44.During modeling of the CRISP-DM method, we would ______.(A)clarify business goals for the data mining project(B)select a subset of data to be used and prepare it(C)assess if the model achieves business goals(D)apply selected modeling techniquesAnswer :(D) Show
45.____ is the last phase of the six-phase CRISP-DM method. Get answer to your question and much more (D)DeploymentAnswer :(D)46.Which of the following isNOTconsidered a BI and analytics tool? Get answer to your question and much more 47.Suppose you have access to quantitative data on populations by ZIP code and crime rates. You wish todetermine if there is a relationship between the two variables and display the results in a graph. Which BI tool willbe most useful? Get answer to your question and much more 48.To analzye various alternative scenarios, a manager would use _______.(A) the spreadsheet's 'what-if' capability(B)the graphing features of spreadsheet software(C)the reporting feature of a BI tool(D) a data visualization toolAnswer :(A) 49.Which of the following isNOTa key capability of BI tools? Get answer to your question and much more Get answer to your question and much more 50.The graphical representation that summarizes the steps a consumer takes in making the decision to buy yourproduct and become a customer is called _____. Get answer to your question and much more 51.What determines the size of words in a word cloud? Get answer to your question and much more 52.Which of the following is IBM's BI product?(A)Power BI(B)Business objects(C)Cognos(D)HyperionAnswer :(C) 53.From which vendor is the BI product Business Objects available? Get answer to your question and much more 54._________ encourages nontechnical end users to make decisions based on factsand analyses rather than intuition. Get answer to your question and much more
Upload your study docs or become a Course Hero member to access this document Upload your study docs or become a Course Hero member to access this document CRISP-DM Diagram. Inspired by WikipediaThe CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the base for a data science process. It has six sequential phases:
Published in 1999 to standardize data mining processes across industries, it has since become the most common methodology for data mining, analytics, and data science projects. Data science teams that combine a loose implementation of CRISP-DM with overarching team-based agile project management approaches will likely see the best results. CRISP-DM Training & CertificationMaster the skills and gain the confidence to deliver data science projects and to lead data teams. Grow by earning the Data Science Team Lead certification. Available in individual courses and in private group courses. What are the 6 CRISP-DM Phases?I. Business Understanding
Any good project starts with a deep understanding of the customer’s needs. Data mining projects are no exception and CRISP-DM recognizes this. The Business Understanding phase focuses on understanding the objectives and requirements of the project. Aside from the third task, the three other tasks in this phase are foundational project management activities that are universal to most projects:
While many teams hurry through this phase, establishing a strong business understanding is like building the foundation of a house – absolutely essential. II. Data UnderstandingNext is the Data Understanding phase. Adding to the foundation of Business Understanding, it drives the focus to identify, collect, and analyze the data sets that can help you accomplish the project goals. This phase also has four tasks:
III. Data PreparationA common rule of thumb is that 80% of the project is data preparation. This phase, which is often referred to as “data munging”, prepares the final data set(s) for modeling. It has five tasks:
IV. ModelingWhat is widely regarded as data science’s most exciting work is also often the shortest phase of the project. Here you’ll likely build and assess various models based on several different modeling techniques. This phase has four tasks:
Although the CRISP-DM Guide suggests to “iterate model building and assessment until you strongly believe that you have found the best model(s)”, in practice teams should continue iterating until they find a “good enough” model, proceed through the CRISP-DM lifecycle, then further improve the model in future iterations. V. EvaluationWhereas the Assess Model task of the Modeling phase focuses on technical model assessment, the Evaluation phase looks more broadly at which model best meets the business and what to do next. This phase has three tasks:
VI. Deployment“Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process across the enterprise.” –CRISP-DM Guide A model is not particularly useful unless the customer can access its results. The complexity of this phase varies widely. This final phase has four tasks:
Your organization’s work might not end there. As a project framework, CRISP-DM does not outline what to do after the project (also known as “operations”). But if the model is going to production, be sure you maintain the model in production. Constant monitoring and occasional model tuning is often required. Is CRISP-DM Agile or Waterfall?Some argue that it is flexible and agile and while others see CRISP-DM as rigid. What really matters is how you implement it. Waterfall: On one hand, many view CRISP-DM as a rigid waterfall process – in part because of its reporting requirements are excessive for most projects. Moreover, the guide states in the business understanding phase that “the project plan contains detailed plans for each phase” – a hallmark aspect of traditional waterfall approaches that require detailed, upfront planning. Indeed, if you follow CRISP-DM precisely (defining detailed plans for each phase at the project start and include every report) and choose not to iterate frequently, then you’re operating more of a waterfall process. Agile: On the other hand, CRISP-DM indirectly advocates agile principles and practices by stating: “The sequence of the phases is not rigid. Moving back and forth between different phases is always required. The outcome of each phase determines which phase, or particular task of a phase, has to be performed next.” Thus if you follow CRISP-DM in a more flexible way, iterate quickly, and layer in other agile processes, you’ll wind up with an agile approach. Example: To illustrate how CRISP-DM could be implemented in either an Agile or waterfall manner, imagine a churn project with three deliverables: a voluntary churn model, a non-pay disconnect churn model, and a propensity to accept a retention-focused offer. CRISP-DM Waterfall: Horizontal SlicingIn a waterfall-style implementation, the team’s work would comprehensively and horizontally span across each deliverable as shown below. The team might infrequently loop back to a lower horizontal layer only if critically needed. One “big bang” deliverable is delivered at the end of the project. CRISP-DM Agile: Vertical SlicingAlternatively, in an agile implementation of CRISP-DM, the team would narrowly focus on quickly delivering one vertical slice up the value chain at a time as shown below. They would deliver multiple smaller vertical releases and frequently solicit feedback along the way. Which is better?When possible, take an agile approach and slice vertically so that:
