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. Data science gives the data collected by an organization a purpose. Data science vs. data analytics. Data must be collected and cleaned.

Data Science 139

Data Science Statistics Machine Learning Visualization 139

O'Reilly on Data

JUNE 18, 2019

Bottom-up solutions with human-guided ML pipelines (such as Tamr, Paxata, or Informatica— full disclosure: Ihab Ilyas is co-founder of Tamr ) show how to leverage the available rules and human expertise to train scalable integration models that work on thousands of sources and large volumes of data. Data programming.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

IBM Big Data Hub

MARCH 7, 2023

The challenge was gleaning relevant insights from the vast amount of data collected to help identify those infants at risk. The specific approach we took required the use of both AI and edge computing to create a predictive model that could process years of anonymized data to help doctors make informed decisions.

Risk 52

Risk Technology Informatics Predictive Modeling 52

IBM Big Data Hub

AUGUST 14, 2023

QRadar’s ML models use root-cause analysis automation and integration to make connections for threat and risk insights, showing interrelationships that stretched teams might miss due to turnover, inexperience and the increased sophistication and volume of threats.

Cost-Benefit 55

Cost-Benefit Machine Learning Consulting Data mining 55

CIO Business Intelligence

APRIL 25, 2022

It’s a fast growing and lucrative career path, with data scientists reporting an average salary of $122,550 per year , according to Glassdoor. Here are the top 15 data science boot camps to help you launch a career in data science, according to reviews and data collected from Switchup. Data Science Dojo.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? Model Explainability. Model Interpretability. Model Reproducibility. Machine Learning Model Lineage.

Machine Learning 88

Machine Learning Modeling Metadata Recreation/Entertainment 88

IBM Big Data Hub

FEBRUARY 14, 2024

Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Cost-Benefit 83

Cost-Benefit Insurance Unstructured Data Machine Learning 83

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. 5 common data governance mistakes 1.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

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 53

Data mining Machine Learning Deep Learning Marketing 53

Sisense

APRIL 10, 2020

These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data. Libraries are collections of modules and functions that programmers can include in programs and projects to accomplish specific tasks. Loading data in both R and Python. Python: Versatile workhorse.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

CIO Business Intelligence

AUGUST 15, 2023

Whether a project aims to improve suicide prevention using data science or to create new revenue streams by reimagining an organization’s core business, CIO 100 Award winners demonstrate the innovative spirit of today’s IT in the face of rapidly evolving organizational challenges.

IT 98

IT Manufacturing IoT Cost-Benefit 98

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. 5 common data governance mistakes 1.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Jedox

JULY 23, 2019

These are often not “out of the box” solutions and will require us to spend time learning new skills in process automation, analytics and financial modeling. Just as well as our mindset is on a journey, when it comes to understanding innovative technologies so is data. Once cleansed, its possible to enrich the data.

Technology 52

Technology Forecasting Finance Data-driven 52

Rocket-Powered Data Science

JULY 13, 2023

Data is more than just another digital asset of the modern enterprise. Insights discovery (powered by analytics, data science, and machine learning) drives next-best decisions, next-best actions, and business process automation. However, we are not into clear sailing just yet in the sea of data. Access to data has done that.

Analytics 167

Analytics Machine Learning Data Science Deep Learning 167

Rocket-Powered Data Science

JULY 6, 2021

In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade. 2) MLOps became the expected norm in machine learning and data science projects.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

CIO Business Intelligence

JULY 17, 2023

Most manufacturers do not have their data consolidated,” the CIO explains. To render the most comprehensive data map and recommendations, Dickson and his team collected data from every business unit, every IT system, every server, and every data center.

IoT 98

IoT Digital Transformation Manufacturing Sales 98

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

IBM Big Data Hub

JULY 5, 2023

It allows the company to run tests and predict performance based on simulations. With a good digital twin, it’s possible to predict how an asset will perform under certain conditions and what it’s projected lifespan will be. The four stages of ALM 1.

Strategy 53

Strategy Management IoT Cost-Benefit 53

Domino Data Lab

AUGUST 12, 2021

For data, this refinement includes doing some cleaning and manipulations that provide a better understanding of the information that we are dealing with. In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

DataRobot Blog

DECEMBER 1, 2022

Artificial intelligence (AI) can help improve the response rate on your coupon offers by letting you consider the unique characteristics and wide array of data collected online and offline of each customer and presenting them with the most attractive offers. How Can AI Target the Right Prospects with Sharper Personalization?

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

BI-Survey

NOVEMBER 24, 2020

In particular, the integration of strategic planning and company-wide operational planning, as well as its integration with analytics and business intelligence (BI), are becoming increasingly important to making comprehensive and well-founded decisions based on data.

Forecasting 94

Forecasting Predictive Modeling Statistics Data-driven 94

Darkhorse

JULY 6, 2017

Let’s imagine the process of building a data lake. Let’s further pretend you’re starting out with the aim of doing a big predictive modeling thing using machine learning. First off, data wrangling is gathering the appropriate data. Consider buying data or pulling it from open sources. Create new data?

Data Lake 74

Data Lake Sales Machine Learning Visualization 74

Domino Data Lab

OCTOBER 23, 2019

It covers questions to consider as well as collecting, prepping and plotting data. Collecting and prepping data are core research tasks. While the most ideal situation is to start a project with clean well-labeled data, the reality is that data scientists spend countless hours on obtaining and prepping data.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 20, 2019

Many data scientists deliver value to their organizations by mapping , developing, and deploying an appropriate ML solution to address a business problem. Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models. Filling missing values.

Testing 68

Testing Modeling Interactive Measurement 68

datapine

JANUARY 31, 2022

The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions. Let’s see it with a real-world example.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Domino Data Lab

AUGUST 1, 2021

the need for explainable AI is mainly motivated by the following three reasons: The need for trust – if a doctor is recommending a treatment protocol based on a prediction from a neural network, this doctor must have absolute trust in the network’s capability. According to Fox et al.,

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

O'Reilly on Data

MARCH 24, 2020

If a self-driving car’s decision-making algorithm is trained on data of traffic collected during the day, you wouldn’t put it on the roads at night. The model and the data specification become more important than the code. Data collected for one purpose can have limited use for other questions.

Machine Learning 347

Machine Learning Statistics Data Quality Modeling 347

Jet Global

MAY 9, 2024

Change Data : A method of tracking and capturing changes made to data in a database or data source, including monitoring inserts, updates, and deletes to data records in real-time or near-real-time for the purpose of keeping downstream systems, such as data warehouses, data lakes, or other applications, in sync with the source data systems.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MAY 1, 2023

that gathers data from many sources. These tools prep that data for analysis and then provide reporting on it from a central viewpoint. These systems are designed for people whose primary job is data analysis. The data may come from multiple systems or aggregated views, but the output is a centralized overview of information.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52