Visual data mining (VDM) is the process of interaction and analytical reasoning with one or more visual representations of abstract data. The process may lead to the visual discovery of robust patterns in these data or provide some guidance for the application of other data mining and analytics techniques.
What's so special about learning Data Mining And Visualization through Braingroom's Whatsapp Edubots?
When you conventionally learn anything online, you would go on the flow of how the course is structured where in with our Learning Bots you can focus on the areas which you need to by choosing which ones to do on priority with respect to the experiences you've had in your past interviews. Thereby, customizing the bot to your needs.
Background
The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns. in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.
Pre-processing :
Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.
It exists, however, in many variations on this theme, such as the Cross-industry standard process for data mining (CRISP-DM) which defines six phases:
- Business understanding
- Data understanding
- Data preparation
- Modeling
- Evaluation
- Deployment
Through this course we help you do that by discussing on topics such as,
- Introduction and Installation
- Jupyter Notebook 1
- Jupyter Notebook 2
- Jupyter Notebook 3
- Sympy
- Array 1
- Array 2
- Array 3
- Array 4
- Array 5
- Stack 1
- Stack 2
- Stack 3
- Titanic Dataset 1
- Titanic Dataset 2
- Pandas 1
- Pandas 2
- Pandas 3
- Pandas 4
- Xlrd Installation 1
- Xlrd Installation 2
- Google API 1
- Google API 2
- Google API 3
Braingroom
Braingroom