Learn Data Mining in Melbourne, Victoria

Training courses, certificates, diplomas or degree programs of Data Mining for students in Melbourne, VIC

Total 26 training courses and degree programs available.

IBM SPSS Modeler and Data Mining (Certificate)

Course Format: Public Course / Instructor-Led / Open Enrollment
School/Trainer: DDLS
Venue(s): Adelaide, Brisbane, Canberra, Melbourne, Perth, Syndey, Australia

Introduction to IBM SPSS Modeler and Data Mining (V16) is a two day course, that provides an overview of data mining and the fundamentals of using IBM SPSS Modeler.

The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.

1. Introduction to Data Mining
•List two applications of data mining
•Explain the stages of the CRISP-DM process model
•Describe successful data-mining projects and the reasons why projects fail
•Describe the skills needed for data mining

2. Working with Modeler
•Describe the MODELER user-interface
•Work with nodes
•Run a... [Read More]

Data Mining for School Leaders

Course Format: Online / Virtual / E-learning
School/Trainer: GradSchool

With increasing pressure on school leaders to draw upon research and evidence in their decision-making, the need for effective management and critical engagement with school-based and system-wide data has become imperative. This course aims to assist school leaders in managing and mining data to make evidence-based decisions.

Objectives This course provides students with the opportunity to:
1) understanding and analyse school-based and system-wide data,
2) apply data mining and/or knowledge management strategies to a real-world case, and
3) link data mining and analysis to strategic decision-making.
Content Indicative course content:

* Contemporary Context of Performance-Based Leadership in Schools
* Reliability and Validity of Data
* Issues related to School-Based Data
* Issues related to System-Wide Data
* Data Mining Strategies
* Knowledge Management Principles
* Evidence-Based Decision-Making

Spatial Data Mining: A Deep Dive into Cluster Analysis

Course Format: Online / Virtual / E-learning
School/Trainer: Esri Training

Cluster analysis is used to identify the locations of statistically significant hot spots, cold spots, spatial outliers, and similar features. Explore spatial statistics techniques to preform Hot Spot Analysis and Cluster and Outlier Analysis.

Statistical Learning and Data Mining

Course Format: Online / Virtual / E-learning
School/Trainer: Colorado State University OnlinePlus

Regularization, prediction, regression, classification and clustering. Students will learn to implement modern statistical techniques for analyzing the types of data that would be encountered by statisticians working in business, medicine, science and government.

Applied Data Mining

Course Format: Online / Virtual / E-learning
School/Trainer: Colorado State University College of Business

Information and communication technologies are enabling organizations to accumulate and access vast quantities of both structured and unstructured data. Data mining refers to the methodical preparation and analysis of this data using statistical, mathematical and artificial intelligence techniques and algorithms. This course will focus on data mining concepts, methodologies, models, and tools, and its applications to business for prediction, classification, and forecasting.

Data Warehousing and Data Mining

Course Format: Online / Virtual / E-learning
School/Trainer: Brandeis University

This course covers the foundations of data warehousing and data mining, and then explores how these technologies convert information into knowledge. Data warehousing is compared and contrasted with operational databases, and the use of various data mining techniques are considered in terms of a variety of problems. From a technical perspective, a special emphasis is placed on data warehouse design and the most common implementation issues.

At the end of the course, students will be able to:

Compare and contrast data warehouses to operational databases.

Devise a plan for moving data from an operational system to a data warehouse.

Map business requirements to dimensional models.

Explain the concepts of on-line analytical processing.

Compare and contrast a variety of data mining algorithms.

Web Data Mining for Business Intelligence

Course Format: Online / Virtual / E-learning
School/Trainer: DePaul University

An in-depth study of the knowledge discovery process and its applications in Web mining, Web analytics and business intelligence. The course provides coverage of various aspects of data collection and preprocessing, as well as basic data mining techniques for segmentation, classification, predictive modeling, association analysis, and sequential pattern discovery. The primary focus of the course is the application of these techniques to Web analytics, user behavior modeling, e-metrics for business intelligence, Web personalization and recommender systems. Also addressed are privacy and ethical issues related to Web data mining. Students can choose from three types of final course projects: implementation projects, research papers, or data analysis projects. Throughout the course, the students will learn and use a variety of data mining tools to analyze sample data sets as part of class assignments.

Programming Data Mining Applications

Course Format: Online / Virtual / E-learning
School/Trainer: DePaul University

The course will focus on the implementations of various data mining and machine learning techniques using a high-level programming language. Students will have hands on experience developing both supervised and unsupervised machine learning algorithms and will learn how to employ these techniques in the context of popular applications including automatic personalization, recommender systems, searching and ranking, text mining, group and community discovery, and social media analytics.

Mining Big Data

Course Format: Online / Virtual / E-learning
School/Trainer: DePaul University

Introduction to fundamentals of distributed file systems and map-reduce technology (e.g., Hadoop), tuning map-reduce performance in a distributed network. Algorithms and tools for mining massive data sets and discussion of current challenges. Applications in clustering, similarity search, classification, data warehousing (e.g., Hive), machine learning (e.g., Mahout).


Course Format: Online / Virtual / E-learning
School/Trainer: Franklin University

A study of auditing issues from case studies and application of data mining techniques in solving audit issues. Key area of concentration with case studies include client acceptance, understanding client business, audit risk assessment, materiality, fraud considerations, internal control objectives and deficiencies, auditing business processes and related accounts, and professional and ethical responsibilities. Key areas of focus with data mining techniques include application with ACL software tables, filters, and commands, audit planning, test of transactions and test of balances. Students will also explore the use of ACL software for forensic auditing and management reports.

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