|Course Title||Diploma in Predictive Data Analytics|
|Typical Schedule||11 Weeks, 1 evening per week|
|Intakes per year||Autumn and Spring|
|Next Commencement Date||Autumn 2018 (September)|
|Learning Mode||Traditional Classroom Based|
Programme Overview and Audience
With the large quantity of data being collected from web sites or the catalogue of previous orders made by clients, data mining provides unique insights into the data which may have previously not been seen. Although possible to draw conclusions from small samples of data, the larger the collection becomes and the more variables that are introduced the process of deducing a simple insight becomes an impossible task. Data mining provides the ability to work with any data set size and draw new unseen perspectives on the data.
This course is designed to provide the learner with the skills needed to collect data from any data source and extract useful insights which have previous been unseen, 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. 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. This is achieved using a wide collection of proprietary and open source data mining tools explored during the course.
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 utilise during a data mining process
- Utilise 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 visualise the findings to inform the business decision making processes
The programme is delivered through tutor led classes, concentrating on labs and hands on skills providing the learner first-hand experience with each of the approaches and technologies described during the classes. Topics Covered during the programme include:
- Integrating Data Sources into your Data Mining Process
- How Data Mining can be Applied to Business Scenarios
- Business Problem Identification
Data Sources and Aggregation
- Data Pre-processing & Quality Assessment
- Noise Filtering and Data Cleansing
- Feature Selection
- Data Types and Variations
- Data Exploration
- Outlier Detection
Methodology and Process Creation
- Introduction to Data Mining Life Cycle
- Data Mining Methodologies (CRISP-DM)
- Process Modelling
Algorithms and Implementations
- Machine Learning – Supervised and Unsupervised
- Decision Trees
- Cluster Analysis
- Sentiment Analysis
- Association Rule Mining
- Term Weighting
- POS Tagging
- N-Gram Creation
- Text Mining
- Visualisation Libraries and Plugins
- Link Based Data Visualisation
- Quantitative Data Visualisation
- Qualitative Data Visualisation
- Result Verification
- Cross Validation
- Enterprise Reporting
- Data Sources
- Key Performance Indicators
- Automated Reporting Approaches and Implementations
Teaching and Assessment
Assignment will be utilised to assess student progression on this programme ensuring a high level of proficiency is achieved. Assessment for this programme is directly mapped to each of the practical tasks which will be explored during lectures and lab time.
CCT College Dublin uses Datacamp extensively as part of the teaching approach on this programme. Datacamp allows students revisit and reinforce the knowledge acquired during lectures when and where they like.
All course material is included in the programme fee. Fees are payable by credit card, debit card, bank transfer, bank draft or cheque. All fees must be paid before the programme begins. Please note that CCT closes on public holidays and for a number of days over the Christmas and New Year holiday period. CCT reserves the right to postpone, cancel, or alter part-time courses without notice, or to change any of the details in the college website or brochures at any time. Fees are not refundable unless the course is cancelled by CCT.