Creating Dashboards and Charts
Introduction to Dashboards and Charts In today’s data-driven world, organizations are increasingly relying on data visualization to make informed decisions. Dashboards and charts play a crucial role in this process,….
Tag: Data Exploration
Drillthrough and Cross Filtering in Dynamics Reports
Introduction Modern business intelligence is not just about displaying static data—it’s about enabling users to explore, interact with, and drill into data for deeper understanding. In the Dynamics 365 ecosystem,….
Dynamic Filtering in Dashboards
1. Understanding Dynamic Filtering Dynamic filtering allows dashboard viewers to interactively control which data appears across all visuals through intuitive selection mechanisms. Unlike static reports, these filters respond to user….
Data visualization in cloud platforms
Data visualization is one of the most powerful ways of analyzing and presenting data, especially in the context of cloud platforms. In the cloud, data visualization tools and services allow….
Data lakes and warehouses in the cloud
Data Lakes and Data Warehouses in the Cloud In the age of big data, organizations generate massive amounts of information from various sources, such as applications, sensors, and social media…..
Data Analysis with Seaborn
Seaborn is a powerful and easy-to-use Python data visualization library built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. Seaborn integrates well….
Self-Organizing Maps (SOMs)
Self-Organizing Maps (SOMs): Detailed Explanation Self-Organizing Maps (SOMs), also known as Kohonen maps, are a type of unsupervised neural network developed by Teuvo Kohonen in the 1980s. SOMs are primarily….
Association Rule Learning (Apriori, Eclat)
Association Rule Learning: Apriori and Eclat Algorithms Association Rule Learning is a technique in data mining used to discover interesting relationships (associations) between variables in large datasets. It is commonly….
t-SNE for High-Dimensional Data
t-SNE (t-Distributed Stochastic Neighbor Embedding) is a powerful technique for dimensionality reduction and visualization of high-dimensional data. Unlike PCA, which is a linear method, t-SNE is a non-linear technique that….
Identifying Data Trends and Patterns
Identifying Data Trends and Patterns: A Comprehensive Guide Introduction Identifying data trends and patterns is a crucial part of Exploratory Data Analysis (EDA) and plays a vital role in data….