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computer science
analytics and data science
Business Intelligence Analytics And Data Science A Managerial Perspective 4th Global Edition Ramesh Sharda, Dursun Delen, Efraim Turban - Solutions
What is metadata? Explain the importance of metadata.
What is a cube? What do drill down, roll-up, slice, and dice mean?
What are the distinguishing features of KPIs?
List and briefly define the four most commonly cited operational areas for KPIs.
What are the four perspectives that BSC suggests to view organizational performance?
Draw a comparison between data integration, functional integration, and application integration. List the integration technologies that enable data and metadata integration.
What is the architectural difference between a data warehouse, data mart, operational data store, and an enterprise data warehouse?
What are the various types of metadata, and what roles do they play in the various integration exercises?
What is the relationship between business processes and the various architecture for data warehousing?
What are the major design principles of security architecture that need to be adhered to in the creation and operation of a data warehouse?
What is the relationship between master data management and a data warehouse within an enterprise? What further advancements can be made to improve data quality?
Discuss the relationships between data warehouse architectures and development methods.
Function point analysis (FPA) is a set of disparate methods for supporting the cost-estimations of projects creating information systems. Note them down from the literature and determine which would fit a data warehousing project and how the estimated costs and elapsed time can be calculated.
ISACA COBIT (https://www.isaca.org/COBIT/Pages/default.aspx) contains many control objectives and security goals. Which ones are useful for data warehousing projects? Describe the results of your assessment in a study.
The governance of information systems and data warehouses is a critical domain of enterprise governance.Study the literature available (visit https://cobitonline.isaca.org/) and determine which objectives and methods can be implemented in reasonable costs and time. The results of the team’s work
Virtually every BPM/CPM vendor provides case studies on their Web sites. As a team, select two of these vendors(you can get their names from the Gartner or AMR lists). Select two case studies from each of these sites.For each, summarize the problem the customer was trying to address, the
Why do law enforcement agencies and departments like Miami-Dade Police Department embrace advanced analytics and data mining?
What are the top challenges for law enforcement agencies and departments like Miami-Dade Police Department? Can you think of other challenges (not mentioned in this case) that can benefit from data mining?
What are the sources of data that law enforcement agencies and departments like Miami-Dade Police Department use for their predictive modeling and data mining projects?
What type of analytics do law enforcement agencies and departments like Miami-Dade Police Department use to fight crime?
What does “the big picture starts small” mean in this case? Explain.
What is an ensemble model in data mining? What are the pros and cons of ensemble models?
Why do you think the most popular tools are developed by statistics-based companies?
What are the most popular free data mining tools? Why are they gaining overwhelming popularity (especially R)?
Why has data mining proliferated over the past few decades in contrast to the previous epoch?
Define a taxonomy for data mining that reflects the evolution of the field.
How can alignment between IT and enterprise strategy be applied to tools for data mining?
List the steps of data mining processes and the corresponding major methods.
What are the common accuracy metrics for data-mining algorithms?
Search the available literature for additional metrics that measure algorithms for accuracy, suitability for a particular purpose, etc.
What are the basic principles of model testing? What are the popular assessment methods for classification?
Create a project plan for the introduction and rollout of a data analytics program. The plan should include the selected data mining process, life cycle, cost–benefit analysis, impact analysis, alternatives for enterprise and technology architecture, alternative organizational approaches, and
Download data on migration statistics (https://archive.ics.uci.edu/ml/datasets/Census-Income+(KDD)). Use the regression, artificial neuron network, and timeseries methods to build up a model for predicting the next one or two years’ tendencies in the manner of predictions and prescriptions.
Visit http://biogps.org/ and search for the tag “colorectal cancer.” Look at the data set you get and plan and design a data analytics program based on the selected data mining process (either CRISP-DM or SEMMA). Choose and formulate the objective of the data mining process as evaluation of
How can wearables improve player health and safety? What kinds of new analytics can trainers use?
List some capabilities of information systems that can facilitate managerial decision making.
What was the primary difference between the systems called MIS, DSS, and Executive Support Systems?
Define OLTP.
Define OLAP.
What is a DW? How can data warehousing technology help to enable analytics?
Why would a health insurance company invest in analytics beyond fraud detection?Why is it in their best interest to predict the likelihood of falls by patients?
How would you convince a new health insurance customer to adopt healthier lifestyles(Humana Example 3)?
What are the sources of Big Data?
List the 11 categories of players in the analytics ecosystem.
Give examples of companies in each of the 11 types of players.
Which companies are dominant in more than one category?
Is it better to be the strongest player in one category or be active in multiple categories?
Survey the scientific, technology, and professional literature from the past six months to find application examples from the field of enterprise information systems and data warehouses where data mining and data analytics have been used for economic and financial purposes.
What is the difference between “traditional data mining”(in the context of data warehouses) and Big Data analytics?
Categorize the underlying data structure and the data processing of technologies as OLTP, OLAP, or Big Data analytics.
In the case of a project aimed at introduction of tools for BI, Data Analytics, and data science into an enterprise, which elements of the analytics ecosystems can and should be combined?
What does SiriusXM do? In what type of market does it conduct its business?
What were the challenges? Comment on both technology and data-related challenges.
What were the proposed solutions?
How did they implement the proposed solutions? Did they face any implementation challenges?
Where does the data for business analytics come from?
What is data? How does data differ from information and knowledge?
What are the main data preprocessing steps?
What is a report? What are reports used for?
What are the main differences among line, bar, and pie charts? When should you use one over the others?
What are the main reasons for the recent emergence of visual analytics?
What is an information dashboard? Why are they so popular?
Can you think of other companies facing similar challenges that can potentially benefit from similar data-driven marketing solutions?
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