Analytics Vidhya

APRIL 28, 2023

Introduction If you work with programming languages and are familiar with Python, you must have had a brush with Pandas, a robust yet flexible data manipulation and analysis library. It was founded by Wes McKinney in 2008. appeared first on Analytics Vidhya.

Data Analysis 328

Data Analysis Data Analysis Python Analytics 328

Analytics Vidhya

MARCH 27, 2023

Introduction Could the American recession of 2008-10 have been avoided if machine learning and artificial intelligence had been used to anticipate the stock market, identify hazards, or uncover fraud? The recent advancements in the banking and finance sector suggest an affirmative response to this question.

Machine Learning 380

Machine Learning Machine Learning Artificial Intelligence Artificial Intelligence 380

Analytics Vidhya

JULY 17, 2024

Until the release of NumPy in 2005, Python was considered slow for numeric analysis. Pandas, in 2008, made Python the best language […] The post Fundamentals of Python Programming for Beginners appeared first on Analytics Vidhya. But Numpy changed that.

Python 285

Python Analytics Analytics Data Analysis 285

Dataconomy

APRIL 3, 2025

t-SNE (t-distributed stochastic neighbor embedding) has become an essential tool in the realm of data analytics, standing out for its ability to unravel the complexities inherent in high-dimensional data. t-SNE was developed by Laurens van der Maaten and Geoffrey Hinton in 2008 to visualize high-dimensional data.

Clustering 91

Clustering Exploratory Data Analysis Data Analysis Data Analysis 91

Smart Data Collective

OCTOBER 31, 2022

Human analysts are able to incorporate these emotional responses into their stock predictions, combining them with trend data to produce relatively accurate analytics. Fortunately, the first robo-advisors were created in 2008. Unfortunately, even the same trends can have different interpretations from multiple analysts.

AI 145

AI AI Artificial Intelligence Artificial Intelligence 145

Tableau

DECEMBER 1, 2021

In this blog post, I'll describe my analysis of Tableau's history to drive analytics innovation—in particular, I've identified six key innovation vectors through reflecting on the top innovations across Tableau releases. Then, we’ll end with my observations of two lessons for future analytics innovation. Self-service Analysis.

Tableau 145

Tableau ML ML Database 145

Kaggle

APRIL 20, 2020

I’m also a part-time software developer for 11ants analytics. In 2009 and 2010, I participated the UCSD/FICO data mining contests. After the first 10 testing submissions, I realised that there was a concept drift happening between 2007 and 2008. What tools I used Software/Tools used for modelling and data analysis: Weka 3.7.1

Algorithm 61

Algorithm Data Mining Data Mining Data Mining 61