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DAM301Sciences3 Unitsintermediate

Data Mining And Data Warehousing

This course introduces the concepts of data mining and data warehousing. It explores data mining problems, applications, and commercial tools, along with knowledge discovery. The course also covers data warehousing architecture, data marts, the data warehousing lifecycle, data modeling, and building data warehouses. Students will learn about OLAP, MOLAP, ROLAP technologies, and future trends in data warehousing. The course is designed to provide a comprehensive understanding of data mining and data warehousing principles and practices.

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208h
Study Time
13
Weeks
16h
Per Week
basic
Math Level
Course Keywords
Data MiningData WarehousingOLAPKnowledge DiscoveryData Analysis

Course Overview

Everything you need to know about this course

Course Difficulty

Intermediate Level
Builds on foundational knowledge
65%
intermediate
➕
Math Level
Basic Math
🔬
Learning Type
Hands-on Practice

Course Topics

Key areas covered in this course

1

Data Mining Concepts

2

Data Warehousing

3

OLAP Technologies

4

Data Preprocessing

5

Data Mining Applications

6

Data Mining Technologies

Total Topics6 topics

Ready to Start

No specific requirements needed

This course is designed to be accessible to all students. You can start immediately without any prior knowledge or specific preparation.

Assessment Methods

How your progress will be evaluated (3 methods)

assignments

Comprehensive evaluation of course material understanding

Written Assessment

tutor-marked assessments

Comprehensive evaluation of course material understanding

Written Assessment

final examination

Comprehensive evaluation of course material understanding

Written Assessment

Career Opportunities

Explore the career paths this course opens up for you

Data Analyst

Apply your skills in this growing field

Data Scientist

Apply your skills in this growing field

Business Intelligence Analyst

Apply your skills in this growing field

Database Administrator

Apply your skills in this growing field

Data Warehouse Architect

Apply your skills in this growing field

Industry Applications

Real-world sectors where you can apply your knowledge

FinanceRetailTelecommunicationsHealthcareE-commerce

Study Schedule Beta

A structured 13-week journey through the course content

Week
1

Module 1: Concepts of Data Mining

4h

Unit 1: Overview of Data Mining

4 study hours
  • •Understand the definition of data mining and its importance.
  • •Explore the motivations behind data mining and its applications.
  • •Identify the architecture of data mining systems and their components.
Week
2

Module 1: Concepts of Data Mining

4h

Unit 2: Data Description for Data Mining

4 study hours
  • •Examine the different types of information collected in databases and flat files.
  • •Describe the various types of data to mine, including relational databases and data warehouses.
  • •Explain the different kinds of data mining functionalities and the knowledge they discover.
Week
3

Module 1: Concepts of Data Mining

4h

Unit 3: Classification of Data Mining

4 study hours
  • •Identify the various classifications of data mining systems.
  • •Describe the categories of data mining tasks.
  • •State the diverse issues coming up in data mining and the challenges facing data mining.
Week
4

Module 1: Concepts of Data Mining

4h

Unit 4: Data Mining Technologies

4 study hours
  • •Identify the various data mining technologies available.
  • •Understand the principles behind neural networks and decision trees.
  • •Explore rule induction and genetic algorithms.
Week
5

Module 2: Data Processes and Trends

4h

Unit 1: Data Preparation and Preprocesses

4 study hours
  • •Identify different data formats and types.
  • •Understand the importance of data preparation.
  • •Learn data preprocessing techniques such as data cleaning, transformation, and reduction.
Week
6

Module 2: Data Processes and Trends

4h

Unit 2: Data Mining Process

4 study hours
  • •Describe the steps involved in building a data mining database.
  • •Understand the importance of data exploration and preparation.
  • •Learn about model building, evaluation, and deployment.
Week
7

Module 2: Data Processes and Trends

4h

Unit 3: Data Mining Applications

4 study hours
  • •Explore the applications of data mining in various industries such as finance, retail, and telecommunications.
  • •Understand the use of data mining for biological data analysis.
  • •Identify data mining system products and research prototypes.
Week
8

Module 2: Data Processes and Trends

4h

Unit 4: Future Trends in Data Mining

4 study hours
  • •Explore future trends in data mining.
  • •Understand the theoretical foundations of data mining research.
  • •Discuss the challenges and opportunities in the field of data mining.
Week
9

Module 3: Data Warehousing Concepts

4h

Unit 1: Overview of Data Warehouse

4 study hours
  • •Define the term data warehouse and understand how it works.
  • •Explore the different types of data warehouses.
  • •Identify the goals and characteristics of data warehouses.
Week
10

Module 3: Data Warehousing Concepts

4h

Unit 2: Data Warehouse Architecture

4 study hours
  • •Explain the term data warehouse architecture.
  • •List the three types of data warehouse architecture.
  • •Describe the components of data warehouse architecture.
Week
11

Module 3: Data Warehousing Concepts

4h

Unit 3: Data Warehouse Design

4 study hours
  • •Differentiate between a logical and physical design.
  • •List the basic methodologies used in building a data warehouse.
  • •Explain the phases involved in developing a data warehouse.
Week
12

Module 3: Data Warehousing Concepts

4h

Unit 4: Data Warehouse and OLAP Technology

4 study hours
  • •State the meaning of OLAP.
  • •Differentiate between OLAP and data warehouse.
  • •List the different types of OLAP server.
Week
13

Module 3: Data Warehousing Concepts

4h

Final Revision

4 study hours
  • •Review all modules and units.
  • •Work on assignments and TMAs.
  • •Prepare for final examinations.

This study schedule is in beta and may not be accurate. Please use it as a guide and consult the course outline for the most accurate information.

Course PDF Material

Read the complete course material as provided by NOUN.

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Study Tips & Exam Preparation

Expert tips to help you succeed in this course

1

Review all module objectives and summaries.

2

Practice solving data mining problems from the textbook.

3

Create concept maps linking data mining techniques to specific applications.

4

Focus on understanding the differences between OLTP and OLAP systems.

5

Study data preprocessing methods and their impact on data quality.

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