Know the relevant career opportunities in Data Science

Machine learning, artificial intelligence are buzzwords in the business world. In very simple terms, machine learning is the art and science of creating programs that learn from data processed by the program. With machine learning, programs should get better at performing a task as they learn from the data. In other words, machine learning programs are very good at learning patterns in data, and based on these patterns, a machine learning program makes decisions on the fly.

A new role in high demand

Machine learning has revolutionized several fields of application. Some of the most popular are computer vision and voice recognition. However, we are still at an early stage of building apps that apply machine learning algorithms. Just as in the 1990s HTML was a hot technology that powered the entire internet, we seem to be at a similar stage with machine learning and its superset called artificial intelligence. Creating machine learning applications is the responsibility of a new type of employee called the “Data Scientist”. While software developers and statisticians have been around for at least three decades, Data Scientist is a relatively new role that many companies are actively hiring for.

What qualifications are required?

The qualifications of a data scientist are mainly related to computer science, statistics and mathematics. In fact, most companies are willing to hire people qualified in one or more of 3 areas: computer science, statistics and mathematics. A good data scientist is part software engineer, part mathematician, and part statistician. Additionally, a key skill a data scientist must have is the ability to write code. It’s not an easy role to take on simply because having a degree in computer science isn’t enough to work as a data scientist. A data scientist must also have a superior understanding of statistics and mathematics and must be able to apply them in the field of data science. Although a data scientist does not need to be a mathematician or a statistician, he/she must have a superior grasp of these subjects to succeed as a data scientist.

Aditya Narvekar

Value for money

The fact that being a data scientist requires expertise in computer science, math, and statistics means companies are willing to pay top dollar for well-qualified people. According to a survey conducted by the IEEE in the United States, data scientists had a median salary of 164,500 in 2020, an increase of 8% from the median salary of 152,500 in 2019.

Data Scientist must specialize in many areas

Data Scientist is a relatively new role that has come to the fore over the last 10 or so years. With the rise of internet companies like Amazon, Facebook, and Google, increasingly large datasets have been used by these companies in creating predictive models. Data science professionals are now broadly categorized into 2 groups: Data Scientist and Data Engineer. Whereas data engineers are people who specialize in capturing data and then extracting it into an easily usable format to create predictive models. Data Scientists, on the other hand, use the datasets created by Data Engineers to build predictive models. In terms of skills, Data Engineer should have a better understanding of distributed computing which is required to process large datasets while a Data Scientist should specialize in understanding different types of models and their application to various problems.

Young industry with a lot of potential

Organizations are still relatively data illiterate and data science is still a relatively young industry. As data science continues to grow, it will be important to acquire and retain experienced and skilled data scientists and engineers. By 2025, Humanity will generate 175 Zeta Bytes of data, so it is very likely that the industry will have a strong demand for data scientists and data engineers to mine this data to create applications that will solve many of our problems. The most common.

The author is Assistant Professor of Data Science at SP Jain School of Global Management.

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