Towards AI

JUNE 25, 2024

As a Python user, I find the {pySpark} library super handy for leveraging Spark’s capacity to speed up data processing in machine learning projects. But here is a problem: While pySpark syntax is straightforward and very easy to follow, it can be readily confused with other common libraries for data wrangling. Let’s get started.

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ML ML EDA Data Wrangling 75

Pickl AI

APRIL 21, 2025

Introduction In today’s hyper-connected world, you hear the terms “Big Data” and “Data Science” thrown around constantly. They pop up in news articles, job descriptions, and tech discussions. What exactly is Big Data? This is where Big Data often comes into play as the source material.

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Pickl AI

NOVEMBER 4, 2024

This article will guide you through effective strategies to learn Python for Data Science, covering essential resources, libraries, and practical applications to kickstart your journey in this thriving field. They introduce two primary data structures, Series and Data Frames, which facilitate handling structured data seamlessly.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Becoming Human

MAY 15, 2023

You have to learn only those parts of technology that are useful in data science as well as help you land a job. Don’t worry; you have landed at the right place; in this article, I will give you a crystal clear roadmap to learning data science. Because this is the only effective way to learn Data Analysis.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

JULY 20, 2023

Text Analytics and Natural Language Processing (NLP) Projects: These projects involve analyzing unstructured text data, such as customer reviews, social media posts, emails, and news articles. NLP techniques help extract insights, sentiment analysis, and topic modeling from text data.

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Analytics Analytics Big Data Big Data 40

Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

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