Chapter 2 1 Business Problems and Data Science

Chapter 2 1 Business Problems and Data Science Solution

2 Fundamental concepts An important Principle of data science is that data mining is a process with fairly well-understood stages. Some involve the application technology, such as the automated discovery and evaluation of patterns from data, while others mostly require an analyst’s creativity, business knowledge, and common sense.

3 Fundamental concepts Since the data mining process breaks up the overall task of finding patterns from data into a set of well-defined subtasks, it is also useful for structuring discussions about data science. This chapter introduces the data mining process, but first we provide additional context by discussing common types of data mining task.

4 From Business Problems to Data Mining Tasks Each data-driven business decision-making problem is unique, comprising its own combination of goals, desires, constraints, and even personalities. The solutions to the subtasks can then be composed to solve the overall problem. Some of subtasks are unique to the particular business problem, but others are common data mining tasks.

5 From Business Problems to Data Mining Tasks Example: telecommunications churn problem(電信客戶流 失) is unique to Mega. Tel. Co: Estimate from historical data the probability of a customer terminating her contract shortly after it has expired. Despite the large number of specific data mining algorithms developed over the years, there are only a handful of fundamentally different types of tasks these algorithms address. Individual(個體): entity. Ex: a customer or a business. Correlations(相關性): between a particular variable describing an individual the company after their contracts expired.

6 Classification(分類) and class probability estimation attempt to predict, for each individual in a population, which of a (small) set of classes this individual belongs to. Example question: “Among all customers of Mega. Tel. Co, which are likely to respond to a given offer? ” Two classes: will respond and will not respond. Scoring or class probability estimation Score representing the Probability( quantification of likelihood)

7 Regression(回歸)(“value estimation”) attempts to estimate or predict, for each individual, the numerical value of some variable for that individual. Example question: “ How much will a given customer use the service? ” Classification predicts whether something will happen. Regression predicts how much something will happen.

8 Similarity matching(相似度配對) attempts to identify similar individuals based on data known about them. Ex: IBM is interested in finding companies similar to their best business customer, in order to focus their sales force on the best opportunities. Recommendations

9 Clustering(群集) attempts to group individuals in a population together by their similarity, but not driven by any specific purpose. Example question: “ Do our customers form natural groups or segments? ” Decision-making processes.

10 Co-occurrence grouping(共生分群) attempts to find association between entities based on transactions involving them. Example question: “ What items are commonly purchased together? ” Ex: analyzing purchase records from a supermarket. Recommendation system

11 Profiling(剖析)(also as behavior description(型為描述)) attempts to characterize the typical behavior o an individual, group, or population. Example question: “ What is the typical cell phone usage of this customer segment? ” Profiling is often used establish behavioral norms for anomaly detection applications. Fraud detection and monitoring for intrusions to computer systems.

12 Link prediction(連結預測) attempts to predict connections between data items, usually by suggesting that a link should exist, and possibly also estimating the srength of the link. “Since you and Karen share 10 friends, maybe you’d like to be Karen’s friend? ”

13 Data reduction(資料縮減) attempts to take a large set of data and replace it with a smaller set of data that contains much of the important information in the larger set. Ex: a massive dataset on consumer movie-viewing preferences may be reduce to a much smaller dataset revealing the customer taste preferences.

14 Causal modeling attempts to help us understand what events or actions actually influence others. Ex: consider that we use predictive modeling to target advertisements to consumers.

15 Supervised vs. Unsupervised Methods Unsupervised: no specific purpose or target. “Do our customers nataturally fall into different group? ” Supervised: specific target defined. “ Can we find groups of customers who have particularly high likelihoods of canceling their service soon after their contracts expires? ”

16 Supervised vs. Unsupervised Methods Supervised data mining: there must be data on the target. Tow subclasses of supervised data mining: Classification “ Will this customer purchase service S 1 if given incentive I? ” “Which service package( S 1, S 2 , or none) will a customer likely purchase if given incentive I? ” Regression “How much will this customer use the service”

17 Supervised vs. Unsupervised Methods A vital part in the early stages of the data mining process To decide whether the line of attack will be supervised or unsupervised. If supervised, to produce a precise definition of a target variable. This variable must be specific quantity that will be the focus of the data mining.

