Smart Data Collective

MARCH 13, 2020

The better strategy is to demarcate each data science project into four distinct phases : Phase 1: Preliminary Analysis. Phase 3: Data Visualization. With the data analyzed and stored in spreadsheets, it’s time to visualize the data so that it can be presented in an effective and persuasive manner. Phase 4: Knowledge Discovery.

Data Science 101

Data Science Key Performance Indicator Knowledge Discovery Visualization 101

Ontotext

MARCH 8, 2023

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

FineReport

JULY 16, 2021

Through this way, it can support current corporate analysis and future decision or strategy making. It is a process of using knowledge discovery tools to mine previously unknown and potentially useful knowledge. It is an active method of automatic discovery. Data Visualization. INTERFACE OF BI SYSTEM.

Business Intelligence 98

Business Intelligence Informatics Data Warehouse Data mining 98

Domino Data Lab

MAY 20, 2021

propose a different strategy where the minority class is over-sampled by generating synthetic examples. Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Chawla et al.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136