Smart Data Collective

NOVEMBER 18, 2020

You may not even know exactly which path you should pursue, since some seemingly similar fields in the data technology sector have surprising differences. We decided to cover some of the most important differences between Data Mining vs Data Science in order to finally understand which is which. What is Data Science?

Data Mining 360

Data Mining Big Data Artificial Intelligence Data Warehouse 360

Watermark Learning

OCTOBER 3, 2023

To support your work as a Business Analyst and for a certification exam, review these top modeling techniques: (Note to author – I added some definition around each one, so they knew what they were) Scope Modeling – visually describes what is in and out of scope of the focus area – e.g., solution, stakeholders, department, etc.

Process Modelling 86

Process Modelling Business Analysis Business Analysis Visualization 86

Data Pine

MARCH 25, 2022

It seems clear that there isn’t one standard “correct” definition of the differences between the two terms. The most straightforward and useful difference between business intelligence and data analytics boils down to two factors: What direction in time are we facing; the past or the future? Definition: description vs prediction.

Business Intelligence 105

Business Intelligence Business Intelligence E-Commerce Predictive Analytics 105

Sisense

APRIL 10, 2020

These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. There are also a wide array of libraries available for both languages for text processing, text analysis, and text modeling. A dedicated data expert never stops developing their skills.

Visualization 98

Visualization Artificial Intelligence Data Mining Logistics 98

The BAWorld

NOVEMBER 22, 2021

Consistency is a data quality dimension and tells us how reliable the data is in data analytics terms. It confirms that data values, formats, and definitions are similar in all the data sources. Data Modeling. Conceptual Data Model. Logical Data Model. Data Mining.

Data Analytics 98

Data Analytics Data Analytics Data Warehouse Data Modelling 98

Sisense

AUGUST 11, 2019

BI lets you apply chosen metrics to potentially huge, unstructured datasets, and covers querying, data mining , online analytical processing ( OLAP ), and reporting as well as business performance monitoring, predictive and prescriptive analytics. But on the whole, BI is more concerned with the whats and the hows than the whys.

Business Intelligence 75

Business Intelligence Business Intelligence Data Mining Predictive Analytics 75

Marutitech

JANUARY 21, 2022

Companies worldwide follow various approaches to deal with the process of data mining. . This method is generally known as the CRISP-DM, abbreviated as Cross-Industry Standard Process for Data Mining. . Data Understanding. Apart from reducing the data set, train your model to differentiate and classify your data.

Project Management 113

Project Management Data Mining Data Analytics Data Analytics 113