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Separate Strings

Separate Strings

This example teaches you how to separate strings in Excel.
Separate Strings Example
The problem we are dealing with is that we need to tell Excel where we want to separate the string. In case of Smith, Mike the comma is at position 6 while in case of Williams, Janet the comma is at position 9.
1. To get the first name, use the formula below.
First Name
Explanation: to find the position of the comma, use the FIND function (position 6). To get the length of a string, use the LEN function (11 characters). =RIGHT(A2,LEN(A2)-FIND(",",A2)-1) reduces to =RIGHT(A2,11-6-1). =RIGHT(A2,4) extracts the 4 rightmost characters and gives the desired result (Mike).
2. To get the last name, use the following formula.
Last Name
Explanation: to find the position of the comma, use the FIND function (position 6). =LEFT(A2,FIND(",", A2)-1) reduces to =LEFT(A2,6-1). =LEFT(A2,5) extracts the 5 leftmost characters and gives the desired result (Smith).
3. Select the range B2:C2 and drag it down.
Separate Strings Result
Refer: http://www.excel-easy.com/data-analysis.html
This example teaches you how to separate strings in Excel.
Separate Strings Example
The problem we are dealing with is that we need to tell Excel where we want to separate the string. In case of Smith, Mike the comma is at position 6 while in case of Williams, Janet the comma is at position 9.
1. To get the first name, use the formula below.
First Name
Explanation: to find the position of the comma, use the FIND function (position 6). To get the length of a string, use the LEN function (11 characters). =RIGHT(A2,LEN(A2)-FIND(",",A2)-1) reduces to =RIGHT(A2,11-6-1). =RIGHT(A2,4) extracts the 4 rightmost characters and gives the desired result (Mike).
2. To get the last name, use the following formula.
Last Name
Explanation: to find the position of the comma, use the FIND function (position 6). =LEFT(A2,FIND(",", A2)-1) reduces to =LEFT(A2,6-1). =LEFT(A2,5) extracts the 5 leftmost characters and gives the desired result (Smith).
3. Select the range B2:C2 and drag it down.
Separate Strings Result
Refer: http://www.excel-easy.com/data-analysis.html
Separate Strings
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10 things that wowed me in Excel 2013

10 things that wowed me in Excel 2013

I have used Flash Fill and it make me surprised.

http://chandoo.org/wp/2013/04/03/best-new-features-in-excel-2013/
I have used Flash Fill and it make me surprised.

http://chandoo.org/wp/2013/04/03/best-new-features-in-excel-2013/
10 things that wowed me in Excel 2013
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Date 13/800: thứ 2 14-10-2013: tạo calculation từ cube

Date 13/800: thứ 2 14-10-2013: tạo calculation từ cube

Ta có thể tạo các tính toán phức tạp trên cube thông qua calculation.



Ta có thể tạo các tính toán phức tạp trên cube thông qua calculation.



Date 13/800: thứ 2 14-10-2013: tạo calculation từ cube
https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiuh61LhX0k1Y-b_kGxY4LCUwIO2EIpVQAIvjeMQ06T_3WfAm8FID-cd0UklTyVTyluM4LPXaZnKVcjthyOBO_yuXQV-bRtEH1KttE73rcXgQrgpugBljvfHdXEmAQHLL951_QgeBFhb3s/s72-c/create-calculation-cube-ssas.png
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Date 12/800

Date 12/800

Sự khác biệt giữa Star schema và snowflake schema

In a star schema, you create dimensions from single
tables joined to a fact table. In a snowfl ake schema, you create dimensions from two or more
joined dimension tables.
Sự khác biệt giữa Star schema và snowflake schema

In a star schema, you create dimensions from single
tables joined to a fact table. In a snowfl ake schema, you create dimensions from two or more
joined dimension tables.
Date 12/800
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Date 12/800: thứ hai 7-10-2013: BI semantic model

Date 12/800: thứ hai 7-10-2013: BI semantic model

http://www.mssqltips.com/sqlservertip/2818/understanding-the-sql-server-2012-bi-semantic-model-bism/

