Complete the Police Killings Exercise located at the following.docx

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Complete the "Police Killings" Exercise located at the following link: Working With Data DEVELOPING INTIMACY WITH YOUR DATA SUBJECT: Police Killings This exercise involves you working with an already acquired dataset to undertake the remaining three key steps of examining, transforming and exploring your data to develop a deep familiarisation with its properties and qualities. This spreadsheet provides you with two contrasting worksheets showing snapshot details of recorded deaths caused by US law enforcement agencies, from The Guardian (“ The Counted “) and the Washington Post (“ Fatal Force “). For each dataset: Examination : Articulate the meaning of the data (its representativeness and phenomenon) and thoroughly examine the physical properties (type, size, condition) noting down your descriptions in each case. Compare what the two datasets offer and contrast their differences. Transformation : What could you do/would you need to do to clean or modify the existing data? What other data could you imagine would be valuable to consolidate the existing data? Exploration : Use a tool of your choice (common recommendations would be Excel, Tableau, R) to visually explore the two datasets separately in order to deepen your appreciation of their physical properties and their discoverable qualities (insights) to help you cement your understanding of their respective value. .

Education

Exploration
: Use a tool of your choice (common recommendations would be
Excel, Tableau, R) to visually explore the two datasets
separately in order to deepen your appreciation of their physical
properties and their discoverable qualities (insights) to help you
cement your understanding of their respective value.

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Graphs represent data in an engaging manner and make comparisons and analyses easier. For example, a graph depicting the number of crimes committed each year over a decade is easier to comprehend visually than reading the numerical values for each year. Before creating a graph, however, it is important to choose one that appropriately represents the data. A histogram, rather than a pie chart, is appropriate for depicting the age groups (e.g., 15–24, 25–34) of murder victims in a city. Histograms are designed to be used with variables that are categorized, but pie charts plot each value. Therefore, it would be easier to read a histogram showing bars for age groups of murder victims than a pie chart in which every single age would have to be plotted. In the past, creating graphs was cumbersome and time consuming, but present-day software programs such as Microsoft Word and Excel provide tutorials that walk you through the process. With knowledge of these software programs, you can create customized charts and figures to represent your research data in visually interesting ways. In this Assignment, you create at least two different graphs in Excel or Word that can be used to illustrate hypothetical data related to six incidents of crime. · Create at least two different graphs in Excel or Word using the data provided in the table below: Type of Crime Offender’s Age (Years) Offender’s Gender Time of the Incident Theft 22 Male Early morning Possession of drugs 21 Female Late evening Theft 19 Male Late evening Theft 33 Female Afternoon Possession of drugs 47 Female Morning Possession of drugs 17 Male Early morning · Briefly describe the data represented in the graphs and/or charts you created. · Explain why the graphs and/or charts you created best represent the data compared to other options. Be specific. Submit the graphs you created in a document that is separate from your written Assignment. Bachman, R. D., & Schutt, R. K. (2019). The practice of research in criminology and criminal justice (7th ed.). Thousand Oaks, CA: SAGE Publications. · Chapter 4, “Conceptualization and Measurement” (pp. 86–116) The Practice of Research in Criminology and Criminal Justice, 7th Edition by Bachman, R. D. & Schutt, R. K. Copyright 2019 by SAGE Publications, Inc. Reprinted by permission of SAGE Publications, Inc via the Copyright Clearance Center. Bachman, R. D., & Schutt, R. K. (2019). The practice of research in criminology and criminal justice (7th ed.). Thousand Oaks, CA: SAGE Publications. · Chapter 14, “Analyzing Quantitative Data” (pp. 404–415 and 426–444) The Practice of Research in Criminology and Criminal Justice, 7th Edition by Bachman, R. D. & Schutt, R. K. Copyright 2019 by SAGE Publications, Inc. Reprinted by permission of SAGE Publications, Inc via the Copyright Clearance Center. Trochim, W. M. K. (2006). Levels of measurement. In Research methods knowledge base. Retrieved from http://www.socialresearchmethods.net/kb/measlevl.php Walden Univer ...

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2 days ago Rajani Sade Data examination: Identifying physical properties and meaning COLLAPSE Top of Form Data examination: Identifying physical properties and meaning Next step after Data acquisition is data examination. Here we examine the physical properties of the data, what it contains and what we can get from it. Physical properties we need to observe from the data are type, size, and condition. This is more mechanical work. We have to observe the surface characteristics to understand its properties. Type gives us the nature of the data whether it is qualitative or quantitative. If a variable is qualitative, we have to find out if it is text or nominal or ordinal. If a variable is quantitative then we have to find out if it is interval or ratio, whether it is continuous or discrete. Based on the type we can come up with statistical analysis methods. Size of the variable is the number of bytes it occupies in the database. For each column we have to find out the format of the data and maximum length it requires. Condition tells us about the quality of the data. Some of the checks we do to check the quality of the data is missing values, erroneous values, inconsistency, duplicate records, incorrect dates, Special characters, leading or training blanks etc. From the condition we can estimate what kind of cleanup we can do to make this data useful for the analysis. Reference: Kirk, A. (2016). Data Visualisation: A Handbook for Data Driven Design. Thousand Oaks, CA: Sage Publications, Ltd. ISBN: 978-1-4739-1214-4 Bottom of Form Niteshkumar Laxmidas Patel Data Examination Data examination is a process, in which, you would examine and understand the data that one has gathered. It is really important to know that when working with information, you should study the data before making any further decisions. Data investigation is to confirm our outcomes whether it is substantial, reproducible and verifiable. Data examination is also a procedure of investigating, purging, changing, and demonstrating information with the objective of finding valuable data, illuminating conclusions, and supporting basic leadership (Kirk; 2016) .Data examination has various aspects and methodologies, incorporating assorted systems under an assortment of names, while being utilized as a part of various business, science, and sociology spaces. There are cases, for example at leading information organizations, for example, Amazon and Google, where information is utilized as a part in which examined data, comes about themselves to settle on the choices, for instance with respect to proposal engines , PageRank, and request anticipating frameworks.So, it is important to examine data before making decisions regarding business (Rosenthal, Robert; Rosnow, Ralph L; 1991). References:- Kirk, A. (2016). Data Visualisation: A Handbook for Data Driven Design (p. 50). SAGE Publications. Rosenthal, R., & Rosnow, R. L. (1991). Essentials of behavioral research: ...

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