Data Mining involves the systematic examination of large datasets to identify patterns and establish connections in order to address problems through data analysis. Data mining tools enable enterprises to make predictions about future trends.

In today's fiercely competitive business landscape, Data Mining holds significant importance. It finds extensive applications in various domains, such as consumer research marketing, product analysis, demand and supply analysis, e-commerce, investment trends in stocks and real estate, telecommunications, and more. Data Mining relies on mathematical algorithms and analytical skills to extract desired outcomes from extensive databases.

The Data Mining and Management Strategies training course aims to help individuals uncover and explore hidden patterns within data, providing valuable insights to predict, experiment, and continuously refine strategic decisions with substantial business impact. Participants will delve into marketing business processes that increasingly rely on analytics, including customer acquisition, marketing segmentation, and understanding customer lifetime value. Utilizing analytical tools, they will develop models to support these critical business processes.

Course Objectives:

At the end of the Data Mining and Management StrategiesTraining course, participants will be able to:

The definitions of data mining and data science.
The role of statistics in data mining.
Machine learning concepts.
To differentiate between supervised and unsupervised learning.
The data mining process.
How to conduct exploratory data analysis.
To identify data mining models and algorithms.
How to match the problem with the model.
Model validation techniques.
How to deploy data mining models.

Who Should Attend?

Data Mining and Management Strategies training course is designed for:

professionals who want to deepen their understanding of how big data can be mined and managed to uncover information. With its exploration into relational databases and predictive modeling techniques, the course helps professionals understand how this process works effectively with various types of data.

Course Outlines:

Enterprise Database and Data Models

Key differences between data and information.
An understanding of enterprise database environments.
Define specific challenges with data cleansing.
The elements that make up a data model.

Extracting Data from a Database

The role of queries in extracting data from a database.
How to implement advanced queries in Microsoft® Access (or other database environment) using a visual querying language.
How to write queries using Structured Query Language (SQL).
Recognize the manner in which SQL supports, extracts, transforms and loads to prepare data for analytics model development.

Large Scale Implementation of Hadoop® MR

An understanding of and differences between brute force and parallel approaches.
Core concepts, advantages and supporting programs of ApacheTM Hadoop®.
Identify the components of MapReduce.

Getting Data: Social Networks and Glocalization

Structure of a web page and how to obtain HTML files.
The advantages of web crawlers and how to get data page by page.
How to conduct text analysis: identifying human text, common issues, and resource libraries.
The ethical implications of using publicly available data.

Unstructured Data, Graphs and Networks

How to apply the right data structure for a problem.
The differences between graph, node and edge properties.
Define what degree means and analyze and interpret the degree distribution.
Concept of clustering coefficient and what it can mean for your data.

Clustering: Understanding the Relationship of Things

The Idea Behind Clustering.
Types of Clusters.
Distances Between Points.
K-Means Clustering.
Not Every Cluster Is a Good Cluster.
How Good Are My Clusters?
Hierarchical Clustering.
Min, Max, and Mean.

Classifications: Putting Things Where They Belong

The Idea Behind Classification.
Reading and Interpreting a Classification Tree.
Making a Decision Tree.

Alternative Impurity Measures

Expansion to 2D.
How Good Is My Classifier?
But I Only Have Training Data.
A Brief Look at Association Rule Mining.

Classifications: Advanced Methods

Rule-Based Classifier.
Extracting Rules.
Nearest Neighbors.
Classifiers – Defined Boundaries.

Artificial Neural Networks

Limits, Boundary Conditions and Choosing the Right Classifier.
Clustering vs. Classification.
Outlier and Anomaly Detection.

Filters