Data Pine
MAY 2, 2023
Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications.
Astera
MAY 31, 2024
Data Science vs. Data Analytics Organizations increasingly use data to gain a competitive edge. Two key disciplines have emerged at the forefront of this approach: data science vs data analytics. In contrast, data science enables you to create data-driven algorithms to forecast future outcomes.
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Analysts Corner
MARCH 7, 2023
Data scientists use a variety of techniques and tools to collect, analyze, and interpret data, and communicate their findings to stakeholders. Data science involves several steps, including data collection, data cleaning, data exploration, data modeling, and data visualization.
Business Analysis Knowledge Share
SEPTEMBER 20, 2023
Business analysts (BAs) stand at the crossroads of technology and business, making sense of data to drive strategic decisions. What roles do they play, especially in the context of data analytics ? By analyzing vast amounts of shipment data, the BAs identified bottlenecks in the supply routes. The result?
The BAWorld
JANUARY 25, 2021
Data Visualization. Now you are all set to visualize your data to see if there are any relevant relationships between different features or variables that can be beneficial for you. For data visualization purposes, commonly used libraries like matplotlib and seaborn in Python, ggplot2 in R are quite useful.
Data Pine
JUNE 5, 2019
To summarize, in the context of BI, data dashboards are used for: Deep-level insight: Drilling down deeper into key aspects of your business’s daily, weekly and monthly operation to create initiatives for increased efficiency. Data being spread out amongst many databases. Lack of different data visualization types.
Astera
MAY 30, 2024
What Is Data Analytics? Data analytics is the science of analyzing raw data to draw conclusions about it. The process involves examining extensive data sets to uncover hidden patterns, correlations, and other insights. Data Mining : Sifting through data to find relevant information.
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