In this tutorial, we'll learn The Obstacle of Big Data. Techniques to overcome 100+ million datapoints.
While surfing through the open data platforms for New York City, San Francisco, and Seattle, in search of a time series project that struck my fancy, I stumbled upon a behemoth — a dataset so large that I questioned my (and my computer’s) ability to handle it.
A bit of background: naturally, being what my friend sardonically refers to as a bookhead, I was drawn to library data, and I began to wonder if I could build a project around forecasting future library activity, i.e. the number of checkouts across an entire library system. To my dismay, both New York Public Library (NYPL) and San Francisco Public Library (SFPL) did not have any data regarding checkouts; but data ho! Seattle Public Library (SPL) did. And did it ever.
In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines. In this post, we'll learn Data Science vs Big Data: Difference Between Data Science & Big Data.
🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...
In this tutorial, we'll learn Python for Big Data: Top 12 Convincing Reasons To Choose Python for Big Data. What is Python? Python is a programming language that is most widely used in Data Science, Machine Learning,...
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
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