Đại khái là nó giống như 1 nền tảng BI thống nhất, unified BI flatform, cho phép người dùng có thể dùng multidimensional model hay tabular model, kết hợp những công nghệ hiện tại và công nghệ mới

--------------

SSAS 2012 cung cấp 3 cách tiếp cận để xây dựng BI semantic model: multidimensional, tabular and power pivot.

http://msdn.microsoft.com/en-us/library/hh212940(v=sql.110).aspx

BISM được xây dựng như 1 model thống nhất cho mọi đối tượng người dùng, từ user đến developer.

http://www.codeproject.com/Articles/506032/WhatplusisplusBIplusSemanticplusmodelplus-BISM-plu

http://www.mssqltips.com/sqlservertip/2818/understanding-the-sql-server-2012-bi-semantic-model-bism/

Đại khái là nó giống như 1 nền tảng BI thống nhất, unified BI flatform, cho phép người dùng có thể dùng multidimensional model hay tabular model, kết hợp những công nghệ hiện tại và công nghệ mới

--------------

SSAS 2012 cung cấp 3 cách tiếp cận để xây dựng BI semantic model: multidimensional, tabular and power pivot.

http://msdn.microsoft.com/en-us/library/hh212940(v=sql.110).aspx

BISM được xây dựng như 1 model thống nhất cho mọi đối tượng người dùng, từ user đến developer.

http://www.codeproject.com/Articles/506032/WhatplusisplusBIplusSemanticplusmodelplus-BISM-plu

Date 12/800: thứ hai 7-10-2013: BI semantic model
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Date 11/800

Date 11/800

Measures

If you had a calculated measure that returned the percentage of sales along some dimension, what type of measure would this be?
Choose your answer:

Measures

If you had a calculated measure that returned the percentage of sales along some dimension, what type of measure would this be?
Choose your answer:
Date 11/800
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Date 11/800: chủ nhật 6-10-2013

Date 11/800: chủ nhật 6-10-2013

MDX: multi dimensional eXpression:

Just as Structured Query Language (SQL) is a query language used to retrieve data from relational
databases, Multi-Dimensional eXpressions (MDX) is a query language used to retrieve data from
Analysis Services databases. MDX supports two distinct modes:
‰ Expressions language: Defi ne and manipulate Analysis Services objects and data to calculate
values.
‰ Query language: Retrieve data from Analysis Services.
MDX was originally designed by Microsoft and introduced in 1998 with SQL Server Analysis
Services 7.0, but it is nevertheless a general, standards-based query language to retrieve data from
OLAP databases. Many other OLAP providers support MDX, including Microstrategy’s Intelligence
Server, Hyperion’s Essbase Server, and SAS’s Enterprise BI Server. There are those who want to
extend the standard for additional functionality, and MDX extensions have indeed been developed
by individual vendors, but the constituent parts of any extension are expected to be consistent with
the MDX standard. Analysis Services provides several extensions to the MDX standard defi ned by
the OLE DB for OLAP specifi cation. In this book you learn about the form of MDX supported by
SQL Server Analysis Services 2012.

Khái niệm về Measure Group
Measure Groups: Measure groups are composed of one or more columns of a fact
table that, in turn, are composed of the data to be aggregated and analyzed. Measure
groups combine multiple measures under a single entity.

Business intelligence is nothing more than analyzing your data and making
actionable decisions.

Trả lời 1 câu hỏi liên quan đến Analysis services, nó hỏi ssas có thể chạy trong mode nào, tôi trả lồi windows authentication và nó đã đúng,
tham khảo: http://msdn.microsoft.com/en-us/library/ms144288(SQL.90).aspx
MDX: multi dimensional eXpression:

