PoT - probeer de mogelijkheden van datamining zelf uit 30-10-2014

1. © 2014 IBM Corporation An IBM Proof of Technology IBM SPSS Data Mining Workshop Laila Fettah– Technical Sales Specialist Advanced Analytics Robin van Tilburg – Business analytics Specialty Architect 30 oktober 2014

2. © 2014 IBM Corporation IBM Software 2 IBM SPSS Data Mining Workshop Welcome to the Technical Exploration Center Introductions Access restrictions Restrooms Emergency Exits Smoking Policy Breakfast/Lunch/Snacks – location and times Special meal requirements?

3. © 2014 IBM Corporation IBM Software 3 IBM SPSS Data Mining Workshop Introductions Please introduce yourself Name and organization Current integration technologies/tools in use What do you want out of this Data Mining Workshop?

4. © 2014 IBM Corporation IBM Software 4 IBM SPSS Data Mining Workshop Agenda 10:00-10:10 Welcome and Introductions 10:10-11:00 Introduction to Predictive Analytics 11:00-11:30 Exercise: Navigating IBM SPSS Modeler 11:30-12:00 Exercise: Predictive in 20 Minutes 12:00-12:45 Lunch 12:45-13:30 Data Mining Methodology and Application 13:30-14:00 Exercise: Data Mining Techniques 14:00-14:30 Exercise: Deployment 14:30-14:45 Wrap-up

5. © 2014 IBM Corporation IBM Software 5 IBM SPSS Data Mining Workshop Objectives Introduction to predictive analytics and data mining Stimulate thinking about how data mining would benefit your organization Demonstrate ease of use of powerful technology Get experience in “doing” data mining See examples of existing customers and their realized ROI/benefits

7. © 2014 IBM Corporation IBM Software 7 IBM SPSS Data Mining Workshop Smarter Planet The world is changing, enabling organizations to make faster, better-informed decisions Digital technologies (sensors and other monitoring instruments) are being embedded into every object, system and process. All the data generated by digital technology is providing intelligence to help us do things better, improving our responsiveness and our ability to predict and optimize for future events. INTELLIGENT INSTRUMENTED INTERCONNECTED In the globalized, networked world, people, systems, objects and processes are connected, and they are communicating with one another in entirely new ways.

8. © 2014 IBM Corporation IBM Software 8 IBM SPSS Data Mining Workshop With this change comes an explosion in information … … Yet organizations are operating with blind spots Inefficient Access 1 in 2 don’t have access to the information across their organization needed to do their jobs Lack of Insight 1 in 3 managers frequently make critical decisions without the information they need Inability to Predict 3 in 4 business leaders say more predictive information would drive better decisions Variety of Information Volume of Digital Data Velocity of Decision Making Source: IBM Institute for Business Value

12. © 2014 IBM Corporation IBM Software 12 IBM SPSS Data Mining Workshop Why is predictive analytics important to your organization? “The median ROI for the projects that incorporated predictive technologies was 145%, compared with a median ROI of 89% for those projects that did not.” –Source: IDC, “Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study”

13. © 2014 IBM Corporation IBM Software 13 IBM SPSS Data Mining Workshop SPSS Customers: Business Objectives Attract the best customers Retain profitable customers Grow customer value Manage Risk Detect and prevent Non-Compliance “What is the likelihood a prospect will respond?” “What is the most likely next product for each customer?“ “Which customers are likely to leave?” “What activities are likely to be fraudulent?” “Which customers are likely to default on a loan?”

14. © 2014 IBM Corporation IBM Software 14 IBM SPSS Data Mining Workshop Enabling the Predictive Analytics Process Connect & Capture Analyse & Predict Deliver & Act Data Collection delivers an accurate view of customer attitudes and opinions Predictive capabilities bring repeatability to ongoing decision making, and drive confidence in your results and decisions Unique deployment technologies and methodologies maximize the impact of analytics in your operation

15. © 2014 IBM Corporation IBM Software 15 IBM SPSS Data Mining Workshop SPSS Predictive Analytics Software -- 4 Product Families Data Collection (surveys) Delivers accurate view of customer attitudes & opinions •IBM SPSS Data Collection Statistics Drives confidence in your results & decisions •IBM SPSS Statistics •IBM SPSS Text Analytics for Surveys (STAFS) Modeling (data mining) Brings repeatability to ongoing decision making •IBM SPSS Modeler •IBM SPSS Text Analytics (TA) Deployment (automation, scoring service, sharing, …) Maximizes the impact of analytics in your operation •IBM SPSS Decision Management •IBM SPSS Collaboration & Deployment Services

