Having the basic knowledge of Data Literacy would be beneficial
Suitable for everyone!
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DATA MINING AND DISCOVERY
Data Mining studies algorithms that allow computers to find patterns and regularities in data, perform prediction and forecasting, and generally improve their performance through interaction with data.
What you'll learn
Data Mining studies algorithms that allow computers to find patterns and regularities in data, perform prediction and forecasting, and generally improve their performance through interaction with data. It is regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data.
The process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course provides students with an appreciation of the uses of data mining software in solving business decision problems. Students will gain knowledge of theoretical background to several of the commonly used data mining techniques and will learn about the application of data mining as well as acquiring practical skills in the use of data mining algorithms.
Learning Outcomes:
- Appraise the application of data analytics in a given context
- Recommend appropriate analytics solutions in a given context
- Preparation and visualisation of data for analytical modelling
- Construct analytics models/results as part of solutions to address business problems
- Evaluate the performance of analytics models
- Analyse the results or outputs of analytics models
Learn In No Time
Course Duration
1
Hours
1
Days
Have a look
Course Plan
To enable learners to have an overall understanding of the landscape of Data Mining and Machine Learning.
- Introduction: Machine Learning and Data Mining
- Supervised and Unsupervised learning techniques
To give the participants the very first steps towards forming hypothesis and doing predictive analytics, learning some of the classification and regression techniques in supervised learning.
- Machine Learning and Classification
- Classification: Logistic Regression, K-Nearest Neighbour.
- Regression: Multiple Linear Regression
It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out.
- Evaluation and Metrics
- Understanding and comparing model results
To equip learners in understanding some of the preparation and visualisation techniques which is required for knowledge discovery into their data.
- Data Preparation for Knowledge Discovery
- Data Mining and Visualization
To further equip learners with knowledge on other unsupervised learning techniques, and rule-based mining also be taught.
- Clustering
- Associations Rules
Looking for something else?
Data Literacy
Before embarking on the data science journey, organizations need to be equipped with the right skills and be data literate. This course walks through that journey of getting an organization ready.
Data Collection & Analysis
Data collection ensures that a business or management has the quality information they need to make informed decisions for further analysis, study, and research.
Emerging Technologies
Data Analytics, Automation, Python, Computational Thinking are the upcoming technologies that are driving organizations in the right direction.