Just as Structured Query Language (SQL) is a query language used to retrieve data from relational
databases, Multi-Dimensional eXpressions (MDX) is a query language used to retrieve data from
Analysis Services databases. MDX supports two distinct modes:
‰ Expressions language: Defi ne and manipulate Analysis Services objects and data to calculate
values.
‰ Query language: Retrieve data from Analysis Services.
MDX was originally designed by Microsoft and introduced in 1998 with SQL Server Analysis
Services 7.0, but it is nevertheless a general, standards-based query language to retrieve data from
OLAP databases. Many other OLAP providers support MDX, including Microstrategy’s Intelligence
Server, Hyperion’s Essbase Server, and SAS’s Enterprise BI Server. There are those who want to
extend the standard for additional functionality, and MDX extensions have indeed been developed
by individual vendors, but the constituent parts of any extension are expected to be consistent with
the MDX standard. Analysis Services provides several extensions to the MDX standard defi ned by
the OLE DB for OLAP specifi cation. In this book you learn about the form of MDX supported by
SQL Server Analysis Services 2012.

Khái niệm về Measure Group
Measure Groups: Measure groups are composed of one or more columns of a fact
table that, in turn, are composed of the data to be aggregated and analyzed. Measure
groups combine multiple measures under a single entity.

Business intelligence is nothing more than analyzing your data and making
actionable decisions.

Trả lời 1 câu hỏi liên quan đến Analysis services, nó hỏi ssas có thể chạy trong mode nào, tôi trả lồi windows authentication và nó đã đúng,
tham khảo: http://msdn.microsoft.com/en-us/library/ms144288(SQL.90).aspx
Date 11/800: chủ nhật 6-10-2013
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Date 10/800: thứ 6 04-10-2013

Date 10/800: thứ 6 04-10-2013

Data Sources: Your data warehouse is likely made up of disparate data sources such as
Microsoft SQL Server, Oracle, DB2, Teradata, and so forth. Analysis Services can easily deal
with retrieving relational data from various relational databases. Data source objects contain
information needed to connect to a data source such as server name, catalog or database
name, and login credentials. You establish connections to relational servers by creating a data
source for each one.
‰ Data Source Views: When working with a large operational data store, you don’t always
want to use all the tables in the database. With Data Source Views (DSVs), you can limit the
number of visible tables by including only the tables that are relevant to your analysis. DSVs
enable you to create a logical data model upon which you build your multidimensional database.
A DSV can contain tables from one or more data sources. Data sources and DSVs are
discussed in Chapter 4.
Data Sources: Your data warehouse is likely made up of disparate data sources such as
Microsoft SQL Server, Oracle, DB2, Teradata, and so forth. Analysis Services can easily deal
with retrieving relational data from various relational databases. Data source objects contain
information needed to connect to a data source such as server name, catalog or database
name, and login credentials. You establish connections to relational servers by creating a data
source for each one.
‰ Data Source Views: When working with a large operational data store, you don’t always
want to use all the tables in the database. With Data Source Views (DSVs), you can limit the
number of visible tables by including only the tables that are relevant to your analysis. DSVs
enable you to create a logical data model upon which you build your multidimensional database.
A DSV can contain tables from one or more data sources. Data sources and DSVs are
discussed in Chapter 4.
Date 10/800: thứ 6 04-10-2013
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Date 9/800: Mining structure and model

Date 9/800: Mining structure and model

Structures and Models As detailed in Chapter 3, SQL Server Analysis Services has two major objects that deal with data mining: mining structures and mining models. A mining structure defines the domain of a mining problem, whereas a mining model is the application of a mining algorithm to the data in a mining structure. A mining structure contains a list of structure columns that have data and content types, bindings to the data source, and some optional flags that control how the data is modeled. Additionally, a mining structure contains a list of mining models that use the columns from the structure. The definition of a mining model contains an algorithm with its associated parameters, plus a list of columns from the mining structure. Each model in a structure can use a different algorithm, or the same algorithm with different parameters, and/or a different subset of the columns in the structure. For each column in the model, you can assign how it is to be used in that model, as well as algorithm-specific modeling flags. This feature allows you to easily test different hypotheses on the same data set.
Structures and Models As detailed in Chapter 3, SQL Server Analysis Services has two major objects that deal with data mining: mining structures and mining models. A mining structure defines the domain of a mining problem, whereas a mining model is the application of a mining algorithm to the data in a mining structure. A mining structure contains a list of structure columns that have data and content types, bindings to the data source, and some optional flags that control how the data is modeled. Additionally, a mining structure contains a list of mining models that use the columns from the structure. The definition of a mining model contains an algorithm with its associated parameters, plus a list of columns from the mining structure. Each model in a structure can use a different algorithm, or the same algorithm with different parameters, and/or a different subset of the columns in the structure. For each column in the model, you can assign how it is to be used in that model, as well as algorithm-specific modeling flags. This feature allows you to easily test different hypotheses on the same data set.
Date 9/800: Mining structure and model
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Date 8/800: Why Data Mining