18 Data Mining and Its Result Distinction pertaining to mining data: Mining the data to find patterns and build models. Using the results of data mining. Churn example.

19 Data Mining and Its Result

20 The Data Mining Process Cross Industry Standard Process for Data Mining process.

21 Business Understanding It is vital to understand the problem to solved. A part of the craft where the analysts’ creativity plays a large role. The design team should think carefully about the use scenario.

22 Data Understanding The data comprise the available raw material from which the solution will be built. Estimating the costs and benefits of each data source and deciding whether further investment is merited. Ex: Credit card fraud Medicare fraud

23 Data Preparation

24 Data preparation Often proceeds along with data understanding. Ex. 1. converting data to tabular format. 2. removing or inferring missing values. 3. converting data to different types.

25 Data preparation Leaks a variable collected in historical data gives information on the target variable-information that appears in historical data but is not actually available when the decision has to be made. Leakage must be considered carefully during data preparation.

26 Modeling Output of modeling is some sort of model or pattern capturing regularities in the data.

27 Evaluation Assess the data mining results rigorously and to gain confidence that they are valid and reliable before moving on. Includes both quantitative and qualitative assessments.

28 Deployment Put into real use in order to realize some return on investment. The clearest cases of deployment involve implementing a predictive model in some information system or business process. ex. Churn example

29 Deployment The data mining techniques themselves are deployment. Two reasons 1. the world may change faster than the data science team can adapt, as with fraud and intrusion detection. 2. a business has too many modeling tasks for their data science team to manually curated each model individually.

30 Deployment Can also be mush less “technical” It is not necessary to fail in deployment to start the cycle again. The Evaluation stage may reveal that results are not good enough to deploy.

31 Implications for Managing the Data Science Team It is tempting - but usually a mistake - to view the data mining process as a software development cycle. Software skills versus analytics skills

32 Other Analytics Techniques and Technologies Present six groups of related analytic techniques. Comparisons and contrasts with data mining. Data mining => automated search for knowledge, patterns, or regularities from data. Business analyst => to recognize what sort of analytic technique is appropriate for addressing a particular problem.

33 Statistics Two different uses in business analytics. 1. used as a catchall term for the computation of particular numeric values of interest from data. 2. denote the field of study that goes by that name.

34 Data Querying A specific request for a subset of data or for statistics about data, formulated in a technical language and posed to a database system. Differs fundamentally from data mining in that there is no discovery of patterns or models. Ex: select * from customers where age >45 ‘m’ and domicile = ‘ne’ and sex =

35 Data Querying On-line Analytical Processing (OLAP) easy-to-use GUI to query large data collections Data mining tools generally can incorporate new dimensions of analysis easily as part of the exploration.

36 Data Warehousing Collect and coalesce data from across an enterprise, often from multiple transaction-processing systems, each with its own database.

37 Regression Analysis This will involve estimating or predicting values for cases that are not in the analyze data set.

38 Machine Learning and Data Mining A field of study arose as a subfield of Artificial Intelligence, which was concerned with methods for improving the knowledge or performance of an intelligent agent over time. KDD focused on concerns raised by examining real-world applications.

39 Answer Business Questions with these Techniques 1. who are the most profitable customers? 2. Is there really a difference between the profitable customers and the average customer? 3. But who really are these customers? Can I characterize them? 4. Will some particular new customer be profitable? How much revenue should I expect this customer to generate?

40 summary Data mining is a craft. As with many crafts, there is a welldefined process that can help to increase the likelihood of a successful result. We will refer back to the data mining process repeatedly throughout the book, showing how each fundamental concept fits in.

41 THE END