17. © 2014 IBM Corporation IBM Software 17 IBM SPSS Data Mining Workshop Predicting Customer Behavior Marketing activities are driven by predicted customer behavior Data Mining Data on Historic and Present Customer Behavior Predicted Customer Behavior Enterprise Data Sources Marketing Attitudinal Interaction Web Call-center Operational Attrition risk Potential value Cross sell B Cross sell A Credit risk Fraud risk

18. © 2014 IBM Corporation IBM Software 18 IBM SPSS Data Mining Workshop Definition of Data Mining Finding patterns in your data that you can use to do your business better Business-oriented discovery of patterns producing insight and a predictive capability which can be deployed widely Process of autonomously retrieving useful information or knowledge (“actionable assets”) from large data stores or set Predictive analysis helps connect data to effective action by drawing reliable conclusions about current conditions and future events.” Gareth Herschel, Research Director, Gartner Group

19. © 2014 IBM Corporation IBM Software 19 IBM SPSS Data Mining Workshop Statistical vs. Data Mining Approach Top-Down Approaches: Query, Search Bottom-Up Approaches: Data Mining, Text Mining A Statistical Approach can involve a user forming a theory about a possible relationship in a database and converting that to a hypothesis and testing that hypothesis using a statistical method. It is a manual, user-driven, top- down approach to data analysis. Source DM Review •The difference with data mining is that the interrogation of the data is done by the data mining method--rather than by the user. It is a data-driven, self- organizing, bottom-up approach to data analysis that works on large data sets. * "Statistical Modeling: The Two Cultures," Leo Breiman, Statistical Science, 2001, Vol.16 (3), pp.199-231.

20. © 2014 IBM Corporation IBM Software 20 IBM SPSS Data Mining Workshop Data Mining: a Different Approach Top-Down Query Search (OLAP, BI) Bottom-Up Data Mining Text/Web Mining Measurement (historical) Prediction (future) Business value Facts Segments & Trends Predictions Data mining Which customer types are at risk and why? Which cities were they located in? OLAP How many subscribers did we lose? Query & Reporting What should we offer this customer today? Integrated Analytical Solutions

21. © 2014 IBM Corporation IBM Software 21 IBM SPSS Data Mining Workshop IBM SPSS Modeler High performance data mining and text analytics workbench Used for the proactive •Identification of revenue opportunities •Reduction of costs •Increase in productivity •Forecasting Allows analytics to be repeated and integrated within business systems

24. © 2014 IBM Corporation IBM Software 24 IBM SPSS Data Mining Workshop Exercise: Predictive in 20 Minutes Goal: Identify who has cancelled their contract Approach: Use a data extract from a CRM Define which fields to use Choose the modeling technique Automatically generate a model to identify who has cancelled Review results Why? To prevent customers cancelling, by proactively identifying those likely to cancel before they do.

27. © 2014 IBM Corporation IBM Software 27 IBM SPSS Data Mining Workshop Data mining methodology CRoss-Industry Standard Process Model for Data Mining Describes Components of Complete Data Mining Project Cycle Shows Iterative Nature of Data Mining Vendor and Industry Neutral

28. © 2014 IBM Corporation IBM Software 28 IBM SPSS Data Mining Workshop Data Mining Considerations – CRISP-DM 28 Business Understanding What is the goal, what are we trying to achieve? Data Understanding/Preparation Available data (structured/unstructured) Relevant factors Subject matter expertise Modeling Supervised vs. Unsupervised Different types of models (NN vs. Rules) Combining models (Meta modeling) Deployment Batch vs. Real-time Production Automation Scheduling Champion – Challenger Multi-step jobs, conditional logic Governance Version control Security and auditing

29. © 2014 IBM Corporation IBM Software 29 IBM SPSS Data Mining Workshop Business Understanding Business Problem Telco Company has seen an increase in Customer Churn. Problems with the Current Process Based on Analysis it is not clear what the factors drive churn. The business is in reactive mode vs. proactive. Business Need The executives have asked the marketing department to identify the customers that are likely to churn and create an action plan to address the problem.

