DAT 375 Project One

.docx

School

Southern New Hampshire University *

*We aren’t endorsed by this school

Course

375

Subject

Industrial Engineering

Date

Dec 6, 2023

Type

docx

Pages

Uploaded by HighnessCrownMouse16

Nathan Cumbo DAT 375 Project One Data Analysis Process Job Aid This job aid is intended to be used by newly hired data analysts at the Miami Police Department in coordination with the SCDR as a direct reference. In 1984, a man named John Naisbitt once said “We are drowning in information, but starved for knowledge.” Nearly 4 decades later, this is still true, if not even moreso. A large reason for this is not the lack of data, but rather the lack of skilled data analysts in the field. According to the McKinsey report, there will be a shortage of talent necessary for organizations to take advantage of big data (Larose & Larose, 2015). While the use of data mining and analysis is incredibly useful, we require human direction of data mining to be maximally efficient and fluent. Large companies and corporations globally use data mining and predictive analysis to increase productivity, efficiency, accuracy and precision, leading to less wasted time and fewer wasted resources. So, what exactly does that entail? Data mining is defined as “the process of discovering patterns and trends in large data sets.” This is where you, our newly hired data analysts, come into play. Let’s talk about what type of analyses we are going to use to create a Storm and Crime Data Report (SCDR). Since we are using data from a few years back to create a prediction model of future crime rates, this signifies using historical and predictive analyses; but above all is exploratory analysis. Predictive analysis is defined as “The process of extracting information from large data sets in order to make predictions and estimates about future outcomes”, whereas exploratory data analysis, otherwise known as EDA, allows an analyst to delve into the data set,

examine the interrelationships among the attributes, identify interesting subsets of the observations, and develop an initial idea of possible associations amongst the predictors, as well as between the predictors and the target variable. Next, let’s explore two specific angles of analysis: “Are certain storms connected with an increase in certain crimes?” and “Is the severity of a storm correlated to the severity of a crime?” To answer this, we require both qualitative and quantitative analyses; the quantifiable data will include the percentage increase in crime rate during storms. When we run the analysis in MySQL, the script we need to run is one that calls only events where StormEventID and CrimeEventID are not null, resembling a crime that occurs during a storm. Then, we need to set further parameters to our search; in this scenario, our search parameters only include crimes in the city of Miami, Florida, during the month of October in the year 2019. After running this script, we see the three most common crimes occurring in this data collection sample are ‘Violent Crimes’, ‘Burglary’, and ‘Murder and non-negligent manslaughter’. However, we also need to ensure the validity of this data. This leads to some important questions: Is only one month enough data to make correlations? Do we need more time? How valid is this data? In other words, where did this data come from?

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help