O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

CIO Business Intelligence

OCTOBER 26, 2023

Through use of data analytics, data visualization, and data modeling techniques and technologies, BI analysts can identify trends that can help other departments, managers, and executives make business decisions to modernize and improve processes in the organization.

Business Intelligence 126

Business Intelligence Data-driven Statistics Data Warehouse 126

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. 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.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

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. According to Gartner, the goal is to design, model, align, execute, monitor, and tune decision models and processes. Model-driven DSS. They emphasize access to and manipulation of a model.

Data mining 144

Data mining Data-driven Statistics OLAP 144

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

datapine

MARCH 31, 2022

According to the US Bureau of Labor Statistics, demand for qualified business intelligence analysts and managers is expected to soar to 14% by 2026, with the overall need for data professionals to climb to 28% by the same year. The Bureau of Labor Statistics also states that in 2015, the annual median salary for BI analysts was $81,320.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

datapine

AUGUST 14, 2019

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. It helps to automate and makes the usage of the R programming statistical language easier and much more effective. perfect for statistical computing and design.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

CIO Business Intelligence

MAY 12, 2022

The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them. Visual IDE for data pipelines; RPA for rote tasks. Highlights.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

CIO Business Intelligence

JANUARY 19, 2024

The US Bureau of Labor Statistics (BLS) forecasts employment of data scientists will grow 35% from 2022 to 2032, with about 17,000 openings projected on average each year. You need experience in machine learning and predictive modeling techniques, including their use with big, distributed, and in-memory data sets.

Data Science 126

Data Science Machine Learning Predictive Modeling Experimentation 126

O'Reilly on Data

MARCH 26, 2024

TL;DR LLMs and other GenAI models can reproduce significant chunks of training data. Researchers are finding more and more ways to extract training data from ChatGPT and other models. And the space is moving quickly: SORA , OpenAI’s text-to-video model, is yet to be released and has already taken the world by storm.

Modeling 275

Modeling Machine Learning Risk Advertising 275

Alation

JUNE 2, 2022

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. And until recently, such governance processes have been fragmented.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

CIO Business Intelligence

APRIL 22, 2022

Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For example, data analysts should be on board to investigate the data before presenting it to the team and to maintain data models. What is data science?

Data Science 140

Data Science Statistics Machine Learning Visualization 140

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. The exam consists of 65 multiple-choice or multiple-response questions, which the candidate has 180 minutes to complete.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Smarten

APRIL 25, 2024

What you need are apps and solutions that allow you to ask easy questions in your own words and receive guidance and recommendations on how to best visualize and present your data and what techniques to use to gain the most insight. These measures empower them with a deeper understanding of their data like never before.

Data Quality 62

Data Quality Analytics Machine Learning Visualization 62

IBM Big Data Hub

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

FineReport

MARCH 24, 2024

Data analysts contribute value to organizations by uncovering trends, patterns, and insights through data gathering, cleaning, and statistical analysis. They identify and interpret trends in complex datasets, optimize statistical results, and maintain databases while devising new data collection processes.

Statistics 52

Statistics Data mining Visualization Reporting 52

datapine

JANUARY 6, 2022

Through the art of streamlined visual communication, data dashboards permit businesses to engage in real-time and informed decision-making and are key instruments in data interpretation. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

datapine

JANUARY 24, 2021

Additionally, incorporating a decision support system software can save a lot of company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems. There are basically 4 types of scales: *Statistics Level Measurement Table*.

IT 317

IT Statistics KPI Data-driven 317

Smarten

NOVEMBER 23, 2023

The Smarten Augmented Analytics suite includes Smart Data Visualization , AI and Assisted Predictive Modeling , Self-Serve Data Preparation , Natural Language Processing (NLP) and Search Analytics , SnapShot Monitoring and Alerts , and many other sophisticated features.

Publishing 62

Publishing Marketing Analytics Predictive Modeling 62

Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

CIO Business Intelligence

MAY 9, 2024

That includes IT, to align AI technologies with existing infrastructure; HR, on workforce development; finance, to understand funding and new business cost models; and legal and compliance, to ensure responsible use of AI. This includes skills in statistical analysis, data visualization, and predictive modeling.

