ANL303 Fundamentals of Data Mining TMA Assignment Sample

Extracting the worthy and useful part from a large amount of something is always a good option. Technically it is called data mining. It is very essential for the students of business administration to learn data mining techniques in order to address different types of business problems. That’s why several business students in Singapore opt for the ANL303 Fundamentals of Data Mining at SUSS University.

Singapore University of Social Sciences offers a variety of course programs to the students and complete guidance over how they can build their career in any specific field. ANL303 is also one of such courses provided to the business students so that they can acquire the basic knowledge and skills of data mining and utilize in their practice for solving any business problems.

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Major Objective of SUSS ANL303 Fundamentals of Data Mining Course

The major objective behind introducing the ANL303 SUSS course is to equip the students with efficient knowledge and understanding of the data mining process and applications. Not only the theoretical knowledge but this course also provides the students with effective skills of implementing the crucial strategies of data mining into the real process and tackle and business issues.

Also, the ANL303 course aims to educate the SG students with the skills to explore and prepare data for mining. This course also furnishes the students with the basic skills and knowledge in constructing, interpreting, and evaluating data mining results or models.

Besides, the SUSS ANL303 course will also endow the students with expertise to apply data analytics using a suite of tools including cloud services.

Introduction to Data Mining

Data mining is an essential part of data management which is involved with sorting the useful and important data from a bundle of information which can lead us to obtain the required output. Several companies also utilize various data mining techniques to emphasize critical data available in the data store.

In simple words, data mining is the process of extracting useful data from a larger set of raw data. It involves various strategies and techniques to analyze data patterns in huge data batches employing one or more software.

Goals of Data Mining

The major goals of data mining process are: –

No. 1- Prediction: – Determining the future of the market by analyzing the past trends

No. 2- Identification: – Conducted on a data pattern by thoroughly identifying the already available items

No. 3- Classification: – Dividing the data into different categories to ease identifying the categories on the basis of parameters

No. 4- Optimization: – Reduce time usage, money, material, & space; & improve the outputs like profits

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Advantages of Data Mining

The data mining can be utilized in the following fields providing advantageous outputs: –

• Marketing
• Researchers
• Finance/ banking
• Increase brand loyalty
• Determining the customer group
• Assist in the decision making process
• Enhance company revenue
• Help in predicting future trends
• Boost the website optimization

ANL303 Fundamentals or Data Mining Course Topics

The SUSS ANL303 course furnishes the students with great knowledge and understanding of the data mining process in different pieces.

Here are the main topics of the ANL303 SUSS course: –

• Introduction to data mining
• Types of data mining
• Data mining applications
• The cross-industry standard process for data mining
• Preparing data for different types of mining tasks
• Introduction to association rule mining
• General data quality issues and treatments
• Introduction to predictive modeling
• Model evaluation and result interpretation
• Introduction to clouds analytics
• Applying clouds analytics

Educational Upshots of ANL303 SUSS Course

At the end of the SUSS Fundamentals of Data Mining (ANL303) course, the students will be able to explain all the major features of data mining and its applications.

The major learning outcomes of the ANL303 course are as follows: –

• Explain various features of developing data mining solutions
• Examine the application of data analytics in some specific context
• Suggest a suitable analytics solution for a provided condition
• Build analytics models/results as part of solutions to solve the business issues
• Assess the performance of analytics models
• Examine the results or outputs of analytics models

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