IBM Big Data Hub

AUGUST 15, 2022

Sometimes as data scientists, we are often so determined to build a perfect model that we can unintentionally include human bias into our models. Often the bias creeps in through training data and then is amplified and embedded in the model. Model risk management.

Data mining 63

Data mining Risk Modeling Risk Management 63

TDAN

AUGUST 17, 2021

If you are planning on using predictive algorithms, such as machine learning or data mining, in your business, then you should be aware that the amount of data collected can grow exponentially over time.

Predictive Modeling 115

Predictive Modeling Modeling Data mining Big Data 115

Analytics Vidhya

APRIL 29, 2020

Overview Deploying your machine learning model is a key aspect of every ML project Learn how to use Flask to deploy a machine learning. The post How to Deploy Machine Learning Models using Flask (with Code!) appeared first on Analytics Vidhya.

Machine Learning 393

Machine Learning Modeling Analytics Data mining 393

David Menninger's Analyst Perspectives

SEPTEMBER 16, 2021

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization.

Data Science 340

Data Science Data Lake Data mining Deep Learning 340

Jet Global

JUNE 5, 2019

So how does a leading-edge business find a way to marry their wealth of data with the opportunity to utilize it effectively via BI software? Let’s introduce the concept of data mining. Toiling Away in the Data Mines. Clustering helps to group data and recognize differences and similarities.

Data mining 75

Data mining Business Intelligence OLAP Key Performance Indicator 75

Analytics Vidhya

JANUARY 28, 2022

Image Source: Author Introduction Data Engineers and Data Scientists need data for their Day-to-Day job. Of course, It could be for Data Analytics, Data Prediction, Data Mining, Building Machine Learning Models Etc., All these are taken care of by the respective team members and […].

Data Science 269

Data Science Data mining Machine Learning Publishing 269

KDnuggets

JUNE 15, 2022

14 Essential Git Commands for Data Scientists; A Structured Approach To Building a Machine Learning Model; How is Data Mining Different from Machine Learning?; Understanding Functions for Data Science; Top 18 Data Science Facebook Groups.

Machine Learning 95

Machine Learning Data mining Modeling Data Science 95

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Business analytics is a subset of data analytics. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Analytics 136

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 136

O'Reilly on Data

JANUARY 22, 2019

An exploration of three types of errors inherent in all financial models. At Hedged Capital , an AI-first financial trading and advisory firm, we use probabilistic models to trade the financial markets. All financial models are wrong. Clearly, a map will not be able to capture the richness of the terrain it models.

Modeling 134

Modeling Finance Uncertainty Data mining 134

CIO Business Intelligence

NOVEMBER 14, 2022

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

Data mining 144

Data mining Data-driven Statistics OLAP 144

Jen Stirrup

SEPTEMBER 30, 2021

They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way. Data Science – Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

Decision Management Solutions

JUNE 4, 2021

Like many enterprises, you’ve likely made a hefty investment in analytic technology—from interactive dashboards and advanced visualization tools to data mining, predictive analytics, machine learning (ML), and artificial intelligence (AI). 1 MIT Sloan Management Review September 06, 2017.

Analytics Technologies 52

Analytics Technologies Technology Modeling Key Performance Indicator 52

CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. They know how to assess data quality and understand data security, including row-level security and data sensitivity.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

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 includes the tools and techniques used to perform data analysis.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Financial services: Develop credit risk models. from 2022 to 2028.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

Smart Data Collective

MAY 16, 2022

There are various types of data management systems available. These include, but are not limited to, database management systems, data mining software, decision support systems, knowledge management systems, data warehousing, and enterprise data warehouses. They are a part of the data management system.

Management 127

Management Data mining Data Warehouse Data Collection 127

Data Science 101

DECEMBER 11, 2019

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact. The details are below.

KDD 81

KDD Knowledge Discovery Data mining Statistics 81

Smart Data Collective

MARCH 2, 2023

Big data helps businesses address cash flow needs A growing number of companies use big data technology to improve their financing. They can use data mining tools to evaluate the average interest rate of different lenders. Therefore, data-driven pricing may be even more critical during a bad economy.

Big Data 81

Big Data Uncertainty Risk Data mining 81

Smart Data Collective

AUGUST 30, 2022

Analytics can use existing data to model scenarios where customers will respond to different prices. This article will walk you through 5 top B2B pricing models that you should consider when determining your own strategy. We will also talk about ways to incorporate data analytics into these models.

B2B 131

B2B Analytics Cost-Benefit Metrics 131

Smart Data Collective

JUNE 15, 2021

Big data can play a very important role in solving these challenges. Pre-employment screening with data mining tools increases the quality of candidates. These organizations use data mining tools to find out everything that they can about the people they are screening. Let’s have a look at some facts.