30. © 2014 IBM Corporation IBM Software 30 IBM SPSS Data Mining Workshop Data Understanding Do we have historical data that describes our customer behavior? –Yes, the data is available in the Enterprise Data Warehouse Do we have historical data of the customers that have churned? –Yes, we keep that historical data in the EDW as well. What data do we need? Where is it located? –Billing data, call data, payment data and demographics

31. © 2014 IBM Corporation IBM Software 31 IBM SPSS Data Mining Workshop Data Preparation Aggregate the data so that we have one row for each account Get the relevant attributes and calculate them if necessary Demographic data Call behavioral data Churn flag

32. © 2014 IBM Corporation IBM Software 32 IBM SPSS Data Mining Workshop Modeling In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, going back to the data preparation phase is often necessary.

34. © 2014 IBM Corporation IBM Software 34 IBM SPSS Data Mining Workshop Questions Customer Ask That Modeler Helps Answer Segment –I know my customers aren’t all the same, but how? Acquire –What customer should I be going after? –Where should I put my new store? Grow –I’ve got dozens of products to offer– how do I know the best mix to offer? –I’m blanketing my customer base with offers, but my returns seem to be diminishing. What am I doing wrong? Retain –I wish I knew which customers were most likely to leave me for a competitor. –I wish I knew which customers were the most profitable Fraud/Risk –I am spending a lot of time reviewing each claim, I wish there was a way of identifying which claims I should focus on.

35. © 2014 IBM Corporation IBM Software 35 IBM SPSS Data Mining Workshop “After a thorough investigation of the analytical solutions in the market, we selected IBM SPSS for its ease of use for the business users and the extensive insight it provides into customer behavior and profitability. The software generates results rapidly.” — Paul Groenland Project manager, database marketing Rabobank Business challenge Rabobank aims to strengthen its position as a market leader in financial services by further developing and expanding its relationship with its private and corporate customers. Solution Rabobank uses predictive analytics software from IBM SPSS to create and execute targeted direct marketing and lead generation campaigns. The quality of the leads is higher, so marketing campaigns are much more cost- efficient and effective Benefits Completion time for marketing campaigns has decreased, on average, by two to four weeks The quality of the leads is higher, so marketing campaigns are much more cost-efficient and effective Highly targeted support for local banks and advisors. By providing timely and targeted leads, they can quickly respond to changes and to individual customers’ wishes. Rabobank

36. © 2014 IBM Corporation IBM Software 36 IBM SPSS Data Mining Workshop Zorg en Zekerheid Uses business analytics to target fraudulent insurance claims The need: Processing millions of healthcare records requires surgical precision. For this Netherlands health insurer, this level of efficiency was missing from the process of analyzing claims and invoices to catch fraudulent activity. Manually selecting the data on the basis of predefined risk indicators had proven to be both time- consuming and unreliable in catching those abusing the system. The solution: Zorg en Zekerheid deployed a predictive analytics software solution capable of analyzing larger quantities of data, discovering patterns automatically,and catching anomalies in the process with a sharper level of accuracy and efficiency. The software provides a simple, graphical interface to deliver robust data mining, advanced analytics and interactive visualization for business users. What makes it smarter: Propels the fraud investigation process to action within days, instead of multiple weeks, using predictive analytics. Enables lost money to be recovered. Captures all relevant data, including hard-copy invoices, which the system scans and archives. Aggregates millions of digitally submitted records from multiple data sources and media formats into a central database, so data can be cross-functionally structured and automatically analyzed. “The analytics solution has doubled our financial results each year since 2007.” — Andor de Vries, Fraud Analyst, Zorg and Zekerheid Solution component: IBM® SPSS Modeler

37. © 2014 IBM Corporation IBM Software 37 IBM SPSS Data Mining Workshop Data Mining Methods Unsupervised Learning – Input and outputs are unknown, finds useful patterns Supervised Learning – Modeler specifies what to predict Clustering Associations / Sequences Regression •Exploratory data analysis •Reveals natural groups within a data set •Distance Measure: No prior knowledge about groups or characteristics •Not always an end in itself •Finds things that occur together •Associations can exist between any of the attributes •Discovers association rules in time-oriented data •Find the sequence or order of the events Customer Segmentation Market Basket Analysis, Next logical purchase Classification •Predicts an fixed outcome based on a set of inputs. •Modelers pre-defines input and outputs Fraudulent insurance claim prediction

