Diploma in Predictive Data Analytics

Program Overview

This Diploma in Predictive Data Analytics equips learners with the skills to collect, analyze, and extract insights from data using industry-leading tools like Rapidminer. Through hands-on labs and tutor-led classes, students gain proficiency in data preparation, unsupervised and supervised learning, and data visualization. The program prepares graduates for entry-level roles in data mining and analytics or further study in related fields.

Program Outline

Degree Overview:

This course in data analytics is designed to provide the learner with the skills needed to:

• Collect data from any data source and extract useful insights which have previously not been seen, providing a unique view on the problems faced during the business decision making process.
In this programme, the learner will become familiar with a suite of different leading tools available to gather information from different sources and apply commonly used algorithms to deduce answers to common business questions from data, predominantly Rapidminer. Data mining aids the decision-making process by informing the key stake holders by relying on the most reliable source, the data available. This approach helps the decision-making process by making informed decisions before key changes or alterations are made to any business process. This course is aimed at learners with no previous experience with data mining or data analysis and wish to begin the process of understanding how data is aggregated, cleaned and utilised for data mining processes. CCT College Dublin Professional Certification confirms the successful completion of the Diploma in Predictive Data Analytics. Professional Certification supports career advancement through verification of upskilling. This programme does not lead to an award on the National Framework of Qualifications (NFQ).

Outline:

Programme Aims and Objectives

This module aims to introduce the learner to the area of data mining and analytics by providing real world examples of business questions that can be encountered during day to day life, and how they can be solved using freely available data mining software packages. On completion of this course, the learner will have acquired the skills to:

• Assess the needs of a customer and how they can be met with one or more developed data mining solutions
• Assess and aggregate available data sources to utilize during a data mining process
• Utilize industry standard methodologies for data mining, ensuring a robust process is created
• Develop a data mining process to identify anomalies, clean and extract quality data to run the identified algorithms on
• Run leading data mining software packages on available data to identify patterns and predict outcomes
• Document and visualize the findings to inform the business decision making processes

Programme Content

The programme is delivered through tutor led classes, concentrating on labs and hands on skills providing the learner firsthand experience with each of the approaches and technologies described during the classes. Topics Covered during the programme include:

• Introduction
• What is Predictive Analytics?
• The business case for data analytics
• The Data Mining Life Cycle
• KDD and CRISP-DM
• Overview of Supervised and Unsupervised Learning
• Overview of Classification and Regression
• Data Preparation & Pre-Processing
• Data Types
• Exploratory Data Analysis
• Handling Missing Data – Removal vs Imputation
• Outlier Detection
• Noise Filtering
• Feature Selection
• Unsupervised Learning
• k-Means & k-Mediods
• Association Rule Mining
• Self-Organizing Maps
• Classification
• Decision Trees
• k-Nearest Neighbours
• Naive Bayes
• Regression
• Linear & Polynomial Regression
• Regression Trees
• Validation and Testing
• Hold-out and Cross Validation
• Evaluating the performance of classifiers
• Evaluating the performance of regression models
• Text Analytics
• Tokenisation & N-Grams
• The Term Document Matrix
• Term Frequency – Inverse Document Frequency
• Sentiment Analysis
• Data Visualization
• Exploratory vs Explanatory Data Visualization
• Quantitative Data Visualization
• Quantitative Data Visualization

Assessment:

Continuous Assessment will be utilized to assess student progression on this programme ensuring a high level of proficiency is achieved. All assessments for this programme are directly mapped to each of the practical tasks which will be explored during lectures and lab time.

Careers:

This programme provides a strong foundation in Data Mining and Data Analytics. It is envisioned that graduates will be able to fulfill a wide range of entry-level roles within data mining industries, and/or engage in further study in a wide range of areas within Computing and Information Technology, and specifically programming, to further develop their careers.