Data Mining
3.6 (12 ratings)
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Data Mining

An introductory course about understanding patterns, process, tools of data mining.
3.6 (12 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
287 students enrolled
Last updated 3/2015
English
Current price: $10 Original price: $50 Discount: 80% off
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Includes:
  • 2 hours on-demand video
  • 1 min on-demand audio
  • 12 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Be introduced to data mining, its advantages and disadvantages.
  • Be aware of the importance of visualizing data.
  • Know the usefulness of data mining in different businesses.
  • Look forward to applying data mining to their businesses.
View Curriculum
Requirements
  • Basic understanding of the IT industry
  • Knowledge of the English language
Description

Uncover the essential tool for information management professionals known as Data Mining.

Data mining is the process of extracting patterns from large data sets by connecting methods from statistics and artificial intelligence with database management. Although a relatively young and interdisciplinary field of computer science, data mining involves analysis of large masses of data and conversion into useful information.

This introductory course will discuss: its involvement in the 9-step KDD process, which data can be mined and used to enhance businesses, data patterns which can be visualized to understand the data better, the process, tools, and its future by modern standards. It will also talk about the increasing importance of transforming unprecedented quantities of digital data into business intelligence giving users an informational advantage.

Who is the target audience?
  • IT managers looking to improve data management and analysis techniques.
  • Data analysts investigating the processes and tools of successful Data Mining.
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Curriculum For This Course
Expand All 58 Lectures Collapse All 58 Lectures 05:20:04
+
Introduction to Knowledge Discovery in Databases
5 Lectures 15:20

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

.

Section Outline

Lecture 2: Definition of Knowledge Discovery in Databases

Lecture 3: Techniques in Knowledge Discovery in Databases

Lecture 4: Process in Knowledge Discovery in Databases

Preview 00:33

This lecture will talk about the definition of Knowledge Discovery in Databases.

Preview 02:21

This lecture will identify the different techniques in Knowledge Discovery in Databases.

Preview 01:57

This discussion will focus on the process involved in Knowledge Discovery in Databases.

.

Lecture outline:

0:00    Introduction of the Process
	in Knowledge Discovery in Databases
1:36    Developing
2:35    Selecting
4:02    Preprocessing and Cleansing
5:11    Transformation
6:37    Choosing the Task
7:41    Choosing the Algorithm
8:40    Employing
9:04    Evaluation
9:42    Using the Knowledge
Preview 10:29

Section 1 - Quiz
6 questions

This PDF file contains Section 1 of this course. You can download the complete e book at the end of this course.

Reading Assignment Section 1
9 pages
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Introduction to Data Mining
10 Lectures 20:49

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

.

Section Outline

Lecture 7: Definition of Data Mining

Lecture 8: Styles of Learning

Lecture 9: Advantages of in Data Mining

Lecture 10: Disadvantages of in Data Mining

Lecture 11: Data

Lecture 12: Information and Knowledge

Lecture 13: Data Warehouses

Lecture 14: Decision Tree Learning

Preview 00:28

This lecture will talk about the definition of data mining.

Definition of Data Mining
03:04

This discussion will focus on the different styles of learning.

Styles of Learning
00:54

This lecture will identify the different advantages of data mining.

Advantages in Data Mining
04:21

This lecture will identify the different disadvantages of data mining.

Disadvantages in Data Mining
04:21

This lecture will cover the different aspects of data.

Data
00:42

This lecture will identify the definition and differences between information and knowledge.

Information and Knowledge
00:46

This lecture will explain the data warehouse.

Data Warehouses
00:55

This lecture will discuss the definition and process of decision tree learning.

.

Lecture outline:

0:00    Definition of the Decision Tree
1:44    Types of Decision Tree
2:04    Advantages
3:45    Limitations
Decision Tree Learning
05:18

Section 2 - Quiz
6 questions

This PDF file contains Section 2 of this course. You can download the complete e book at the end of this course.

Reading Assignment Section 2
12 pages
+
Minable Data
4 Lectures 15:18

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

.

Section Outline

Lecture 17: Types of Data Studied in Data Mining

Lecture 18: Minable Information

Preview 00:23

This lecture will identify the different types of data studied in data mining.

.

Lecture outline:

0:00    Introduction to the Types
	of Data Studied
0:53    Flat Files
1:22    Relational Databases
1:52    Data Warehouses
2:12    Transaction Databases
3:00    Multimedia Databases
3:36    Spatial Databases
3:55    Time-Series Databases
4:26    World Wide Web
Types of Data Studied in Data Mining
05:37

This lecture will discuss information that is minable.

.

Lecture outline:

0:00    Minable Information
0:25    Business Transactions
1:22    Scientific Data
2:01    Medical and Personal Data
2:50    Surveillance Video and Pictures
3:19    Satellite Sensing
4:20    Games
5:09    Digital Media
6:01    CAD and Software Engineering Data
6:54    Virtual Worlds
7:50    Test Reports and Memos
8:24    The World Wide Web Repositories
Minable Information
09:18

Section 3 - Quiz
6 questions

This PDF file contains Section 3 of this course. You can download the complete e book at the end of this course.

Reading Assignment Section 3
10 pages
+
Visualizing Data Patterns
7 Lectures 26:02

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

.

Section Outline

Lecture 21: Introduction to Visualizing Data Patterns

Lecture 22: Orienteering

Lecture 23: Why Visualize?

Lecture 24: Trusting a Model

Lecture 25: Understanding a Model

Preview 00:23

This lecture will talk about visualizing patterns of data.

Introduction to Visualizing Data Patterns
01:59

This lecture will discuss orienteering.

Orienteering
02:15

This discussion will focus on the reason why visualization is needed.