How popular is CRISP-DM?Definitive research does not exist on how frequently data science teams use different management approaches. So to get an idea on approach popularity, we investigated KDnuggets polls, conducted our own poll, and researched Google search volumes. Each of these views suggests that CRISP-DM is the most commonly used approach for data science projects. KDnuggets PollsBear in mind that the website caters toward data mining, and the data science field has changed a lot since 2014. KDnuggets is a common source for data mining methodology usage. Each of the polls in 2002, 2004, 2007 posed the question: “What main methodology are you using for data mining?”, and the 2014 poll expanded the question to include “…for analytics, data mining, or data science projects.” 150-200 respondents answered each poll. CRISP-DM was the popular methodology in each poll spanning the 12 years. Our 2020 PollTo learn more about the poll, go to this post. For a more current look into the popularity of various approaches, we conducted our own poll on this site in August and September 2020. Note the response options for our poll were different from the KDnuggets polls, and our site attracts a different audience. CRISP-DM was the clear winner, garnering nearly half of the 109 votes., Google SearchesGiven the ambiguity of a searcher’s intent, some searches like “my own” could not be analyzed and others like “tdsp” and “semma” could be misleading. For yet third view into CRISP-DM, we turned to Google Keyword Planner tool which provided the average monthly search volumes in the USA for select key search terms and related terms (e.g. “crispdm” or “crisp dm data science”). Clearly irrelevant searches like “tdsp electrical charges” or “semma both aagatha” were then removed. CRISP-DM yet again reigned as king, and this time with a much broader margin. Should I use CRISP-DM for Data Science?So CRISP is popular. But should you use it? Like most answers in data science, it’s kind of complicated. But here’s a quick overview. BenefitsFrom today’s data science perspective this seems like common sense. This is exactly the point. The common process is so logical that it has become embedded into all our education, training, and practice. -William Vorheis, one of CRISP-DM’s authors (from Data Science Central)
Weaknesses & ChallengesIn a controlled experiment, students who used CRISP-DM “were the last to start coding” and “did not fully understand the coding challenges they were going to face” –Saltz, Shamshurin, & Crowston, 2017
RecommendationsCRISP-DM is a great starting point for those who are looking to understand the general data science process. Five tips to overcome these weaknesses are:
Dive Deeper: Explore key actions to considerfor Data Science projects using CRISP-DM What are other CRISP-DM Alternatives?SEMMA A few years prior to the publication of CRISP-DM, SAS independently developed Sample, Explore, Modify, Model, and Assess (SEMMA). Although designed to help guide users through tools in SAS Enterprise Miner for data mining problems, SEMMA is often considered to be a general data mining methodology (Tiwari & Dixit, 2017). SEMMA (8.5%) was the third most popular methodology per the 2014 KDnuggets poll, but its use is down from 13% in 2007. Compared to CRISP-DM, SEMMA is even more narrowly focused on the technical steps of data mining. It skips over the initial Business Understanding phase from CRISP-DM and instead starts with data sampling processes. SEMMA likewise does not cover the final Deployment aspects. Otherwise, its phases somewhat mirror the middle four phases of CRISP-DM. Although potentially useful as a process to follow data mining steps, SEMMA should not be viewed as a comprehensive project management approach. KDD and KDDSKnowledge Dicsovery in Database (KDD) is the general process of discovering knowledge in data through data mining, or the extraction of patterns and information from large datasets using machine learning, statistics, and database systems. In 2016, Nancy Grady of SAIC, expanded upon CRISP-DM to publish the Knowledge Discovery in Data Science (KDDS). “As an end-to-end process model from mission needs planning to the delivery of value”, KDDS specifically expands upon CRISP-DM to address big data problems. It also provides some additional integration with management processes. KDDS defines four distinct phases: assess, architect, build, and improve and five process stages: plan, collect, curate, analyze, and act (Grady, 2016). KDDS can be a useful expansion of CRISP-DM for big data teams. However, KDDS only addresses some of the shortcomings of CRISP-DM. For example, it is not clear how a team should iterate when using KDDS. In addition, its combination of phases and processes is less straight-forward. Adoption of KDDS outside of SAIC is not known. Where can I learn more?
Data Science Team Lead CertificationThere’s more than just CRISP-DMCRISP-DM is a great starting point to understanding data science projects. But there’s a lot more. To learn these frameworks, how to apply them, and how to deliver data science outcomes, enroll in the Data Science Team Lead course. Which of the following is are steps in the CRISPThose steps are Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment.
What are the phases of CRISPCRISP-DM allows you to create a data mining model that fits your particular needs. In such a situation, the modeling, evaluation, and deployment phases might be less relevant than the data understanding and preparation phases.
Which of the following occurs first in a CRISPSTAGE ONE – DETERMINE BUSINESS OBJECTIVES. The first stage of the CRISP-DM process is to understand what you want to accomplish from a business perspective. Your organisation may have competing objectives and constraints that must be properly balanced.
How many phases does the CRoss Industry Process for data mining CRISPThe CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the base for a data science process. It has six sequential phases: Business understanding – What does the business need?
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