38. © 2014 IBM Corporation IBM Software 38 IBM SPSS Data Mining Workshop 38 Unsupervised Learning - Cluster and Associate Clustering –An exploratory data analysis technique –Reveals natural groups within a data set –Distance Measure: No prior knowledge about groups or characteristics –Not always an end in itself Associations –Finds things that occur together – ex: events in a crime incident –Associations can exist between any of the attributes (no single outcome like Decision Trees) Sequential Associations –Discovers association rules in time-oriented data –Find the sequence or order of the events

39. © 2014 IBM Corporation IBM Software 39 IBM SPSS Data Mining Workshop 39 Supervised Learning - Classification Neural Networks –A technique for predicting outcomes based on inputs where the inputs are weighted on hidden layers –Behaves similar to the neurons in your brain –Powerful general function estimators –Require minimal statistical or mathematical knowledge Decision Trees and Rule Induction –Classification systems that predict or classify –Technique that shows the ‘reasoning’ – contrast with Neural Network –Builds sets of easy to understand ‘If – Then’ Rules –Eliminates factors that are unimportant Cat.%nBad52.01168Good47.99155Total(100.00)323Credit ranking (1=default) Cat.%nBad86.67143Good13.3322Total(51.08)165Paid Weekly/MonthlyP-value=0.0000, Chi-square=179.6665, df=1Weekly payCat.%nBad15.8225Good84.18133Total(48.92)158Monthly salaryCat.%nBad90.51143Good9.4915Total(48.92)158Age CategoricalP-value=0.0000, Chi-square=30.1113, df=1Young (< 25);Middle (25-35) Cat.%nBad0.000Good100.007Total(2.17)7Old ( > 35) Cat.%nBad48.9824Good51.0225Total(15.17)49Age CategoricalP-value=0.0000, Chi-square=58.7255, df=1Young (< 25) Cat.%nBad0.921Good99.08108Total(33.75)109Middle (25-35);Old ( > 35) Cat.%nBad0.000Good100.008Total(2.48)8Social ClassP-value=0.0016, Chi-square=12.0388, df=1Management;ClericalCat.%nBad58.5424Good41.4617Total(12.69)41Professional

40. © 2014 IBM Corporation IBM Software 40 IBM SPSS Data Mining Workshop Anomaly Detection Anomalies –Anomaly detection is an exploratory method –Designed for quick detection of unusual cases or records that should be candidates for further analysis –These should be regarded as suspected anomalies, which, on closer examination, may or may not turn out to be real 40

42. © 2014 IBM Corporation IBM Software 42 IBM SPSS Data Mining Workshop Richmond Police Department Curbing crime with predictive analytics The need: Facing a rising crime rate, the Richmond Police Department needed an efficient and cost-effective way to analyze crime data, assess public safety risks and make intelligent decisions about personnel deployment. The solution: The Department turned to IBM SPSS, to deploy a powerful predictive analytics tool that brings data from multiple sources into one data warehouse; discovers hidden relationships in the data; and automatically generates crime forecasts. What makes it smarter: Analyzes extremely large datasets and predicts crime patterns, giving the Department intelligence it needs to curb crime Enables the Department to be efficient about how, where and when to deploy patrol and tactical units Demonstrates ability to reduce violent-crime rates (homicide rates dropped 32 % from 2006-2007 and an additional 40 % from 2007-2008) “The big performance boost has been for my new guys on the streets. IBM SPSS essentially does the work that is gained only from experience.” — Stephen Hollifield Head of Technology Richmond Police Department Solution components: IBM SPSS Statistics IBM SPSS Modeler IBM Business Partner Information Builders IBM Business Partner RTI International

45. © 2014 IBM Corporation IBM Software 45 IBM SPSS Data Mining Workshop Association model Goal: Identify what products are being sold together Approach: Use a data extract from a transactional system Define which fields to use Visualize relationship between products Generate association model Review results Why? Identify next likely purchase Create bundles to increase $ value

49. © 2014 IBM Corporation IBM Software 49 IBM SPSS Data Mining Workshop The importance of text Because people communicate with words, not numbers, it has become critical to be able to mine text for its meaning and to sort, analyse, and understand it in the same way that data has been tamed. In fact, the two basic types of information complement each other, with data supplying the “what” and text supplying the “why”. Source IDC: “Text Analytics: Software’s Missing Piece?”

