Portfolio Final Attached document to use to start MIS450

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Option #1: Northwind Data Mining and Statistical Analysis Project – Planning

The objective of this Portfolio Project is mining data from a data warehouse, which contains data from the Northwind database that was constructed during your installation of PostgreSQL.

Below are the summarized tasks for this Portfolio Project.

Data Warehouse:

  • Create a data warehouse database, including the fact and dimension tables (star schema).
  • Create the schema for each table.
  • Populate the tables using either ETL (Pentaho) or SQL (PostgreSQL).

Preprocessing for SAS:

  • Extract data from the data warehouse, creating a file for input into SAS. The format of the file is your choice. Ensure SAS University Edition accepts your selected format.

Statistical Analysis Using SAS:

  • Import data created in the preprocessing step.
  • Conduct statistical analysis using the appropriate statistics from each category:
    • Summary statistics
    • Classification
    • Clustering
    • Association
  • Prepare an analysis report.

Using your plan prepared in Module 3, Milestone 1, and leveraging the data warehouse and preprocessing steps in Module 6, Milestone 2, complete the tasks under Statistical Analysis Using SAS.

Your analysis report must include:

  • An analysis of each variable in the data set
  • An analysis to determine which variables could serve as appropriate classifier variables
  • An analysis to determine if any variables are candidates for clustering
  • An analysis to determine if any variables have associations
  • Any tables, histograms, or scatterplot graphs necessary to support your analyses
  • A recommendation as to the suitability of this data set for meeting your organization’s business goal

Your project must meet the following requirements:

Refer to the Portfolio Project rubric in the Module 8 folder for more information on the expectations for this assignment.

Unformatted Attachment Preview

Running Head: DATA MINING AND STATISTICAL ANALYSIS Data Mining and Statistical Analysis The consideration of the developing a secure database using the PostgreSQL can be sophisticated. The creation of a data warehouse database following the procedures and screenshots below: 1 DATA MINING AND STATISTICAL ANALYSIS The outcome of the data warehouse creation is further provided as follows: 2 DATA MINING AND STATISTICAL ANALYSIS The provision of data analysis and interpretation, as well as configuration within a defined database, is best outlaid by the involvement of PostgreSQL which works more efficiently. Its simplicity determines the effectiveness of PostgreSQL in the procedures that are involved in analyzing data (Patrick, 2017). Star Schema Design 3 DATA MINING AND STATISTICAL ANALYSIS 4 Fact and Dimension Tables ProductID TimeID StoreID SalesAmount (‘000) AW2009 0001 001 $698 2050 200 $78 …… AX2019 There are different factors to consider when analyzing and interpreting data in a ProgreSQL system illustrated above. The provisions are distant to the determination of the results identified but are closely matching to the desired customer information. The development of a customer portfolio would require the combination and comparison of all the existing company information regarding inventory management and the supply chain system. DATA MINING AND STATISTICAL ANALYSIS It will ensure that each customer portfolio is matched with the respective product specifications that include the quantity. Application of ProgreSQL database Model for Customer Portfolio The primary issue that is being solved in this technological recommendation is that the sales and sales support team (part of the company) is not in alignment with the IT resource department. This prospect is to be solved in an IT manner that has led to the current recommendation of the purchase of the tablet devices for the sales employees who are selected. This recommendation of the implementation made is not a new set of thought for the employees (Kennedy, 2016). The employees of the company are well conversant with the new technological involvement since they at one time tried to bridge the gap using their gadgets which never worked well since their devices were not allowed to access the IT resources. The changes that are made are not aimed at transforming the business but improving its performance because the business itself is doing well apart from the departmental drag that is brought about by the increasing levels of service provision. Though there have been the efforts put for accessing the company's facilities manually, there is a need for quick access through a mobile device which in this case are the recommended tablets. There is also the need for the improvement of the aggression in the response rate between the IT infrastructure and its dynamically placed needs (Parker, Poe, & Vrbsky, 2014). Most importantly the significant scope of this recommendation prospect is to transition the sales team and the selected members of the sales support team to tablet-style mobile devices. 5 DATA MINING AND STATISTICAL ANALYSIS References Kennedy, M. (2016): MongoDB vs. SQL server 2008 performance showdown. Recovered at, 15. Parker Z., Poe, S., & Vrbsky, S. V. (2014): Comparing NoSQL MongoDB to an SQL DB; In Proceedings of the 51st ACM Southeast Conference (p. 5) ACM. Ρatгıck Seaгs, (2017): Getting Started with PostgreSQL on Mac OSX, retrieved fromhttps://www.codementor.io/engineerapart/getting-started-with-postgresql-onmac-osx-are8jcopb 6 ...
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