Businesses these days are generating and consuming a huge amount of data. This data varies in variety, volume, velocity, and veracity (4Vs of big data). Consequently, this trend has led to organizations’ rapid adoption of different AI and ML tools and techniques. Hence, converting a business problem to a machine...
Natural Language Processing has gained a lot of popularity owing to an exponential increase in unstructured data generation. Here are some fun data to give you context – The world sees 682 million tweets per day There exists 1.2 million new data producing social media users each day Up to...
Data distributions lie at the heart of all the machine learning algorithms and data science techniques. A machine learning algorithm is only as good as the data it gets. Hence, it is important to fully understand the data and data distributions before we build our models. Consequently, explore and understand...
Data Science and storytelling are inseparable. And the bridge between the two is data. I had talked about storytelling in my article about Business Analyst/Data Scientist learnings but thought it would be better to elaborate more about the storytelling part. Imagine you are a data science executive in an organization....
Confused between business analyst or data scientist? Does a business analyst build machine learning models? How much should a data scientist know? In this article, we try to find the answer and end this mystery.
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