Date 8/800: Why Data Mining

Thứ Ba 1-10-2013

That is, the capability to store data has greatly outpaced the capability to
process it.

How decision trees works:

In this case, each path from the root node to the leaf node
forms a rule about the data.
Thứ Ba 1-10-2013

That is, the capability to store data has greatly outpaced the capability to
process it.

How decision trees works:

In this case, each path from the root node to the leaf node
forms a rule about the data.
Date 8/800: Why Data Mining
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Date 7/800: Microsoft Decision Trees

Date 7/800: Microsoft Decision Trees

The Microsoft Decision Trees algorithm can be used for three different data
mining tasks: classification, regression, and association.

what is regression??

Ebook: Data Mining with sql server 2008
The Microsoft Decision Trees algorithm can be used for three different data
mining tasks: classification, regression, and association.

what is regression??

Ebook: Data Mining with sql server 2008
Date 7/800: Microsoft Decision Trees
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Date 6/800: chủ nhật 29-9-2013: just relax

Date 6/800: chủ nhật 29-9-2013: just relax

Date 6/800: chủ nhật 29-9-2013: just relax
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Date 5/800: đi mua laptop

Date 5/800: đi mua laptop

Thứ bảy 28-09-2013

Đi mua laptop
Thứ bảy 28-09-2013

Đi mua laptop
Date 5/800: đi mua laptop
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Date 4/800: Excel 2010 Freeze Panes

Date 4/800: Excel 2010 Freeze Panes

http://www.excel-2010.com/freeze-panes-in-excel/
Date 4/800: Excel 2010 Freeze Panes
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Date 4/800: Wrapping Text In Excel

Date 4/800: Wrapping Text In Excel

27-09-2013

A useful tip about Bullet Points In Excel

This is a strange one. Although there is no bullet point command on the ribbon, there is a keyboard shortcut. Position the cursor where you need a bullet and press ALT-0149. You may need to insert a space before you start typing, otherwise it will look a little cramped. Unfortunately, there is no way to select text you’ve already typed and then apply bullet points to it (like you can do in Microsoft Word).
27-09-2013

A useful tip about Bullet Points In Excel

This is a strange one. Although there is no bullet point command on the ribbon, there is a keyboard shortcut. Position the cursor where you need a bullet and press ALT-0149. You may need to insert a space before you start typing, otherwise it will look a little cramped. Unfortunately, there is no way to select text you’ve already typed and then apply bullet points to it (like you can do in Microsoft Word).
Date 4/800: Wrapping Text In Excel
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Date 4/800: Excel 2010 Sparklines

Date 4/800: Excel 2010 Sparklines

27-9-2013

If we want to show trends, we could use sparklines in Excel.

http://www.excel-2010.com/sparklines-in-excel/
27-9-2013

If we want to show trends, we could use sparklines in Excel.

http://www.excel-2010.com/sparklines-in-excel/
Date 4/800: Excel 2010 Sparklines
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Date 4/800: Format number trong Excel

Date 4/800: Format number trong Excel

27-09-2013

This is an issue my wife often meets when she wants to display the number like '0123', it will be '123' in Excel

In the article the author has described the way to handle this.

Format cells -> Custom -> Type 0000.

Refer: http://chandoo.org/wp/2008/06/16/formatting-numbers-in-excel-few-tips/
27-09-2013

This is an issue my wife often meets when she wants to display the number like '0123', it will be '123' in Excel

In the article the author has described the way to handle this.

Format cells -> Custom -> Type 0000.

