Data Mining

An introductory course about understanding patterns, process, tools of data mining.
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  • Lectures 58
  • Contents Video: 2 hours
    Other: 3.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 2/2014 English

Course 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.

What are the requirements?

  • Basic understanding of the IT industry
  • Knowledge of the English language

What am I going to get from this course?

  • 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.

What 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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction to Knowledge Discovery in Databases
00:33

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

02:21

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

01:57

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

10:29

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
Section 1 - Quiz
6 questions
9 pages

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

Section 2: Introduction to Data Mining
00:28

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

03:04

This lecture will talk about the definition of data mining.

00:54

This discussion will focus on the different styles of learning.

04:21

This lecture will identify the different advantages of data mining.

04:21

This lecture will identify the different disadvantages of data mining.

00:42

This lecture will cover the different aspects of data.

00:46

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

00:55

This lecture will explain the data warehouse.

05:18

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
Section 2 - Quiz
6 questions
12 pages

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

Section 3: Minable Data
00:23

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

05:37

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
09:18

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
Section 3 - Quiz
6 questions
10 pages

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

Section 4: Visualizing Data Patterns
00:23

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

01:59

This lecture will talk about visualizing patterns of data.

02:15

This lecture will discuss orienteering.

04:37

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

06:39

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

10:09

This discussion will focus on understanding a visual model.

Section 4 - Quiz
6 questions
14 pages

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

Section 5: Data Mining Process
00:26

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

03:35

This lecture will discuss what data mining can do.

00:31

This lecture will identify the different types of data sets.

03:57

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

04:31

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
00:27

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

Section 5 - Quiz
6 questions
8 pages

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

Section 6: Data Mining Tools
00:24

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

04:01

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

04:30

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
03:46

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
Section 6 - Quiz
6 questions
8 pages

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

Section 7: Usefulness and Future of Data Mining
00:24

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

00:22

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

02:07

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

00:51

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

01:15

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

01:08

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

01:00

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

01:16

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

01:31

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

01:24

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

01:12

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

00:28

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

05:02

This lecture will discuss the future of data mining.

Section 7 - Quiz
6 questions
10 pages

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

Section 8: Course Resources
114 pages

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

3 pages

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

7 pages

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

Section 9: Data Mining Certification
1 page

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.

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.

01:27

A final message from our CEO.

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