Register for the Data Analytics Paid Course Using Altair® RapidMiner®

Basic Modelling with RapidMiner - Course Kick-Off Monday, December 9th, 2024

Are you ready to unlock the power of data and revolutionize your decision-making process? This course, delivered by the University of Peloponnese, equips you with the skills to extract meaningful information from large datasets, enabling data-driven decision-making and giving you a competitive edge in today's data-driven world.

What you will learn:

Gain a solid foundation in data mining principles and techniques, master the Altair RapidMiner solution, and unlock the potential of big data. Here's what you'll achieve with this course:

Understand Data Mining Fundamentals and Techniques
Navigate Altair RapidMiner's Features and Tools
Explore and Profile Data Effectively
Prepare Data and Optimize Variable Selection
Implement Decision Trees and Linear Regression
Discover Patterns with Cluster Analysis
Simplify Data with Principal Component Analysis

Who can attend this course:

This course is designed for everyone interested in the captivating field of data analytics. Whether you're a seasoned professional looking to enhance your skills or someone new to the realm of data science, this program welcomes participants from diverse backgrounds. No matter your level of expertise, if you have an interest in leveraging the power of data for informed decision-making, this course is tailored to meet your needs.

ENROLL NOW AND EMBRACE THE POWER OF DATA!

Embark on this educational journey, enhance your skills, and take your education to new heights through informed decision-making powered by Altair RapidMiner.

Equip yourself with the skills to make informed decisions and gain a competitive edge in today's data-centric landscape.

Module title	Week	Hours in total
Introduction to Data Mining	1	25
Introduction to Altair RapidMiner	2	25
Data Exploration and Profiling using Altair RapidMiner	3	25
Data Preparation and Variable Selection	4	25
Decision trees	5	25
Linear Regression	6	25
Cluster analysis	7	25
Principal Component Analysis-Quiz for self-assessment	8	25
Total	8	200

Structure of the Program

Section 1: Introduction to Data Mining

Unleashing the Power of Big Data

Big data is transforming decision-making, rendering traditional methods obsolete. The rise of data science and mining, fueled by technology and vast data availability, is shifting decision paradigms from gut-based to data-driven.

This section aims to describe a general definition of data mining, as well as a variety of data mining techniques that can be applied to a variety of business problems.

Section 2: Introduction to Altair RapidMiner

Mastering Altair RapidMiner Essentials

This section offers a comprehensive glimpse into Altair RapidMiner, covering vital features like the Project Pane and Working Directory for efficient organization. Navigate through its intuitive menu system and toolbar to streamline operations. Learn to initiate new projects and grasp the concepts of workflows, nodes, and pallets.

Understand the seamless process of importing files and connecting nodes to construct a straightforward workflow. Enhance your workflows by improving, extending, and adding comments, optimizing your data analytics experience.

Section 3: Data Exploration and Profiling using Altair RapidMiner

Uncover Insights through Data Exploration

Data exploration, as a preliminary process for further modeling, or simply trying to better understand the data, are essential components of data mining. Data exploration involves examining the characteristics and field distributions of the data set to identify potential problems and are ultimately important factors and patterns that distinguish the data.

Altair RapidMiner provides a range of tools for exploring and profiling data. This section describes how to use and understand key concepts in relation to exploring and profiling data using Altair RapidMiner.

Section 4: Data Preparation and Variable Selection

Crafting Quality Data for Analysis

Data preparation in Altair RapidMiner revolves around two fundamental categories of functions: data set functions and field transformations. Data set functions enable actions at the data set level, facilitating tasks like merging files or removing duplicates for streamlined data management.

Field transformations operate at a granular level, allowing the creation of new fields or the transformation of existing ones within each case, ensuring data is refined and optimized for analysis.

Section 5: Decision Trees

Guiding Insights with Decision Trees

A decision tree operates as a segmentation model, systematically dividing a dataset based on the interplay between a dependent or target variable and a set of independent or predictor variables. Its flexibility lies in the accommodation of both categorical and continuous dependent variables, making it a versatile data mining technique.

This adaptability allows for a comprehensive analysis, aiding in insightful interpretations of complex datasets and contributing to effective decision-making processes.

Section 6: Linear Regression

Understanding Linear Relationships

Linear regression is a primary statistical technique designed to model the linear relationship between a dependent variable and a set of input predictors, drivers or independent variables. The dependent variable should be a scale or continuous variable while the inputs, although traditionally and preferably continuous, can be either categorical or continuous.

Regression modeling provides a means of assessing not only the extent to which the outcome of the dependent variable can be determined, but also the effect of each predictor on that outcome.

Section 7: Cluster Analysis

Uncover Hidden Patterns with Clustering

Clustering is a method of grouping objects with similar characteristics into clusters. The purpose of a cluster analysis is to create homogeneous groups where variability within clusters is minimized and variability between clusters is maximized. The goal of clustering is to partition a heterogeneous population into more homogeneous subgroups or clusters.

Clustering is an unsupervised data mining technique, as clustering is not driven by a specific purpose and therefore there is no dependent variable in the model. Instead, all variables are considered independent variables. Cluster analysis is a useful technique for revealing homogeneous groups.

Section 8: Principal Component Analysis

Streamline Data with Principal Component Analysis

Principal components analysis (PCA) stands as a statistical method employing transformations on a group of potentially correlated variables, yielding a set of linearly uncorrelated variables known as principal components. This technique serves two primary purposes: firstly, it aids in comprehending the inherent structure within a group of variables, offering insights into their relationships and interactions. Secondly, PCA allows the reduction of variables to a more manageable subset, facilitating efficient modeling or enabling further in-depth investigation, making it an invaluable tool in data analysis and interpretation.

Instructors

Dr. Athanasios C. Anastasiou

Program Scientific Coordinator, Associate Professor

University of Peloponnese

Contact Here

Dr. Zacharias Dermatis

Faculty member