Part 1, Project Ovenriew Data Acquisition Report. {Part 1 of 4] Dueatendotweekz, Sunday at 11:59 pm. Er Submit a two-page {minimum} report describing your analysis fcws area, your initial set at requirements. your hypothesis. and a description of your data acquisition. Describe your data investigations, sources ioolted at, inltlai review ofdata obtained, and data formats encountered. Provide lnlliai impressions of data validity and quaEl'ly. Consider the following: 1. Research potential data sources In your area of interest Many sources are availabie from the U3. Government and private organizations. For instance, check out hgzinewwlgagglecomg and select \"Datasets" from the top. 2. Determine the focus area. Pagez e Determine an area of interest in which to perform anaiysis and which has a data source avaiiable. 3. Develop Initial Requirements. e Access the results of the analysis that are to be achieved and deveiop specific requirements for outcomes that you expect. . Identify datanfonnation necessary as inputs tothe analysis, which is specified based upon the requirements of those directing the analysis or customers (who will use the nished product cfthe analysis). Develop a set of questions that you will attempt to answer with your anaiysis. Develop specic variables regarding a \"population' that you will attempt to obtained Data may be numerical or categorical. Avoid textual data unless you plan to perform some term of Natural Language Processing or word vectortzation. This type of analysis is highly technical and not recommended. 4. Develop a hypothesis of what you expect to determine in your anatysis. e A hypothesis is a supposition or proposed explanation based on limited evidence as a starting point for further investigation. e An initial hypothesis helps to guide your investigation and search for data to support the hypothesis andtorthe null hypothesis. 5. Collect Datasets and Information based on your diesen area of interest. it Collect data and information from a variety of sources, as required. 0 Evaluate your data sources for vaiidity and quality. . Consider the following data characteristics: . Dened, Measurable, Unitized. Relatahle. Normalized, and Quality