking. But technically, analytics refers to ‘science of analyses – a practice of analysing to make better decisions.

'Analytics' can be understood with different meanings based on the context, sometimes it describes all the idea that has been discussed above and sometimes it means different where a Data Scientist would be using a raw data to build a predictive algorithm which falls into the scope of analytics.

Undergoing Data Science training in Chennai will help you to get the concepts of Data Science and the algorithms.

Let’s understand the difference between a Data Analyst and a Data Scientist

'Analyst' is an arguable job title that can be used to represent different types of job functions and roles, such as data analyst, operations analyst, marketing analyst, financial analyst, etc.

But how does this compares to a data scientist?

Let’s just understand the roles and responsibilities of a Data Scientist and Data Analyst, a Data Scientist has some specialty roles that have hands-on in maths, business, and technology.

Data Scientist works at a database level to extract the insights to build the product or to provide a solution.

Whereas in the case of Analyst, that comprises a lot of work like organizing the data or to assist or to provide the data for a Data Scientist. Anyhow, when looked into the hierarchy in the field of Data scientists would be having an upper hand over the Data Analyst.

What is Machine Learning?

Machine Learning is a discipline that is closely related to the field of Data Science, machine learning uses methods that revolve around data modeling,

  1. Predicting using an algorithm.
  2. Deciphering the patterns using the algorithm.

The former one has a concept of using tagged data (ground data that is already known) to train predictive models, for example, credit card fraud detection can be detected with the help of the historical data that is acquired from the previous information.

The later one is another paradigm that is known as unsupervised learning, where no observations are tagged. Among the broad category of methods, the most commonly used are clustering and techniques.

The future has got a fair amount of dependency on Data Science Training as there would be a high demand for predictive analysis from a business perspective.

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