50. © 2014 IBM Corporation IBM Software 50 IBM SPSS Data Mining Workshop Text data and text analytics Around 80% of data held within a company is in the form of unstructured text documents or records: –Insurance claim notes –Emails –Call center logs, –Reports –Surveys –Web pages –Blogs – … Text Analytics connects unstructured text data to effective action by drawing reliable conclusions about current conditions and future events

53. © 2014 IBM Corporation IBM Software 53 IBM SPSS Data Mining Workshop Text Mining Free form notes entries Linguistic Text Mining: 1.Language analysis 2.Concept extraction 3.Process types, frequencies, & patterns Integrated structured and unstructured data ready for Predictive Text Analytics

55. © 2014 IBM Corporation IBM Software 55 IBM SPSS Data Mining Workshop RTL Nederland / InSites Consulting - Analyzing social media buzz to increase TV viewer involvement The need: RTL Nederland aimed to evaluate its television programs in the Dutch market and increase viewer satisfaction making use of online conversations. Therefore, RTL Nederland needed a way to analyze, interpret and successfully respond to audience feedback from social media sources. The solution: RTL Nederland worked with InSites Consulting to capture viewer opinions from user-generated comments on social media and other online buzz by using IBM predictive analytics software. This helps RTL Nederland to better understand audience needs and preferences, and hence increase viewer satisfaction and involvement. The obtained insight on viewer likes and dislikes allows RTL Nederland to optimize its product offering. What makes it smarter: Analyzed the sentiment of over 71,000 online conversations about ‘X FACTOR’, providing RTL Nederland with a powerful tool to measure attitudes indirectly and quickly adapt the program accordingly Captures unstructured data automatically from the web with sophisticated text analytics technology Approaching the final episodes of the reality competition shows, online buzz on the program even increased by about 400 percent, which provided a very rich source of information about viewer opinions “Collecting and analyzing feedback from social media is of great importance to RTL Nederland in order to offer programmes that are fully aligned with the target audience.” — Emilie van den Berge, senior Research & Intelligence project leader, RTL Nederland

56. © 2014 IBM Corporation IBM Software 56 IBM SPSS Data Mining Workshop Classification model Goal: Identify who is likely to cancel their contract Approach: Use a data extract from a CRM Use open ended comments from call center Extract concepts from the text Define which fields to use Choose the modeling technique Automatically generate a model to identify who has cancelled Review results Why? Identify customers at risk before they churn Unstructured data can provide insight into customers actions and improve model accuracy

59. © 2014 IBM Corporation IBM Software 59 IBM SPSS Data Mining Workshop Deployment Goal: Deploy a predictive model Approach: Use the stream generated in the earlier session Pass new data through the stream and ‘score’ the data Identify those likely to cancel Export an .xls file with 50 most likely to cancel Why? Extend the reach of analytics in an organization Allows analytics at the point of impact rather than being reactive

60. © 2014 IBM Corporation IBM Software 60 IBM SPSS Data Mining Workshop Based on the predictive model, a single offer is presented to the customer A call center agent submits customer information during an interaction The reaction to the offer is tracked and used to refine the model Deployment – integrating with existing systems

61. © 2014 IBM Corporation IBM Software 61 IBM SPSS Data Mining Workshop Customer Example Customer Growth from Inbound Contacts “I’m calling to get my information on my download limit” Next Best Action : Recommend Broadband Unlimited “Certainly, Mr. Watson. I’ll just get that for you right now… “ “Mr.Watson, you currently close to your 10GB monthly limit however, as a valued long-term customer, we’re able to make you an offer on unlimited broadband”

62. © 2014 IBM Corporation IBM Software 62 IBM SPSS Data Mining Workshop Deployment – integrating with Cognos BI 3) Results widely distributed via BI for consumption by business Users Cognos BI Common Business Model 1) Leveraging BI, identify problem or situation needing attention 2) SPSS predictive analytics feed results back into the BI layer

