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Data Mining and Management Strategies

Overview

Examine techniques and algorithms for knowledge discovery in databases, from data pre-processing and transformation to model validation and post-processing. In this 100% online eight-week course, you'll explore marketing business processes that increasingly rely on analytics, including customer acquisition, marketing segmentation, and understanding customer lifetime value. Use analytical tools to develop models to support these business processes.

Who Should Register?

This 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.

Content

What you will learn:

• 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 Apache Hadoop
  • Identify the components of MapReduce
• Getting Data: Social Networks and Geolocalization
  • 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
  • Concept of clustering and necessary conditions
  • Continuous and discrete distances and their different implications for clustering
  • How to use bootstrapping to find a good business solution
  • Min, max, and mean merging and why it is important to understand these relations
• Classifications: Putting Things Where They Belong
  • What classification does and its key components
  • The elements of classification and how to use a decision tree
  • How to apply the idea of impurity to tree induction
  • Discrete and continuous classes and their role in supporting classification
• Classifications: Advanced Methods
  • Statistical and classification methods—when you would use each
  • What issues to consider when only training data is available
  • Advantages and disadvantages of Artificial Neural Networks (ANN)
  • The limits, constraints, and differences of classifiers

Basic Requirements

• Bachelor's Degree or minimum High School with preferably relevant working experience and good English language skills.

Certificate

Upon successful completion, a "Certificate of Completion" will be issued with both MSU and UD authorized signatories.

Price

The current price is د.إ3,643.00, with a 5% VAT.

Duration and Location

• Duration: 8-week course
• Location: Online
• Start Date: 1 Feb / 1 June / 1 October