Data Mining And Data Warehousing
- Sciences
- 300 level
- 3 credit units
- 188 pages
- 11 units
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.
About this course
- Difficulty
- Intermediate
- Study hours
- 208 hours
- Maths
- Basic
- Content
- Theoretical, practical, case study
- Practical work
- Yes
- Assignments
- Tutor marked assessments
- Final examination
What you'll read
The real module and unit structure of DAM301, taken from the course material NOUN publishes.
One paragraph, so you can see how it reads
DAM301 · UNIT 1 OVERVIEW OF DATA MINING
Basically, we made use of textbooks and online materials. You are expected to search for more literature and web references for further understanding. Each unit has references and web references that were used to develop them.
What you should be able to do
- Explain the concepts of data mining and data warehousing.
- Describe data processes and their trends.
- Apply data mining techniques to real-world problems.
- Design and implement a simple data warehouse.
- Utilize OLAP tools for data analysis.
What it prepares you for
- Data Analyst
- Data Scientist
- Business Intelligence Analyst
- Database Administrator
- Data Warehouse Architect
- Finance
- Retail
- Telecommunications
- Healthcare
- E-commerce
Where it gets hard
The units students slow down on, and what makes each one heavy.
- Module 1: Concepts of Data Mining
Unit 4: Data Mining Technologies
Understanding the architecture of data mining systems requires grasping complex interactions between various components and their functionalities.
- Module 2: Data Processes and Trends
Unit 1: Data Preparation and Preprocesses
Data cleaning techniques involve handling noisy, missing, and inconsistent data, which requires careful analysis and application of appropriate methods.
A suggested way through it
13 weeks, about 52 hours in total. Yours will differ.
- Week 1Module 1: Concepts of Data Mining
Unit 1: Overview of Data Mining · 4 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 2Module 1: Concepts of Data Mining
Unit 2: Data Description for Data Mining · 4 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 3Module 1: Concepts of Data Mining
Unit 3: Classification of Data Mining · 4 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 4Module 1: Concepts of Data Mining
Unit 4: Data Mining Technologies · 4 hours
Identify the various data mining technologies available.. Understand the principles behind neural networks and decision trees.. Explore rule induction and genetic algorithms..
- Week 5Module 2: Data Processes and Trends
Unit 1: Data Preparation and Preprocesses · 4 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 6Module 2: Data Processes and Trends
Unit 2: Data Mining Process · 4 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 7Module 2: Data Processes and Trends
Unit 3: Data Mining Applications · 4 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 8Module 2: Data Processes and Trends
Unit 4: Future Trends in Data Mining · 4 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 9Module 3: Data Warehousing Concepts
Unit 1: Overview of Data Warehouse · 4 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 10Module 3: Data Warehousing Concepts
Unit 2: Data Warehouse Architecture · 4 hours
Explain the term data warehouse architecture.. List the three types of data warehouse architecture.. Describe the components of data warehouse architecture..
- Week 11Module 3: Data Warehousing Concepts
Unit 3: Data Warehouse Design · 4 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 12Module 3: Data Warehousing Concepts
Unit 4: Data Warehouse and OLAP Technology · 4 hours
State the meaning of OLAP.. Differentiate between OLAP and data warehouse.. List the different types of OLAP server..
- Week 13Module 3: Data Warehousing Concepts
Final Revision · 4 hours
Review all modules and units.. Work on assignments and TMAs.. Prepare for final examinations..
Preparing for the exam
- Review all module objectives and summaries.
- Practice solving data mining problems from the textbook.
- Create concept maps linking data mining techniques to specific applications.
- Focus on understanding the differences between OLTP and OLAP systems.
- Study data preprocessing methods and their impact on data quality.
Questions students ask about this course
What is DAM301 about?
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.
How many units does DAM301 have?
DAM301, Data Mining And Data Warehousing, has 11 units across 3 modules, over 188 pages of course material. You can read it one unit at a time.
How many credit units is DAM301?
DAM301 carries 3 credit units, at 300 level in Sciences.
Is DAM301 hard?
DAM301 is rated intermediate level, with basic mathematical content. It is mostly theoretical, practical and case study work, and it has a practical component.
How long does DAM301 take to study?
About 208 hours of study, spread across its 11 units.
How is DAM301 assessed?
DAM301 is assessed by assignments, tutor marked assessments and final examination.
What can I do with DAM301?
Data Analyst, Data Scientist, Business Intelligence Analyst, Database Administrator and Data Warehouse Architect.