63. © 2014 IBM Corporation IBM Software 63 IBM SPSS Data Mining Workshop Modeler’s Unique Capabilities Easy to Learn / Intuitive Visual Interface –Visual approach - no programming –Comprehensive range of data mining functions –Flexible deployment options Powerful Automated modeling –Automated data preparation –Multi model creation & evaluation –Integrated analysis of text, web, & survey data Open and scalable architecure –Data mining within standard databases with SQL pushback support –Maximized use of infrastructure with multithreading, clustering and use of embedded algorithms (in database mining) –Integration with IBM technologies such as IBM Cognos Business Intelligence, Netezza and IBM InfoSphere Warehouse

64. © 2014 IBM Corporation IBM Software 64 IBM SPSS Data Mining Workshop Modeler Editions IBM SPSS Modeler Professional –Modeler Professional is a data mining workbench for the analysis of structured numerical data to model outcomes and make predictions that inform business decisions with predictive intelligence. IBM SPSS Modeler Premium –Modeler Premium allows organizations to tap into the predictive intelligence held in all forms of data. Modeler Premium goes beyond the analysis of structured numerical data alone and includes information from unstructured data such as web activity, blog content, customer feedback, e-mails, articles, and more to create the most accurate predictive models possible.

65. © 2014 IBM Corporation IBM Software 65 IBM SPSS Data Mining Workshop IBM SPSS Modeler Deployment Options Client (Desktop) –Access local files –Connect to operational databases –Connect to Cognos BI –Processing performed on local installation Client/Server –Data operations/processing on server –In-database data mining –SQL pushback –Modeler Batch –SuSE Linux Enterprise Server 10 (zLinux) –Inclusion in Smart Analytics System for Power (AIX) 65

66. © 2014 IBM Corporation IBM Software 66 IBM SPSS Data Mining Workshop Workshop Takeaways Easy to use, visual interface Short timeframe to be productive with actionable results Does not require knowledge of programming language Business results focused Cost effective solution that delivers powerful results across organization Flexible licensing and deployment options Full range of algorithms for your business problems End-to-end solution Data preparation through real time interactions Use structured, unstructured and survey data Full suite of products, from data collection through deployment

67. © 2014 IBM Corporation IBM Software 67 IBM SPSS Data Mining Workshop Workshop Takeaways Flexible architecture Leverages the investments already made in technology Does not require data in a proprietary format or DB Structured and unstructured data Open architecture (both inputs and outputs) SQL Pushback

68. © 2014 IBM Corporation IBM Software 68 IBM SPSS Data Mining Workshop Predictive analytics customer success  “94% achieved a positive return on investment with an average payback period of 10.7 months.”  “Returns were achieved through reduced costs, increased productivity, increased employee and customer satisfaction, and greater visibility.”  “Flexibility, performance, and price were all key factors in purchase decisions.” Nucleas Research, An independent provider of Global Research and Advisory Services. “30 “100% increase in Million Euro in new revenue” campaign effectiveness” “Reduced churn from 19 to 2%” “35% reduction in mailing cost, 2X response rate, 29% more profit”

69. © 2014 IBM Corporation IBM Software 69 IBM SPSS Data Mining Workshop We appreciate your feedback. Please fill out the survey form in order to improve this educational event. SIMPLIFIED CHINESE HINDI JAPANESE ARABIC RUSSIAN TRADITIONAL CHINESE TAMIL THAI FRENCH GERMAN ITALIAN SPANISH BRAZILIAN PORTUGUESE

70. © 2014 IBM Corporation IBM Software 70 IBM SPSS Data Mining Workshop IBM Business Solutions Center, La Gaude – october 2011 Thank You Laila Fettah Client Technical Professional Advanced Analytics IBM Johan Huizingalaan 765 1066 VH Amsterdam Tel: +31 (0)20 513 8950 Mobile: +31 (0)6 11 87 61 55 robin.van.tilburg@nl.ibm.com Robin van Tilburg Client Technical Professional Advanced Analytics IBM Johan Huizingalaan 765 1066 VH Amsterdam Tel: +31 (0)20 513 8371 Mobile: +31 (0)6 31 04 10 74 lailafettah@nl.ibm.com Contact

PoT - probeer de mogelijkheden van datamining zelf uit 30-10-2014

Esté a ler uma amostra.

Confira estes a seguir

PoT - probeer de mogelijkheden van datamining zelf uit 30-10-2014

Mais Conteúdo rRelacionado

Diapositivos para si (20)

Semelhante a PoT - probeer de mogelijkheden van datamining zelf uit 30-10-2014 (20)

Mais de Daniel Westzaan (14)

Mais recentes (20)