Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Data Mining

Name: Data Mining
Rating: 3.9 (102 reviews)

An introductory course about understanding patterns, process, tools of data mining.

Created byThe Art Of Service

Last updated 3/2015

English

What you'll 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.

Course content

9 sections • 48 lectures • 2h 3m total length

Introduction and Objectives0:33
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
Definition of Knowledge Discovery in Databases2:21
This lecture will talk about the definition of Knowledge Discovery in Databases.
Techniques in Knowledge Discovery in Databases1:57
This lecture will identify the different techniques in Knowledge Discovery in Databases.

Process in Knowledge Discovery in Databases10:29

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

Introduction and Objectives0:28
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
Definition of Data Mining3:04
This lecture will talk about the definition of data mining.
Styles of Learning0:54
This discussion will focus on the different styles of learning.
Advantages in Data Mining4:21
This lecture will identify the different advantages of data mining.
Disadvantages in Data Mining4:21
This lecture will identify the different disadvantages of data mining.
Data0:42
This lecture will cover the different aspects of data.
Information and Knowledge0:46
This lecture will identify the definition and differences between information and knowledge.
Data Warehouses0:55
This lecture will explain the data warehouse.
Decision Tree Learning5:18
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

Introduction and Objectives0:23
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

Types of Data Studied in Data Mining5:37

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

Minable Information9:18

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

Introduction and Objectives0:23
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
Introduction to Visualizing Data Patterns1:59
This lecture will talk about visualizing patterns of data.
Orienteering2:15
This lecture will discuss orienteering.
Why Visualize?4:37
This discussion will focus on the reason why visualization is needed.
Trusting a Model6:39
This lecture will explain the reason trust is needed when visualizing models.
Understanding a Model10:09
This discussion will focus on understanding a visual model.

Introduction and Objectives0:26
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
What Can Data Mining Do?3:35
This lecture will discuss what data mining can do.
Types of Data Sets0:31
This lecture will identify the different types of data sets.
Data Mining Process3:57
This discussion will focus on the process involved in data mining.

Process Flow4:31

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

Conclusion0:27
This lecture will talk about the conclusion in the data mining process.

Introduction and Objectives0:24
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
Introduction to Tools4:01
This lecture will give an overview of the tools involved in data mining.

Data Mining Tools4:30

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

Data Mining Techniques3:46

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

Introduction and Objectives0:24
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
Usefulness of Data Mining0:22
This lecture will identify the different ways data mining is useful.
Basket Analysis2:07
This lecture will discuss one of the ways data mining is useful, namely with basket analysis.
Sales Forecasting0:51
This lecture will discuss one of the ways data mining is useful, namely with sales forecasting.
Database Marketing1:15
This lecture will discuss one of the ways data mining is useful, namely with database marketing.
Merchandise Planning1:08
This lecture will discuss one of the ways data mining is useful, namely with merchandising planning.
Card Marketing1:00
This lecture will discuss one of the ways data mining is useful, namely with card marketing.
Call Detail Record Analysis1:16
This lecture will discuss one of the ways data mining is useful, namely with call detail record analysis.
Customer Loyalty1:31
This lecture will discuss one of the ways data mining is useful, namely with customer loyalty.
Marketing Segmentation1:24
This lecture will discuss one of the ways data mining is useful, namely with marketing segmentation.
Product Production1:12
This lecture will discuss one of the ways data mining is useful, namely with production.
Warranties0:28
This lecture will discuss one of the ways data mining is useful, namely with warranties.
Future of Data Mining5:02
This lecture will discuss the future of data mining.

Final Exam1:00
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 support@theartofservice.com to purchase your exam voucher and sit your final exam.
Exam access includes more information to better prepare for the exam including a workbook, sample exam plus the final exam.
Please include the course name in your email for us to send the correct information.
Any questions please contact support@theartofservice.com

You will receive a PDF certificate through your email upon passing the examination.
Conclusion - Final Lecture1:27
A final message from our CEO.

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 this course is for:

IT managers looking to improve data management and analysis techniques.
Data analysts investigating the processes and tools of successful Data Mining.

Data Mining

What you'll learn

Explore related topics

Course content

Introduction to Knowledge Discovery in Databases4 lectures • 15min

Introduction to Data Mining9 lectures • 21min

Minable Data3 lectures • 15min

Visualizing Data Patterns6 lectures • 26min

Data Mining Process6 lectures • 13min

Data Mining Tools4 lectures • 13min

Usefulness and Future of Data Mining13 lectures • 18min

Course Resources1 lecture • 3min

Data Mining Certification2 lectures • 2min

Requirements

Description

Who this course is for: