Data analysis and presentation

This resource is in development and will be added to over time, so please let us know if you do not find the information you need or if you would like to give feedback. About this resource What it does Public Health England PHE provides many high quality data and analysis tools and resources for public health professionals. The PHE data and knowledge gateway provides direct access to these resources.

Data analysis and presentation

Retrieve Value Given a set of specific cases, find attributes of those cases. What is the value of aggregation function F over a given set S of data cases? What is the sorted order of a set S of data cases according to their value of attribute A?

What is the range of values of attribute A in a set S of data cases? What is the distribution of values of attribute A in a set S of data cases?

What is the correlation between attributes X and Y over a given set S of data cases? Which data cases in a set S of data cases are relevant to the current users' context? Barriers to effective analysis[ edit ] Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience.

The importance of data visualization

Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis. Confusing fact and opinion[ edit ] You are entitled to your own opinion, but you are not entitled to your own facts. Daniel Patrick Moynihan Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinionor test hypotheses.

Facts by definition are irrefutable, Data analysis and presentation 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.

Data analysis and presentation

Cognitive biases[ edit ] 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. 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.

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 analysisthey 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.

Smart buildings[ edit ] A data analytics approach can be used in order to predict energy consumption in buildings. Analytics and business intelligence[ edit ] Main article: Analytics Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.Data presentation and analysis is crucial for any project which involves collection of data which needs to be processed and presented.

Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others.

Buy Data Analysis and Presentation Skills: An Introduction for the Life and Medical Sciences on benjaminpohle.com FREE SHIPPING on qualified orders1/5(1). Data Analysis illustrates the powerful features Excel has to offer to prepare and analyze data.

Learn how to combine, split, or delete data in Excel to better control the data cleansing of your raw data. Big data's big promises are left in a big dust pile because we don't optimize the last mile: Data presentation.

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Learn from eight real-world examples how-to. In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from benjaminpohle.comtion of information from datasets that are high-dimensional, incomplete and noisy is generally challenging.

TDA provides a general framework to analyze such data in a manner that is insensitive to the particular metric chosen and provides dimensionality.

Data Analysis in Excel - Free Merge, Split, Text and Data Cleansing Tools