Refer: http://chandoo.org/wp/2008/06/16/formatting-numbers-in-excel-few-tips/
Date 4/800: Format number trong Excel
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Date 4/800: dùng hàm Offset trong Excel

Date 4/800: dùng hàm Offset trong Excel

I often download software product sales information listed by country . I need to track
 revenues from Iran as well as costs and units sold, but the data about Iran isn’t always in
the same location in the worksheet . Can I create a formula that will always pick out Iran’s
 revenues, costs, and units sold?
I often download software product sales information listed by country . I need to track
 revenues from Iran as well as costs and units sold, but the data about Iran isn’t always in
the same location in the worksheet . Can I create a formula that will always pick out Iran’s
 revenues, costs, and units sold?
Date 4/800: dùng hàm Offset trong Excel
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Date 4/800: A concept of Microsoft Decision Trees

Date 4/800: A concept of Microsoft Decision Trees

Decision Trees
A decision tree is a form of classification shown in a tree structure, in which a node in the tree structure represents each question used to further classify data. The various methods used to create decision trees have been used widely for decades, and there is a large body of work describing these statistical techniques. For more information about the decision trees technique and the Microsoft® Decision Trees algorithm, see Microsoft Decision Trees.
http://technet.microsoft.com/en-us/library/aa178248(v=sql.80).aspx

Decision Trees
A decision tree is a form of classification shown in a tree structure, in which a node in the tree structure represents each question used to further classify data. The various methods used to create decision trees have been used widely for decades, and there is a large body of work describing these statistical techniques. For more information about the decision trees technique and the Microsoft® Decision Trees algorithm, see Microsoft Decision Trees.
http://technet.microsoft.com/en-us/library/aa178248(v=sql.80).aspx

Date 4/800: A concept of Microsoft Decision Trees
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Date 3/800: Decision Trees

Date 3/800: Decision Trees

Đây là công nghệ data mining phổ biến nhất.  Ta dùng nó để xác định một tập dữ liệu có thuộc về một dạng nào đó hay không. Ví dụ một đơn cho vay tiền có thể là high risk or low risk.

Ý tưởng cơ bản của Decision Trees là chia data ra nhiều subset con một cách đệ quy.
Đây là công nghệ data mining phổ biến nhất.  Ta dùng nó để xác định một tập dữ liệu có thuộc về một dạng nào đó hay không. Ví dụ một đơn cho vay tiền có thể là high risk or low risk.

Ý tưởng cơ bản của Decision Trees là chia data ra nhiều subset con một cách đệ quy.
Date 3/800: Decision Trees
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Date 3/800:

Date 3/800:

Done: Creating an Analysis Services Project (Basic Data Mining Tutorial)

http://technet.microsoft.com/en-us/library/784c0401-0358-4117-9c85-4e8220ce71d9

Done: Creating a Data Source (Basic Data Mining Tutorial)

The data source contains the names of the server and database where your source data resides, in addition to any other required connection properties.

http://technet.microsoft.com/en-us/library/d7107c32-69ed-49a8-9b6e-32753eebf42c

Done: Creating a Data Source View (Basic Data Mining Tutorial)

http://technet.microsoft.com/en-us/library/c1e68a88-0f82-415d-becc-78d180d4f845

Lúc tạo Mining structure có 2 option là with a mining model and no model.

Done: Creating a Targeted Mailing Mining Model Structure (Basic Data Mining Tutorial)
http://technet.microsoft.com/en-us/library/75cd508f-b126-418b-848d-3c4c3e6c303f

Done: Specifying the Data Type and Content Type (Basic Data Mining Tutorial)

Done: Specifying a Testing Data Set for the Structure (Basic Data Mining Tutorial)




Done: Creating an Analysis Services Project (Basic Data Mining Tutorial)

http://technet.microsoft.com/en-us/library/784c0401-0358-4117-9c85-4e8220ce71d9

Done: Creating a Data Source (Basic Data Mining Tutorial)

The data source contains the names of the server and database where your source data resides, in addition to any other required connection properties.

http://technet.microsoft.com/en-us/library/d7107c32-69ed-49a8-9b6e-32753eebf42c

Done: Creating a Data Source View (Basic Data Mining Tutorial)

http://technet.microsoft.com/en-us/library/c1e68a88-0f82-415d-becc-78d180d4f845

Lúc tạo Mining structure có 2 option là with a mining model and no model.

