CIO Business Intelligence

JUNE 7, 2022

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? Data analytics and data science are closely related.

Data Analytics 127

Data Analytics Diagnostic Analytics Management Analytics 127

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 122

Predictive Analytics Analytics Predictive Modeling Data mining 122

Rocket-Powered Data Science

APRIL 25, 2019

A few years ago, I generated a list of places to receive data science training. Learn the what, why, and how of Data Science and Machine Learning here. That list has become a bit stale. Follow Kirk Borne on Twitter @KirkDBorne. Follow Kirk Borne on Twitter @KirkDBorne.

Data Science 38

Data Science Machine Learning Deep Learning Statistics 38

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Smart Data Collective

APRIL 3, 2020

Big data is driving a number of changes in the business community. Some of the benefits of big data incredibly obvious. However, there are also a lot of other benefits big data creates that don’t get as much publicity. Big Data is the Future of Giveaway Offerings. Chatbots for Giveaways.

Big Data 80

Big Data Data-driven Data mining Software 80

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. Workshop on Meta-Learning (MetaLearn 2018).

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? In the age of big data, companies are always on the hunt for advanced tools and techniques to extract insights from data reserves.

Data mining 57

Data mining Machine Learning Deep Learning Marketing 57

Rocket-Powered Data Science

DECEMBER 15, 2018

In deep learning, as in typical neural network models, the method by which those adjustments to the model parameters are estimated ( i.e., for each of the edge weights between the network nodes) is called backpropagation. .” NOTE: Genetic Algorithms (GAs) are an example of meta-learning.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

FineReport

DECEMBER 19, 2019

Professional data analysts must have a wealth of business knowledge in order to know from the data what has happened and what is about to happen. In addition, tools for data analysis and data mining are also important. Excel, Python, Power BI, Tableau, FineReport are frequently used by data analysts.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

BizAcuity

MARCH 31, 2022

By 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance. of organizations who participated in an executive survey back in 2019 claimed they are going to be investing in big data and AI. Source: Gartner Research). Source: TCS).

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52