Data mining theory and practical

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Week 1 data analysis
Point data analysis process, methodology, PEST, 5 w2h, logical tree), basic data analysis, data analyst capacity level, data measurement, exploration, sampling, principle and practical operation, in combination with SPSS tools use

2 weeks data mining based
Points (data mining concept, process, important link, based data processing method (missing value, extremum), correlation analysis, correlation analysis, variance analysis, chi-square analysis), the principle and practical

3 weeks data mining tool is introduced and the Modeler software use
Points using Modeler, actual data operation, for the follow-up courses)

4 weeks digging - classification
Point, C5.0 decision tree, logistic regression, the most commonly used two kinds of algorithm, the principle and practical modeling operations)

5 weeks digging - clustering
Point (hierarchical clustering, kmeans), mining association (Apriori), mining - prediction (linear regression, exponential smoothing, moving average), operating principle and practical modeling

6 weeks data mining of actual combat
(the main points in goal customer mining, for example, from the business analysis, project establishment, data processing, data preparation, variable selection, modeling, evaluation and deployment of each link, use the Modeler tool, about the whole process of modeling)

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Zero basis to master data mining
A network backup download address: http://pan.baidu.com/s/1skTCpDN password: b3s3

Now we have from the IT era into the DT era, the data is known as a new generation of oil energy, both in national and enterprise with the informationization development of many years accumulated a large amount of data, but the vast amounts of data has not fully exert their current value, in the past, people tend to be based on the collected historical data to do some simple display or analysis, but as the competition heats up, play out the value of the data itself is more and more important, therefore, data mining and data mining algorithm by the enterprises more and more popular,
This tutorial introduces the basic concept of data mining technology, function, use of personnel required capacity, use well as part of the mainstream algorithm is implemented, and data mining course embedded in the oracle database and office software of excel, the software is mainly used for storage and processing the data needed for data mining, which also use excel as a simple tool for data mining algorithm and this part is mainly used to help people have a comprehensive knowledge about data mining and probably know, on this basis, the late use SPSS MODELER specialized data mining tools combined with some cases of previous excle implementation of mining algorithm part carried on the thorough study and added some SPSS MODELER's own algorithm module,
Course mainly explained the data mining of knowledge and technology, at the same time, it also involves some of the knowledge of oracle database, SQL statements and excel function is applied,

One, the basic content about data mining:
The first lecture: preliminary data mining
Second: data mining function of
Third: basic knowledge of Excel
Fourth: Excel application demonstration
The first five lectures: the Oracle database installation
Sixth: database to demonstrate the application 1
About 7: database application demonstration 2
Eighth: data acquisition and storage
About 9: data preprocessing - related knowledge introduction
Tenth: data preprocessing part - excel
11: data preprocessing part - oracle
12: linear regression prediction algorithm - 1
The first ten basic: linear regression prediction algorithm - 2
Speaking: 14 - ID3 decision tree classification algorithm introduced
The tenth five lectures: - the decision tree classification algorithm modeling processing
16: classification algorithm, excel modeling implementation
17: correlation algorithm, Apriori algorithm introduced
About 18: correlation algorithm - oracle data processing
About 19: correlation algorithm - excel data processing
About 20: correlation algorithm - excel modeling implementation
21: clustering algorithm - kmeans algorithm introduced
22: clustering algorithm - excel modeling implementation
Good: 20 optimization algorithm 1
24: optimal solution 2

Second, SPSS MODELER data mining:
Five speakers: 20 SPSS Modeler to download and install
Speaking: 26 SPSS Modeler data acquisition and record
Article 27: the SPSS Modeler results output
About twenty-eight: SPSS Modeler data field processing
About 29: SPSS Modeler data exploration and analysis of 1
Speak: 30 SPSS Modeler data exploration and analysis of two
31: exploration and analysis of SPSS Modeler graphics 1
32: exploration and analysis of SPSS Modeler graphics 2
Good: 30 SPSS Modeler regression analysis content added
34: SPSS Modeler regression analysis modeling (prediction)
30 five lectures: SPSS Modeler logic analysis modeling (classification)
Article 36: RFM
Article 37: RFM model
Article 38: RFM model application
Speak: 39 SPSS Modeler classification - understand business
40: classification of SPSS Modeler - data understand
Article 41: classification of SPSS Modeler - data preparation
42: SPSS Modeler - the decision tree classification algorithm added
40 basic: SPSS Modeler classification decision tree modeling
-44: SPSS Modeler - neural network classification
40 five lectures: classification of SPSS Modeler - model assessment
46: SPSS Modeler correlation analysis - Apriori algorithm added
47th speak: SPSS Modeler correlation analysis - Apriori modeling
Speak: forty-eight SPSS Modeler sequence correlation analysis -
Extinguished, SPSS Modeler KMeans clustering analysis.
Fiftieth: SPSS Modeler clustering analysis - TwoStep1
Article 51: - TwoStep2 SPSS Modeler clustering analysis

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