A quick look around the internet shows that people are donning a lot of different hats. Some claim to be "data scientists" yet fail to mention their background — which is the most important part — and others who might be confused by the term "data science" take pride in labeling themselves as "artificial intelligence" or "cognitive science". Yet, no matter how they'd like to describe themselves, few of them have got much insight into what a data scientist actually does.

But as with most professions, what makes for a good data scientist isn't so cut-and-dry. 

Here are some facts to know about the anatomy of a data scientist.

Who are Data Scientists, anyway?

A data scientist is a person who uses their knowledge of mathematics, statistics, and programming to solve problems with data. They work in tandem with the rest of a company's analytics team to find new ways to analyze data and then use that analysis to make decisions about improving the company's processes or products. They can help develop data models, create software applications based on those models and help build algorithms that can give insights into the underlying data structure. Other duties include: 

He takes care of multiple projects and simultaneously filters, cleans and formats the required data so that different machines can analyze it. 

He prepares reports as per the client's needs to have a better understanding of what is actually happening in their business. 

Since businesses today depend largely on numbers and statistics to decide their future moves, having a professional capable of managing that will certainly be advantageous for them over time. 

It goes without saying that a data scientist must be equipped with timely knowledge from various domains to work successfully.

Roles and Responsibilities of a data scientist 

As data science roles continue to increase across industries and continents, inspiring new careers and opportunities, it is important to understand how these roles are organized and the tools professionals use. However, the job description for a data scientist is not very specific because there are many different paths you can take. Besides the ability to collect valuable data, there are differences in responsibilities between teams, and a role needs to adapt to them.

Here are the main responsibilities performed by them: 

Data Management – He helps the development of the foundation of technical and technological skills within the Data and Analytics sector to support various ongoing and planned data analytics initiatives. The Data Scientist has little administrative role in this process.

Analytics – A Data Scientist performs a scientific function in which he develops, applies, and evaluates advanced statistical models and approaches to solve the company's most challenging problems. The data scientist creates econometric and statistical models for various issues, such as simulations, projections, classification, clustering, pattern analysis, etc.

Strategy/ Design – A data scientist makes data-driven decisions using ML models and strategies. He uses ML algorithms to make predictions, perform statistical analysis, and then make decisions accordingly. 

Collaboration with a team – As a data scientist, you discuss and collaborate with team members and stakeholders. This allows them to communicate the findings easily with clients.

Why is Data Science the hottest career choice?

Data scientists and other data professionals are considered hot professions of today's decade due to the following reasons: 

After the US, India is the second-largest global center for analytics.

All industries are experiencing a rise in job postings, including banking, energy and utilities, retail, travel, and healthcare.

Since there has been a significant increase in these professions, demand has outpaced supply.

These highly sought-after scientists earn an average starting salary of more than Rs. 10,00,000 per annum.

Qualifications and Skills Requirement 

Most data science positions demand a bachelor's degree in a technical subject as a bare minimum. However, advanced degrees in mathematics, computer science, statistics, or data science are more commonly held by data scientists.

This educational background offers aspiring data scientists a good foundation and teaches them the fundamentals of data science and Big Data skills necessary to excel in their careers. However, many institutions are offering online training, such as Learnbay’s data science course in Canada developed by IBM. These courses provide real-world learning strategies you won't find in a textbook, like hands-on learning of in-demand data science skills, Capstone projects, and dedicated mentorship that help students prepare to become data scientists in a competitive world.

Moving on, Let us now discuss the top skills required to work as a data scientist. 

As a Data scientist, you need to efficiently accomplish various complicated planning, modeling, and analytical tasks. After all, that's the reason they are called a jack of all trades. As a result, this role requires expertise in a variety of data science tools and libraries, big data platforms including Spark, Kafka, Hadoop, and Hive, as well as programming languages like Python, R, Julia, Scala, and SQL. 

Technical Skills include:

Mathematics ( Statistics and Probability) 

Programming languages ( Python, R, and SQL)

Data Mining

Predictive Modeling

Data Wrangling

Machine Learning and Deep Learning ( Regression, Classification and NLP)

Data Visualization

Non-technical Skills include: 

Business Acumen 

Communication and presentation skills 

Data Storytelling 

Data intuition and critical thinking ability 

So what are the various roles apart from data scientists? 

The demand for skill sets like data analytics, data scientists, data engineers, and machine learning is growing rapidly as AI becomes more prevalent across all industries. The following are some of the roles in the data science sector:

Data Analyst – Although they are not the same, some people might conflate data scientists and data analysts. Many of the skill sets are similar, yet there are also big differences. A data analyst is skilled in programming languages like R, Python, and SQL. Among the main duties are collecting, processing, and analyzing data using statistical techniques.

Data Engineer - He usually has technical expertise and experiments with databases and powerful processing systems.

ML engineer – Your final result as a machine learning engineer, will be software that can operate on its own with no oversight or human involvement. They specialize in programming machines to carry out certain duties since they are computer programmers. For instance, a machine learning engineer might work on a robot or self-driving car.

Bottom Line! 

The future of data science and AI will only get brighter as the technology becomes more advanced. While it may be one of the newer sciences, data science is already making waves in various industries, with businesses utilizing the fruits of this labor to improve their bottom lines and increase profits. Even now, there are new innovations in the works, and improvements are being made daily to this technology.

As a result, the role of the data scientist will be more relevant than ever going forward, and the demand will only continue to grow. This means that being a data scientist could be a viable career path for anyone interested in tackling difficult problems and getting ahead of the curve! Click here to learn more about the data science courses in Canada available for aspiring professionals of all domains.