Why Visualize?
04:37

This lecture will explain the reason trust is needed when visualizing models.

Trusting a Model
06:39

This discussion will focus on understanding a visual model.

Understanding a Model
10:09

Section 4 - Quiz
6 questions

This PDF file contains Section 4 of this course. You can download the complete e book at the end of this course.

Reading Assignment Section 4
14 pages
+
Data Mining Process
7 Lectures 13:27

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

.

Section Outline

Lecture 28: What Can Data Mining Do?

Lecture 29: Types of Data Sets

Lecture 30: Data Mining Process

Lecture 31: Conclusion

Lecture 32: Process Flow

Preview 00:26

This lecture will discuss what data mining can do.

What Can Data Mining Do?
03:35

This lecture will identify the different types of data sets.

Types of Data Sets
00:31

This discussion will focus on the process involved in data mining.

Data Mining Process
03:57

This lecture will explain the data process flow.

.

Lecture outline:

0:00    The Process Flow
0:16    Problem Definition
1:16    Data Gathering and Preparation
2:50    Model Building and Evaluation
3:57    Knowledge Deployment
Process Flow
04:31

This lecture will talk about the conclusion in the data mining process.

Conclusion
00:27

Section 5 - Quiz
6 questions

This PDF file contains Section 5 of this course. You can download the complete e book at the end of this course.

Reading Assignment Section 5
8 pages
+
Data Mining Tools
5 Lectures 12:41

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

.

Section Outline

Lecture 35: Introduction to Tools

Lecture 36: Data Mining Tools

Lecture 37: Data Mining Techniques

Preview 00:24

This lecture will give an overview of the tools involved in data mining.

Introduction to Tools
04:01

This lecture will specifically identify the different tools required in data mining.

.

Lecture outline:

0:00    Introduction to Data Mining TOols
1:01    Categories of Data Mining Tools
1:15    Traditional Data Mining Tools
1:52    Dashboards
2:26    Text-mining Tools
3:20    Other Applications and Programs
Data Mining Tools
04:30

This lecture will identify the different techniques utilized in data mining.

.

Lecture outline:

0:00    Introduction to Data Mining Techniques
0:19    Artificial Neural Networks
0:48    Decision Trees
1:13    Rule Induction
1:20    Genetic Algorithms
1:29    Nearest-Neighbor Method
1:43    Advantages and Disadvantages
2:20    Applying Advanced Data Mining
Data Mining Techniques
03:46

Section 6 - Quiz
6 questions

This PDF file contains Section 6 of this course. You can download the complete e book at the end of this course.

Reading Assignment Section 6
8 pages
+
Usefulness and Future of Data Mining
14 Lectures 18:00

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

.

Section Outline

Lecture 40: Introduction to Metadata

Lecture 41: Metadata Defined

Lecture 42: Advantages of Metadata

Lecture 43: Metadata Categories

Lecture 44: Examples of Metadata

Lecture 45: Introduction to Metadata

Lecture 46: Metadata Defined

Lecture 47: Advantages of Metadata

Lecture 48: Metadata Categories

Lecture 49: Examples of Metadata

Lecture 50: Metadata Categories

Lecture 51: Examples of Metadata

Preview 00:24

This lecture will identify the different ways data mining is useful.

Usefulness of Data Mining
00:22

This lecture will discuss one of the ways data mining is useful, namely with basket analysis.

Basket Analysis
02:07

This lecture will discuss one of the ways data mining is useful, namely with sales forecasting.

Sales Forecasting
00:51

This lecture will discuss one of the ways data mining is useful, namely with database marketing.

Database Marketing
01:15

This lecture will discuss one of the ways data mining is useful, namely with merchandising planning.

Merchandise Planning
01:08

This lecture will discuss one of the ways data mining is useful, namely with card marketing.

Card Marketing
01:00

This lecture will discuss one of the ways data mining is useful, namely with call detail record analysis.

Call Detail Record Analysis
01:16

This lecture will discuss one of the ways data mining is useful, namely with customer loyalty.

Customer Loyalty
01:31

This lecture will discuss one of the ways data mining is useful, namely with marketing segmentation.

Marketing Segmentation
01:24

This lecture will discuss one of the ways data mining is useful, namely with production.

Product Production
01:12

This lecture will discuss one of the ways data mining is useful, namely with warranties.

Warranties
00:28

This lecture will discuss the future of data mining.

Future of Data Mining
05:02

Section 7 - Quiz
6 questions

This PDF file contains Section 7 of this course. You can download the complete e book at the end of this course.

Reading Assignment Section 7
10 pages
+
Course Resources
3 Lectures 00:00

This e book contains the entire Data Mining course in a PDF format.

Data Mining Complete Certification Kit Book
114 pages

This e book is a list of terms and definitions often used in the field of data mining.

Data Mining Glossary of Terms
3 pages

This PDF file contains all the answers to all the quizzes in each section of this course.

Quiz Answer Sheet
7 pages
+
Data Mining Certification
3 Lectures 01:27

Now that you've finished your Udemy course, - you are eligible to sit your official Certification exam.

Certification is not mandatory.

Once you've completed the course, email our exam department at exams@artofservice.com.au to purchase your exam voucher and sit your final exam.

. Access includes a step-by-step procedure on how to take the final exam and how to obtain your exam certification.

You will receive a PDF certificate through your email upon passing the examination.

Final Exam
1 page

We are always in the process of improving our courses and procedures for a better learning experience for our students. Your input is very important to us.

Follow the step-by-step procedure on taking the evaluation and receiving your certificate of completion.

Evaluation Form
1 page

A final message from our CEO.

Conclusion - Final Lecture
01:27
About the Instructor
The Art Of Service
3.4 Average rating
413 Reviews
4,971 Students
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