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 121

Predictive Analytics Analytics Predictive Modeling Data mining 121

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029. Many stock market transactions use ML.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

CIO Business Intelligence

OCTOBER 13, 2023

In the next six to 12 months, some of the most popular anticipated uses for gen AI include content creation (42%), data analytics (53%), software development (41%), business insight (51%), internal customer support (45%), product development (40%), security (42%), and process automation (51%).

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Analytics Vidhya

MAY 26, 2023

Imagine diving into the details of data analysis, predictive modeling, and ML. Before you decide […] The post Data Science Subjects and Syllabus [Latest Topics Included] appeared first on Analytics Vidhya. Envision yourself unraveling the insights and patterns for making informed decisions that shape the future.

Data Science 298

Data Science Predictive Modeling Modeling Technology 298

CIO Business Intelligence

JUNE 7, 2022

What is data analytics? Data analytics is a discipline focused on extracting insights from data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. What are the four types of data analytics?

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. It offers a bootcamp in data science and machine learning for individuals with experience in Python and coding.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI As model building become easier, the problem of high-quality data becomes more evident than ever. In this post, we shed some light on various efforts toward generating data for machine learning (ML) models.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Sisense

APRIL 10, 2020

R: Analytics powerhouse. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Modeling in R and Python. When we say “modeling” in data science, we mean teaching a program to learn from training data using machine learning algorithms.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

datapine

AUGUST 14, 2019

Companies surely need data scientists to help them empower their analytics processes, build a numbers-based strategy that will boost their bottom line, and ensure that enormous amounts of data are translated into actionable insights. But being an inquisitive Sherlock Holmes of data is no easy task. Source: RStudio. Source: mathworks.com.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

IBM Big Data Hub

MAY 17, 2024

Poorly run implementations of traditional or generative AI technology in commerce—such as deploying deep learning models trained on inadequate or inappropriate data—lead to bad experiences that alienate both consumers and businesses.  The applications of AI in commerce are vast and varied.

B2B 70

B2B Interactive Optimization Forecasting 70

Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal. After cleaning, the data is now ready for processing.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 57

Data mining Machine Learning Deep Learning Marketing 57

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning? temperature, salary).

Machine Learning 80

Machine Learning Modeling Deep Learning Predictive Modeling 80

IBM Big Data Hub

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. Clean up with predictive maintenance AI can be used for predictive maintenance by analyzing data directly from machinery to identify problems and flag required maintenance.

Cost-Benefit 87

Cost-Benefit Insurance Unstructured Data Machine Learning 87

Smarten

APRIL 19, 2024

In this article, we will discuss the current state of AI in analytics, as well as the future of this burgeoning industry and how it can be applied to analytics to simplify and clarify results and to make analytics easier for businesses and business users to leverage.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

DMBS2

DECEMBER 12, 2017

As I wrote back then in a post about the connection between machine learning and the rest of AI , It is my opinion that most things called “intelligence” — natural and artificial alike — have a great deal to do with pattern recognition and response. Accordingly, it can be reasonable to equate machine learning and AI.

Machine Learning 50

Machine Learning Predictive Modeling Deep Learning Modeling 50

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 166

Analytics Machine Learning Data Science Deep Learning 166

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. This is critical in our massively data-sharing world and enterprises.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

CIO Business Intelligence

MARCH 22, 2023

Enter the new class ML data scientists require large quantities of data to train machine learning models. Then the trained models become consumers of vast amounts of data to gain insights to inform business decisions. It is often used to train machine learning models and protect sensitive data in healthcare and finance.

Machine Learning 105

Machine Learning Cost-Benefit Modeling Risk 105

Smart Data Collective

MAY 17, 2021

Intrinsically, it can process information on a large scale, utilizing automation and smart analytics tools. Big data solutions are often created and supported using various technologies from IIoT to machine learning and AI. Big data is at the heart of the digital revolution. It also introduces operational efficiencies.

Big Data 101

Big Data Cost-Benefit IoT Machine Learning 101

Alation

APRIL 14, 2022

And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. Predictive Transformation.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Domino Data Lab

JULY 9, 2019

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. I still believe that data science is the craft of trying to apply machine learning to some real world problem.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. The term “ML” is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

O'Reilly on Data

MARCH 24, 2020

On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0 Before you even think about sophisticated modeling, state-of-the-art machine learning, and AI, you need to make sure your data is ready for analysis—this is the realm of data preparation.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346