Done: Creating a Targeted Mailing Mining Model Structure (Basic Data Mining Tutorial)
http://technet.microsoft.com/en-us/library/75cd508f-b126-418b-848d-3c4c3e6c303f

Done: Specifying the Data Type and Content Type (Basic Data Mining Tutorial)

Done: Specifying a Testing Data Set for the Structure (Basic Data Mining Tutorial)




Date 3/800:
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Date 2/800: Questions

Date 2/800: Questions

làm sao định nghĩa một Data mining structure, một data mining model
làm sao định nghĩa một Data mining structure, một data mining model
Date 2/800: Questions
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Date 2/800: Microsoft Association Rules

Date 2/800: Microsoft Association Rules

MAR được thiết kế để phân tích mối liên hệ (Association Analysis). Đây là một phương thức điển hình để phân tích giỏ hàng.
MAR được thiết kế để phân tích mối liên hệ (Association Analysis). Đây là một phương thức điển hình để phân tích giỏ hàng.
Date 2/800: Microsoft Association Rules
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Date 2/800: Một định nghĩa về Data Profiling

Date 2/800: Một định nghĩa về Data Profiling

Data profiling là 1 quá trình phân tích Raw Data để có cái nhìn sâu bên trong. Mục tiêu của nó là phân tích metadata trong khi metadata này ko available khi nó available.

Data profiling bao gồm các giải thuật phân tích và thống kê khác nhau.

Data profiling is a process of analyzing raw data for the purpose of characterizing
the information embedded within a data set. Data profiling consists of different
statistical and analytical algorithms that provide insight into the content of data sets,
and qualitative characteristics of those values. One goal of profiling data is to
discover metadata when it is not available and to validate metadata when it is
available. Data profiling incorporates column analysis, data type determination,
cross-table analyses, and exploration and discovery of relationships and dependencies
across columns. The result is a constructive process of information inference to prepare a data set for later integration.
Data profiling là 1 quá trình phân tích Raw Data để có cái nhìn sâu bên trong. Mục tiêu của nó là phân tích metadata trong khi metadata này ko available khi nó available.

Data profiling bao gồm các giải thuật phân tích và thống kê khác nhau.

Data profiling is a process of analyzing raw data for the purpose of characterizing
the information embedded within a data set. Data profiling consists of different
statistical and analytical algorithms that provide insight into the content of data sets,
and qualitative characteristics of those values. One goal of profiling data is to
discover metadata when it is not available and to validate metadata when it is
available. Data profiling incorporates column analysis, data type determination,
cross-table analyses, and exploration and discovery of relationships and dependencies
across columns. The result is a constructive process of information inference to prepare a data set for later integration.
Date 2/800: Một định nghĩa về Data Profiling
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Date 2/800: Basic Data Mining Tutorial

Date 2/800: Basic Data Mining Tutorial

Dùng link này để tham khảo: http://technet.microsoft.com/en-us/library/ms167167.aspx

 Kịch bản đặt ra là tôi là nhân viên của 1 công ty, được giao task tìm hiểu khách hàng của công ty dựa vào những lần mua sắm trước đó được lưu trong database, tôi sẽ phân tích và đưa ra những dự đoán để phục vụ cho bộ phận marketing.

 Công ty chưa từng làm data mining trước đó, do đó có thể tôi cần tạo ra 1 database chuyên dụng cho data mining và tạo ra các data mining model để phân tích.
Dùng link này để tham khảo: http://technet.microsoft.com/en-us/library/ms167167.aspx

 Kịch bản đặt ra là tôi là nhân viên của 1 công ty, được giao task tìm hiểu khách hàng của công ty dựa vào những lần mua sắm trước đó được lưu trong database, tôi sẽ phân tích và đưa ra những dự đoán để phục vụ cho bộ phận marketing.

