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

CIO Business Intelligence

JANUARY 20, 2023

BI focuses on descriptive analytics, data collection, data storage, knowledge management, and data analysis to evaluate past business data and better understand currently known information. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

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

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

APRIL 22, 2022

For most organizations, it is employed to transform data into value in the form of improved revenue, reduced costs, business agility, improved customer experience, the development of new products, and the like. Data science gives the data collected by an organization a purpose. Data science vs. data analytics.

Data Science 140

Data Science Statistics Machine Learning Visualization 140

CIO Business Intelligence

SEPTEMBER 14, 2023

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

Analytics 135

Analytics Data Science Data mining Unstructured Data 135

Smart Data Collective

MAY 4, 2023

Professionals have found ways to use big data to transform businesses. Many business leaders are still struggling to understand the role big data will have in the future of their business models. One thing that they need to do is collect data their business needs. They are especially great for web data mining.

Data mining 109

Data mining Big Data Data-driven Measurement 109

Smart Data Collective

MARCH 28, 2022

There are four main types of data analytics: Predictive data analytics: It is used to identify various trends, causation, and correlations. It can be further classified as statistical and predictive modeling, but the two are closely associated with each other. They can be again classified as random testing and optimization.

Data Analytics 105

Data Analytics Software Analytics Statistics 105

CIO Business Intelligence

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers.

Analytics 131

Analytics Data Science Statistics Data mining 131

FineReport

JUNE 24, 2021

Originally, Excel has always been the “solution” for various reporting and data needs. However, along with the diffusion of digital technology, the amount of data is getting larger and larger, and data collection and cleaning work have become more and more time-consuming. Predictive analytics and modeling.

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

Smart Data Collective

MARCH 24, 2020

Big data is changing the direction of our economy in unprecedented ways. Every business should look for ways to monetize big data and use it to optimize your business model. The number of companies using big data is growing at an accelerated rate. They know how to use big data to find the best options available to them.

Big Data 142

Big Data B2B Data mining Sales 142

CIO Business Intelligence

NOVEMBER 28, 2023

We use this data to power AI models that help companies better define how employees are collaborating in their organization, what topics come up most frequently in messages, and whether there is equity in recognition awards across the organization.” The process of making unstructured data usable doesn’t end with analysis, Minarik says.

Unstructured Data 130

Unstructured Data Data-driven Visualization Data Quality 130

Smart Data Collective

MAY 8, 2022

Let’s not forget that big data and AI can also automate about 80% of the physical work required from human beings, 70% of the data processing, and more than 60% of the data collection tasks. From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace.

Big Data 122

Big Data Data Analytics Analytics Statistics 122

erwin

JUNE 11, 2020

With tools such as Artificial Intelligence, Machine Learning, and Data Mining, businesses and organizations can collate and analyze large amounts of data reliably and more efficiently. How Business Benefits from Data Intelligence. Data quality management. Enhanced data discovery and visualization.

Key Performance Indicator 103

Key Performance Indicator KPI Data-driven Machine Learning 103

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

Sisense

APRIL 10, 2020

These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. Modeling in R and Python. Comparison of Python and R for NLP.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large.

Machine Learning 136

Machine Learning Metrics Data mining Data Science 136

FineReport

APRIL 20, 2020

Compared to reporting tools, they can realize data forecast thanks to OLAP analysis and data mining technologies. Besides, in many cases, the data is not entirely grouped, but in the form of fixed columns or segments. All data is divided into strip-shaped models in Crystal Report. Reporting fragment .

Reporting 84

Reporting OLAP Data mining Dashboards 84

FineReport

MARCH 24, 2024

Data Analyst Job Description: Major Tasks and Duties Data analysts collaborate with management to prioritize information needs, collect and interpret business-critical data, and report findings. Python: Known for its text data handling capabilities and compatibility with various platforms and databases.

Statistics 52

Statistics Data mining Visualization Sales 52

Sisense

APRIL 26, 2020

In our modern digital world, proper use of data can play a huge role in a business’s success. Datasets are exploding at an ever-accelerating rate, so collecting and analyzing data to maximum effect is crucial. Companies and businesses focus a lot on data collection in order to make sure they can get valuable insights out of it.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

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? Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g.,

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

IBM Big Data Hub

AUGUST 14, 2023

An exponential boost to your threat detection and investigation efforts To augment your organization’s strained security expertise and resources and increase their impact, QRadar SIEM’s built-in features and add-ons use advanced machine learning models and AI to uncover those hard-to-detect threats and covert user and network behavior.

Cost-Benefit 55

Cost-Benefit Machine Learning Data mining Consulting 55

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. How the models are stored.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

Alation

AUGUST 26, 2021

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. Data lineage features.

Metadata 64

Metadata Data Governance Dashboards Software 64

FineReport

JULY 18, 2022

Most data analysts are very familiar with Excel because of its simple operation and powerful data collection, storage, and analysis. Key features: Excel has basic features such as data calculation which is suitable for simple data analysis. Python enjoys strong portability. RapidMiner. From RapidMiner.

Forecasting 52

Forecasting Dashboards Statistics Visualization 52

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Occam's Razor

SEPTEMBER 15, 2014

In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. Upsight (nee Kontagent) provides mobile app analytics, with a pinch of advanced segmentation (including sweet cohort analysis ) and big data mining thrown in for good measure.

Metrics 143

Metrics Reporting Analytics Marketing 143

datapine

AUGUST 29, 2022

An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”. If we had to pick one book for an absolute newbie to the field of Data Science to read, it would be this one.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

datapine

DECEMBER 28, 2021

Exclusive Bonus Content: Download Our Free Data Integrity Checklist. Get our free checklist on ensuring data collection and analysis integrity! Misleading statistics refers to the misuse of numerical data either intentionally or by error. Exclusive Bonus Content: Download Our Free Data Integrity Checklist.

Statistics 363

Statistics Advertising Visualization Data mining 363

Jet Global

MAY 1, 2023

Users Want to Help Themselves Data mining is no longer confined to the research department. Today, every professional has the power to be a “data expert.” These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52