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❶We live in a world where substantial amounts of data are available at the touch of a button. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.

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In this course, students will examine the issues in managing data and information from an enterprise perspective, and explore data management as an essential resource to organizational success through a deeper understanding of the concepts and techniques for managing the design, development, and maintenance of all the components of enterprise information management.

The course will examine the roles and responsibilities of the various professionals that manage data and information in an organization. The role of many analysts is as much about interpreting the results of data analysis as it is about gathering the data and "crunching the numbers. Concepts from enterprise data management, including data warehousing and business intelligence, will provide a foundation for examining the topics of data mining, advanced and dimensional data modeling, and decision support system development as techniques for an organization's competitive advantage.

In addition to the gathering and interpretation of data, today's business environment calls upon the analyst to communicate the results of data analysis to a variety of audiences. In this course students will learn how to synthesize the technical components of data analysis into reports, presentations, and visual dashboards that are meaningful for the intended audience and deliver those components in a coherent, convincing format.

In the competitive business world, using data to its best advantage becomes all the more crucial. In this course, students will learn how to discern the levels of relevancy of data and the impact it has on operations as well as hone their ability to identify macro and micro level risk and evaluate risk management programs, policies, and strategies.

Building on prior coursework in decision methods and modeling, students will get a deeper understanding of the art and science of predictive analysis. Students will examine the elements that contribute to building reliable predictive models that result in actionable performance predictions such as identifying the variables that have the most predictive power and developing and deploying predictive models currently in use.

This course will emphasize the employment of advanced analytic strategies over the entire life cycle of the data analysis process. Using a comprehensive case-studies approach, students will logically extend and add definition to their existing analytic skill set, resulting in the development of a project proposal that will serve as preparation for the capstone experience.

This course includes the study of concepts, tools, and practices of project management. The course adopts a managerial process approach to Project Management, which consists of initiating, planning, executing, controlling and closing the project. Major topics will include project scope, project time, project cost, project quality, project risk, project resources, project communications and how to be an effective project manager. Cases are utilized to integrate the learning in the course and provide decision- making experience for the student.

This capstone course is the culminating experience for the M. The aim of the capstone is to assess students' ability to synthesize and integrate the knowledge and skills they have developed throughout their coursework, rather than introducing new concepts.

This course is structured to support student success in fulfilling program requirements. Tuition rates for SNHU's online degree programs are among the lowest in the nation. Southern New Hampshire University is a private, nonprofit institution accredited by the New England Association of Schools and Colleges as well as several other accrediting bodies. A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment.

It may be based on a model or algorithm. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy. Once the data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative.

When determining how to communicate the results, the analyst may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays such as tables and charts to help communicate key messages contained in the data. Tables are helpful to a user who might lookup specific numbers, while charts e. Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message.

Customers specifying requirements and analysts performing the data analysis may consider these messages during the course of the process. Author Jonathan Koomey has recommended a series of best practices for understanding quantitative data. For the variables under examination, analysts typically obtain descriptive statistics for them, such as the mean average , median , and standard deviation.

They may also analyze the distribution of the key variables to see how the individual values cluster around the mean.

The consultants at McKinsey and Company named a technique for breaking a quantitative problem down into its component parts called the MECE principle. Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them.

For example, profit by definition can be broken down into total revenue and total cost. In turn, total revenue can be analyzed by its components, such as revenue of divisions A, B, and C which are mutually exclusive of each other and should add to the total revenue collectively exhaustive.

Analysts may use robust statistical measurements to solve certain analytical problems. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false. For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept called the Phillips Curve.

Hypothesis testing involves considering the likelihood of Type I and type II errors , which relate to whether the data supports accepting or rejecting the hypothesis.

Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y e. This is an attempt to model or fit an equation line or curve to the data, such that Y is a function of X. Necessary condition analysis NCA may be used when the analyst is trying to determine the extent to which independent variable X allows variable Y e.

Whereas multiple regression analysis uses additive logic where each X-variable can produce the outcome and the X's can compensate for each other they are sufficient but not necessary , necessary condition analysis NCA uses necessity logic, where one or more X-variables allow the outcome to exist, but may not produce it they are necessary but not sufficient.

Each single necessary condition must be present and compensation is not possible. Users may have particular data points of interest within a data set, as opposed to general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience.

Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis. Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion , or test hypotheses. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them.

This makes it a fact. Whether persons agree or disagree with the CBO is their own opinion. As another example, the auditor of a public company must arrive at a formal opinion on whether financial statements of publicly traded corporations are "fairly stated, in all material respects. When making the leap from facts to opinions, there is always the possibility that the opinion is erroneous.

There are a variety of cognitive biases that can adversely affect analysis. For example, confirmation bias is the tendency to search for or interpret information in a way that confirms one's preconceptions.

In addition, individuals may discredit information that does not support their views. Analysts may be trained specifically to be aware of these biases and how to overcome them. In his book Psychology of Intelligence Analysis , retired CIA analyst Richards Heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions.

He emphasized procedures to help surface and debate alternative points of view. Effective analysts are generally adept with a variety of numerical techniques. However, audiences may not have such literacy with numbers or numeracy ; they are said to be innumerate.

Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques. For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy GDP or the amount of cost relative to revenue in corporate financial statements.

This numerical technique is referred to as normalization [7] or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation i.

Analysts apply a variety of techniques to address the various quantitative messages described in the section above. Analysts may also analyze data under different assumptions or scenarios. For example, when analysts perform financial statement analysis , they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock.

Similarly, the CBO analyzes the effects of various policy options on the government's revenue, outlays and deficits, creating alternative future scenarios for key measures. Courses in Progress AD Become a Data Scientist datacamp.

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