 Công ty chưa từng làm data mining trước đó, do đó có thể tôi cần tạo ra 1 database chuyên dụng cho data mining và tạo ra các data mining model để phân tích.
Date 2/800: Basic Data Mining Tutorial
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Plan

Plan

Tuần này: 23-9-2013: master Basic Data Mining Tutorial
http://technet.microsoft.com/en-us/library/ms167167.aspx

Trong tuần sau:

Xem: Data Source Views in Multidimensional Models

------------------

Hết tháng 10-2013:

  • master các BI algrrith của Microsoft dùng SSAS
  • Đọc xong ebook data mining with sql server 2008
Hết tháng 11-2013:
  • master Excel analyst
Plan mới nhất: dồn hết sưc đọc ebook Wrox proffesional SSAS with MDX
Tuần này: 23-9-2013: master Basic Data Mining Tutorial
http://technet.microsoft.com/en-us/library/ms167167.aspx

Trong tuần sau:

Xem: Data Source Views in Multidimensional Models

------------------

Hết tháng 10-2013:

  • master các BI algrrith của Microsoft dùng SSAS
  • Đọc xong ebook data mining with sql server 2008
Hết tháng 11-2013:
  • master Excel analyst
Plan mới nhất: dồn hết sưc đọc ebook Wrox proffesional SSAS with MDX
Plan
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Date 1/800: Data Mining

Date 1/800: Data Mining

A define of Data Mining: Data mining is as a step in the process of knowledge discovery .

Một định nghĩa khác về Data Mining:
data mining is the process of analyzing data to find hidden patterns
using automatic methodologies.

Data Mining with Microsoft Technique:
Microsoft Association Rules
Ta dùng Microsoft Associtaion Rules để hiểu behavior của khách hàng tốt hơn. Ta có thể dự đoán khách hàng sẽ mua gì dựa vào những gì đã có trong giỏ hàng online của khách hàng hiện tại. Ví dụ cho điều này là những website, những món hàng nào có thể được mua cùng với nhau sẽ xuất hiện gần nhau cho khách hàng nhìn thấy.

 Ví dụ ta có thể phát hiện rằng 5 % khách hàng đã mua xúc xích, sốt cà chua, dưa chua với nhau, và 75% khách hàng đã mua xúc xích sẽ mua dưa chua.

 Mặc dù ta có thể dùng standard sql query để tìm ra các pattern này, có điều ta phải viết hàng trăm câu query để khám phá ra tất cả các sự kết hợp này (xúc xích, sốt cà chua...), tao có thể làm diều này dễ dàng hơn với Microsoft Association Rules.

 Tạm quên Microsoft Association Rules đi, ta nên đi từ căn bản:
http://technet.microsoft.com/en-us/library/ms167167.aspx

Chờ đợi ngày 2.
A define of Data Mining: Data mining is as a step in the process of knowledge discovery .

Một định nghĩa khác về Data Mining:
data mining is the process of analyzing data to find hidden patterns
using automatic methodologies.

Data Mining with Microsoft Technique:
Microsoft Association Rules
Ta dùng Microsoft Associtaion Rules để hiểu behavior của khách hàng tốt hơn. Ta có thể dự đoán khách hàng sẽ mua gì dựa vào những gì đã có trong giỏ hàng online của khách hàng hiện tại. Ví dụ cho điều này là những website, những món hàng nào có thể được mua cùng với nhau sẽ xuất hiện gần nhau cho khách hàng nhìn thấy.

 Ví dụ ta có thể phát hiện rằng 5 % khách hàng đã mua xúc xích, sốt cà chua, dưa chua với nhau, và 75% khách hàng đã mua xúc xích sẽ mua dưa chua.

 Mặc dù ta có thể dùng standard sql query để tìm ra các pattern này, có điều ta phải viết hàng trăm câu query để khám phá ra tất cả các sự kết hợp này (xúc xích, sốt cà chua...), tao có thể làm diều này dễ dàng hơn với Microsoft Association Rules.

