FineReport

MARCH 24, 2024

Descriptive analytics: Assessing historical trends, such as sales and revenue. Predictive analytics: Forecasting likely outcomes based on patterns and trends to facilitate proactive decision-making. Proficiency in programming languages such as R and SAS is essential for data gathering, cleaning, and visualization.

Statistics 52

Statistics Data mining Visualization Reporting 52

datapine

JANUARY 6, 2022

Qualitative data, as it is widely open to interpretation, must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. Quantitative analysis refers to a set of processes by which numerical data is analyzed. It is the sum of the values divided by the number of values within the data set.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). Edge Computing (and Edge Analytics): Industry 4.0: Robotics: A branch of AI concerned with creating devices that can move and react to sensory input (data).

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

IBM Big Data Hub

AUGUST 7, 2023

AI algorithms have the potential to surpass traditional statistical approaches for analyzing comprehensive recruitment data and accurately forecasting enrollment rates. AI empowers real-time site performance monitoring and forecasting by automating data analysis, providing timely alerts and insights, and enabling predictive analytics.

Optimization 59

Optimization Forecasting Data-driven Strategy 59

Data Science 101

OCTOBER 31, 2019

Data Collection. After defining the goals in the previous step, it is essential to collect data. This could involve using data that already exists in a company’s database, getting data from external resources or steps to collect new data through survey forms filled by customers. Regression.

Data mining 77

Data mining KDD Data Science Forecasting 77

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

datapine

AUGUST 29, 2022

Reading this publication from our list of books for big data will give you the toolkit you need to make sure the former happens and not the latter. 7) Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel. An excerpt from a rave review: “The Freakonomics of big data.”.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

FineReport

JULY 18, 2022

Key features: As a professional data analysis tool, FineBI successfully meets business people’s flexible and changeable data processing requirements through self-service datasets. FineBI is supported by a high-performance Spider engine to extract, calculate and analyze a large volume of data with lightweight architecture.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

O'Reilly on Data

MARCH 18, 2020

data cleansing services that profile data and generate statistics, perform deduplication and fuzzy matching, etc.—or However, organizations need to address important data governance and data conditioning to expand and scale their AI practices. [1] or function-as-a-service designs.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

datapine

NOVEMBER 19, 2019

Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI. An exemplary application of this trend would be Artificial Neural Networks (ANN) – the predictive analytics method of analyzing data.

Technology 279

Technology Internet of Things IT IoT 279

Jet Global

MAY 1, 2023

Use the experts in analytics to add value to your product. Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. Some cloud applications can even provide new benchmarks based on customer data. You’re leaving value on the table.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52