Data-driven 129

Data-driven Data mining Cost-Benefit Big Data 129

CIO Business Intelligence

JUNE 28, 2023

Belcorp operates under a direct sales model in 14 countries. We transferred our lab data—including safety, sensory efficacy, toxicology tests, product formulas, ingredients composition, and skin, scalp, and body diagnosis and treatment images—to our AWS data lake,” Gopalan says. This allowed us to derive insights more easily.”

Digital Transformation 105

Digital Transformation Cost-Benefit Informatics Data mining 105

Smart Data Collective

NOVEMBER 1, 2020

Unlike supervised ML, we do not manage the unsupervised model. k-means Clustering – Document clustering, Data mining. In data mining, k-means clustering is used to classify observations into groups of related observations with no predefined relationships. DBSCAN Clustering – Market research, Data analysis.

Machine Learning 133

Machine Learning Data mining Visualization Statistics 133

Analytics Vidhya

DECEMBER 27, 2023

Introduction Data annotation plays a crucial role in the field of machine learning, enabling the development of accurate and reliable models. In this article, we will explore the various aspects of data annotation, including its importance, types, tools, and techniques.

Machine Learning 280

Machine Learning Modeling Analytics Data mining 280

CIO Business Intelligence

OCTOBER 13, 2023

Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes. Application data architect: The application data architect designs and implements data models for specific software applications.

Data Architecture 132

Data Architecture Data Warehouse Statistics Visualization 132

CIO Business Intelligence

OCTOBER 26, 2023

Business intelligence (BI) analysts transform data into insights that drive business value. Business intelligence analyst job requirements BI analysts typically handle analysis and data modeling design using data collected in a centralized data warehouse or multiple databases throughout the organization.

Business Intelligence 126

Business Intelligence Data-driven Statistics Data Warehouse 126

CIO Business Intelligence

JANUARY 19, 2024

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more.

Data Science 126

Data Science Machine Learning Predictive Modeling Experimentation 126

CIO Business Intelligence

AUGUST 11, 2023

Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially. Amazon Comprehend.

Unstructured Data 142

Unstructured Data Modeling Machine Learning Data mining 142

Smart Data Collective

DECEMBER 19, 2021

Besides, it offers data model creation, systematized data sets, developable web services, ML-powered algorithms, versatile use of data mining and so many other very efficient functionalities that make it very flexible and productive to use for Data Preprocessing. Somewhat becomes slow in computation.

Optimization 135

Optimization Machine Learning Data mining Statistics 135

Smart Data Collective

NOVEMBER 12, 2021

We talked about the benefits of outsourcing IoT and other data science obligations. You should use big data to improve your outsourcing models by data mining pools of talented employees. Data-Driven Businesses Are Shifting More Towards Outsourcing Models. Global companies spent over $92.5 Here’s why.

Data-driven 103

Data-driven Cost-Benefit Data mining Big Data 103

FineReport

MAY 6, 2020

Framework Big Data Processing: Hadoop, storm, spark. Data Warehous: SSIS, SSAS. Skill Data Mining: Matlab, R, Python. Seperti yang Anda ketahui, statistik adalah dasar analisis data. Statistik juga adalah sebuah skill utama seorang data analyst. Prinsip database: model data, desain database.

Data mining 98

Data mining Data Warehouse Machine Learning Big Data 98

Smart Data Collective

APRIL 19, 2022

Data mining technology has become very important for modern businesses. Companies use data mining technology for a variety of purposes. One of the most important is collecting revenue data to draft financial statements, forecast future sales and make decisions to address revenue shortfalls.

Sales 106

Sales Reporting Data mining Forecasting 106

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. They cannot process language inputs generally.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Smart Data Collective

NOVEMBER 11, 2021

We have frequently talked about the merits of using big data for B2C businesses. One of the reasons that we focus on these sectors is that there is so much data on consumers, which makes it easier to create a solid business model with big data. Set Goals and Develop a Strategy with Data Mining.

B2B 112

B2B Data mining Big Data Marketing 112

Smart Data Collective

JUNE 14, 2022

The best way to do this is by generating a buyer persona or a model of your typical customer or client which helps you establish a clear way of promoting your services to the right people. Data analytics has made it easier to identify the best audience for your online business. Use Data Mining to Hone Your Content Creation Skills.

Big Data 104

Big Data Data mining Metrics Sales 104

Smart Data Collective

AUGUST 30, 2022

It also allows companies to offload large amounts of data from their networks by hosting it on remote servers anywhere on the globe. Cloud computing allows companies’ multiple servers to store and manage their data in a distributed fashion. Cloud technology has proven to be an excellent model for large companies.

Technology 133

Technology Enterprise Cost-Benefit Testing 133

Smart Data Collective

JUNE 23, 2022

Finally, if your company is paying for office space through renting, it may be worth adopting a hybrid working model and only using communal workspaces on the days you need to get together. Data analytics tools make it easier to take a deep dive into your finances. Data mining tools make it a lot easier to find government grants.

Analytics 104

Analytics Data mining Finance Insurance 104

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