New technologies like AI, Data Analytics, and Machine Learning have dominated almost every field in today's ever-evolving high-tech world. With IT companies introducing innovations on a constant basis, the scope for the development of new technologies is limitless. Organizations are already harnessing the potential of AI and Machine Learning to streamline processes internally and analyse information on everything from customer habits to building a knowledge pool to ensure their overall growth. According to the data collected by The International Data Corporation, the AI market can touch up to 7.8$ billion in India by 2025. From a career perspective, through 2023 it is expected that the ML Engineer will be the fastest growing role with open positions for ML engineers at fifty percent of that of data scientists which were less than 10% in 2019.

Data Science is a coalescence of sundry fields including Statistics, math, Programming, Machine Learning, and domain Erudition with the goal of extracting insights from the data to enable a data-driven decision process, which is the key to business prosperity. Data Scientists accumulate the pertinent business data from sundry internal and external sources, do experiments, and apply sundry statistical techniques to engender vigorous data substratum analytics. They utilize machine learning alimented by data pipelines to provide predictive analytics with a great level of precision. This avails to better understand the business and customers so that they can be accommodated better with a better decision-making process. Why is a Data Science Vocation most desired?

Described as the Airbnb for data scientists, Kaggle is a crowdsourcing platform for aspirants to nurture, train and challenge their learnings. The search for "Kaggle" has increased by 55 percent over five years, and the platform has over 8 million users across 194 countries. While the platform trains several aspirants, it also has many established data scientists. Analytics India Magazine analysed the top 100 Kaggle grandmasters as of April 2022 to explore the top companies represented by them. Here's the latest breakdown of what users do on Kaggle.

At the start of this year, I published a mind map on the Data Science learning roadmap (shown below). The roadmap was widely accepted, that article got translated into different languages, and a large number of folks thanked me for publishing it. Everything was good until a few aspirants pointed out that there are too many resources and many of them are expensive. Python programming was the only branch that had a number of really good courses, but it ends right there for beginners. Answers to a lot of these questions can be found in the book Deep Learning by Ian Goodfellow and Yoshua Bengio.But that book is a bit too technical and math-heavy for many.

Are you planning to start a career in Artificial Intelligence and Machine learning? Well, then this article is for you. Building a career in AI and ML is not easy nor hard either. But it requires a dedicated approach. Sometimes when you're from an IT background, you may feel like swapping the career options too, because of the diverse opportunities.

The ability to extract insights from massive amounts of data decides your enterprise's success. This is where data scientists and analysts interpret data and derive insights to help identify opportunities and make strategic decisions. For effective analysis of data, data scientists need to be equipped with the best tools for analyzing, reporting, and visualization. Languages such as C, C, Java and Javascript help understand data. That's a tricky question to answer.

Data Science is a gift to the modern world. The technology complements the existing data sources by making use of them. Recently, data science is being widely adopted by organizations to make predictive decisions on their behalf. Data science is a blend of various tools, algorithms and machine learning principles with the goal to discover hidden partners from raw data. The technology is primarily used to make decisions and predictions making use of predictive casual analytics, prescriptive analytics and machine learning.

Terms like'Data Science', 'Machine Learning', and'Data Analytics' are so infused and embedded in almost every dimension of lifestyle that imagining a day without these smart technologies is next to impossible. With science and technology propelling the world, the digital medium is flooded with data, opening gates to newer job roles that never existed before. However, quite often it is witnessed that beginners get confused over similar terms being used interchangeably, like'Data Science' and'Data Analytics'. This post will give you a clear idea about what some of the prominent concepts and job roles in Data are, and how they differ from each other! The most popular field that has emerged in the wake of digital disruption is'Data Science'. Data being oil and fuel of all the operations, companies are making the most of the accessible data that had never been used before.

Proficiency in mathematics is essential for aspirants to get started with their data science journey. A strong foundation in mathematics will help beginners to not only learn existing and new machine learning techniques easily but also differentiate themselves from others in the competitive market. Consequently, data science aspirants must ensure that they master algebra, calculus, probability, among others before diving deep into machine learning. Here are top courses on mathematics that aspiring data scientists must take into account while devising their learning strategy. The five-week-long course on Coursera can be the starting point for learners as linear algebra has a wide range of applications in data science practices.

"Whenever you compete, you have to accept simple rules – someone wins, someone loses, and usually the winner takes it all." For this week's ML practitioner's series, Analytics India Magazine got in touch with Oleg Yaroshevskiy from Ukraine. In this interview, he shares his experiences from his journey to the top 20 in one of the toughest data science competitions in the world. Oleg majored in maths and statistics from Cybernetics Faculty of Taras Shevchenko National University of Kyiv, which was co-founded by Victor Glushkov, one of the cybernetics pioneers who played a key role in the advancement of theoretical computer science, including artificial intelligence. Oleg had a formal introduction to machine learning (ML) during his graduation days where he had studied neural networks along with the popular Andrew NG's course on Coursera back in 2013.