Business Intelligence Exam 1 Questions and Answers

Data Science - Answer- involves principles, processes, and techniques for understanding phenomena via the (automated) analysis of data; data and the capability to extract useful knowledge from data, should be regarded as key strategic assets Data Driven Decision-Making - Answer- Refers to te practice of basing decisions on the analysis of data, rather than purely intuition Two Types of Decisions - Answer- 1) decisions for which "discoveries need to be made within data and 2) decisions that repeats, especially at massive scale and so decision-making can benefit from even small increases in decision-making accuracy based on data analysis Big Data - Answer- datasets that are too large for traditional data processing systems and therefore require new processing technologies (usually do not fit into RAM memory); volume, velocity and variety Data Processing vs Data Science - Answer- data processing is more managing the data that supports the data science process Ch.1 Fundamental Concepts - Answer- Extracting useful knowledge from data to solve business problems can be treated systematically by following a process with reasonably well-defined stages. From a large mass of data, information technology can be used to find informative descriptive attributes of entities of interest. If you look too hard at a set of data, you will find something - but it might not generalize beyond the data you're looking at. Formulating data mining solutions and evaluating the results involves thinking carefully about the context in which they will be used. Data Mining - Answer- (or "analytics") includes the techniques used in data science Data cleaning - Answer- cleaning the data or preprocessing, making sure the right inputs and outputs. A lot of time of data mining is data cleaning. classification - Answer- for each individual in a population identify a small set of classes to which that individual belongs. Regression - Answer- used to estimate or predict for each individual the numerical value of some variable similarity matching - Answer- used to identify similar individuals based on data known about them. similarity matching can be used to directly find similar entities. Clustering - Answer- used to group individuals in a population together by their similarity.... Co-occurrence - Answer- .... Profiling - Answer- .... Link Prediction - Answer- ..... Data Reduction - Answer- ..... Why should there be a standard process? - Answer- must be reliable and repeatable by people with little experience; gives a framework for recording experience, aid to project planning and management, "comfort factor" for new adopters CRISP-DM - Answer- cross industry standard process for data mining CRISP-DM Phases - Answer- 1. business understanding 2. data understanding 3. data preparation 4. modeling 5. evaluation 6. deployment Phase 1: Business Understanding - Answer- focuses on understanding project objectives and requirements understanding and converting this knowledge into a data mining definition; devising a preliminary plan includes: statement of business objective, statement of data mining objective and statement of success criteria Phase 2: Data Understaing - Answer- initial data collection and familiarization to identify data quality problems and gain insight on data and/or interesting subsets (outliers; a managerial problem or an opportunity); collect initial data and describe the data, explore the data and verify the quality. Phase 3: Data Preparation - Answer- Select the data to use in the project and clean it up. (takes over 90% of the time); collection, assessment, consolidation and cleaning, data selection and transformations; constructed, integrated and formatted Phase 4: Modeling - Answer- select a modeling technique and generate a test design to test model's quality and validity, build models and then assess the models to find the best one Phase 5: Evaluation - Answer- evaluation of model (how well it performed on test data), methods and criteria, interpretation of the model and determination of the next steps; a decision on the use of the data mining results should be reached