 Tạm quên Microsoft Association Rules đi, ta nên đi từ căn bản:
http://technet.microsoft.com/en-us/library/ms167167.aspx

Chờ đợi ngày 2.
Date 1/800: Data Mining
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Data mining for Sales and Marketing

Data mining for Sales and Marketing

Data mining for Sales and Marketing


  • How to create stable, long-lasting predictive model
  • Làm thế nào để tạo ra mô hình dự báo lâu dài và ổn định.

  • Mining unstructured text

  • Finding patterns with undirected techniques such as clustering, association rules, and link analysis
  • Tìm kiếm patterns with các công nghệ vô hướng như phân nhóm, luật liên hệ, phân tích liên kết.

  • Modeling specific targets with directed techniques such as regression, decision trees, neural networks, and memory based reasoning
Data mining for Sales and Marketing


  • How to create stable, long-lasting predictive model
  • Làm thế nào để tạo ra mô hình dự báo lâu dài và ổn định.

  • Mining unstructured text

  • Finding patterns with undirected techniques such as clustering, association rules, and link analysis
  • Tìm kiếm patterns with các công nghệ vô hướng như phân nhóm, luật liên hệ, phân tích liên kết.

  • Modeling specific targets with directed techniques such as regression, decision trees, neural networks, and memory based reasoning
Data mining for Sales and Marketing
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Excel Data Analyst training.

Excel Data Analyst training.


  1. Làm thế nào tìm được 1 ngày là 50 ngày kể từ ngày hôm nay?

  1. Làm thế nào tìm được 1 ngày là 50 ngày kể từ ngày hôm nay?
Excel Data Analyst training.
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Useful date functions in Excel

Useful date functions in Excel

Use today() function to get current date in Excel

How do I determine a date that is 50 workdays after another date? What if I want to exclude holidays?
The function WORKDAY(start_date,#days,[holidays]) displays the date that is the number of workdays (a workday is a nonweekend day) indicated by #days after a given start date.
Use today() function to get current date in Excel

How do I determine a date that is 50 workdays after another date? What if I want to exclude holidays?
The function WORKDAY(start_date,#days,[holidays]) displays the date that is the number of workdays (a workday is a nonweekend day) indicated by #days after a given start date.
Useful date functions in Excel
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Rept function in Excel

Rept function in Excel

This function help us repeat a text in a given number.

For example:

Here is the result:

This function help us repeat a text in a given number.

For example:

Here is the result:

Rept function in Excel
https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgMK69LJeBqjmpEyIFvmMmGDK_DTgBCdtCpcvqUqFARZU8y1sfZuZqYS-gL8wQu-oH9fPuJkGb8J0LgIcFdCXNPqiPq20BpfkNK0SaiJTSvpNXBhX3qMar5SwPyK5u3wKQAZjkKTmmOo5E/s72-c/Excel-Rept-function.png
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http://www.uniteforsight.org/global-health-university/data-analysis

http://www.uniteforsight.org/global-health-university/data-analysis

http://www.uniteforsight.org/global-health-university/data-analysis
http://www.uniteforsight.org/global-health-university/data-analysis
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Ngày 1: Làm thế nào trở thành một chuyên viên phân tích dữ liệu

Ngày 1: Làm thế nào trở thành một chuyên viên phân tích dữ liệu

Ngày 1:
How to become a Data Analyst ?
http://www.thedataanalysis.com/data-analysis.html
Ngày 1:
How to become a Data Analyst ?
http://www.thedataanalysis.com/data-analysis.html
Ngày 1: Làm thế nào trở thành một chuyên viên phân tích dữ liệu
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Sưu tập ý tưởng thủy sinh

Sưu tập ý tưởng thủy sinh

Cây cổ thụ

Bãi biển

Thủy cung

Cây cổ thụ

Bãi biển

Thủy cung

Sưu tập ý tưởng thủy sinh
https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhc_W9nS2q7_QYV_q7FYS_6g9tmbRGFcbZ9r91Zs_gscBwUAx9PdW6roHgCZkxO_F84eupYOta0v7T0klNWumo2-kft9J1CI_apzoYxOV9J0klonxGu41Zdd2QQqFONeZqhd5cTLGGA4qY/s72-c/co-thu.jpg
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