Risk Management 130

Risk Management Risk Cost-Benefit Technology 130

Smart Data Collective

DECEMBER 15, 2022

Business data is a broad field that, like every other type of data , is statistics about the business. Visualization of Data. Now, after getting the correct data, it is converted into a form of visual data. In the end, these figures and models are required to get analytical insights. Below is a more detailed explanation.

Analytics 117

Analytics Visualization Data Collection Statistics 117

DataRobot Blog

SEPTEMBER 1, 2022

For example, a recent IDC study 1 shows that it takes about 290 days on average to deploy a model into production from start to finish. Once you move your model into production, you need to monitor and manage your models to ensure that you can trust predictions and turn them into the right business decisions.

Metrics 145

Metrics Statistics Modeling Data Science 145

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 culminates with a capstone project that requires creating a machine learning model. Data Science Dojo.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

IBM Big Data Hub

DECEMBER 18, 2023

Geographic information systems (GIS ) visualize and make sense of this data, helping people and businesses better understand the patterns and relationships of our world. These maps, graphs, statistics and cartograms display geographical features like location, natural resources, streets and buildings as well as demographics.

Internet of Things 106

Internet of Things IoT Forecasting Visualization 106

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

DataKitchen

NOVEMBER 18, 2022

Logs and storage for problem diagnosis and visualization of historical trends. DataOps Observability must also store run data over time for root cause diagnosis and statistical process control analysis. the data, the models, the integrations, the reports, and the outputs that your customers see?are An event or rules engine.

Testing 130

Testing Statistics Measurement Metrics 130

Smart Data Collective

MARCH 27, 2023

Data Scientists and Analysts use various tools such as machine learning algorithms, statistical modeling, natural language processing (NLP), and predictive analytics to identify trends, uncover opportunities for improvement, and make better decisions.

Data Science 93

Data Science Statistics Data-driven Machine Learning 93

CIO Business Intelligence

AUGUST 14, 2023

Without visualized analytics, it was difficult to bridge the void between expectation and accurate analysis. The collaboration between Huabao and SAP continued as plans for a new foundation to support corporate development and business model transformation gathered speed.

Key Performance Indicator 98

Key Performance Indicator Sales Manufacturing KPI 98

Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. Statistics.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

CIO Business Intelligence

JANUARY 17, 2024

To do this, Ipsos uses large language models to generate cutting-edge insights built using AI capabilities, complemented by its data science models and tailored to the services it offers,” says Mohammed. js and React.js.

Digital Transformation 119

Digital Transformation Dashboards Reporting Data Science 119

Smart Data Collective

NOVEMBER 1, 2020

Unlike supervised ML, we do not manage the unsupervised model. Overall, clustering is a common technique for statistical data analysis applied in many areas. Hidden Markov Model – Pattern Recognition, Bioinformatics, Data Analytics. As a visualization tool – PCA is great at showing a bird’s view of the process.

Machine Learning 134

Machine Learning Data mining Visualization Statistics 134

FineReport

JUNE 11, 2021

Business analytics is how companies use statistical methods and techniques to analyze historical data to gain new insights and improve strategic decision-making. In contrast, business analytics is often described as a more statistically-based field. Data visualization with FineReport. Business Analytics. Free Download.

Business Intelligence 98

Business Intelligence Analytics Dashboards Statistics 98

Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

Statistics 97

Statistics Metrics Visualization Finance 97

IBM Big Data Hub

DECEMBER 19, 2023

A machine learning model trained with labeled data will be able to detect outliers based on the examples it is given. Common machine learning algorithms for supervised learning include: K-nearest neighbor (KNN) algorithm : This algorithm is a density-based classifier or regression modeling tool used for anomaly detection.

Machine Learning 72

Machine Learning Optimization Unstructured Data Sales 72

AWS Big Data

JUNE 6, 2023

They have to gather detailed data statistics, such as minimums, maximums, averages, and correlations. They have to then review the data statistics to identify data quality rules, and write code to implement these checks in their data pipelines. This simple pricing model doesn’t lock you into annual licenses.

Data Quality 92

Data Quality Statistics Data Lake Visualization 92

Smart Data Collective

OCTOBER 8, 2023

The Power of Data Analytics: An Overview Data analytics, in its simplest form, is the process of inspecting, cleansing, transforming, and modeling data to unearth useful information, draw conclusions, and support decision-making. It is an interdisciplinary field, combining computer science, statistics , mathematics, and business intelligence.

Data Analytics 96

Data Analytics Analytics Statistics Data Collection 96