Task 1 Data Driven Decision Making

Data driven decision-making - (3D) - Increasingly organisations are looking to make decisions that are based on the evidence of real data. Data analytics toolsets are evolving rapidly and many organisations have invested heavily in information architecture so that they have the capability to move transactional data and external data into data warehouses and provide end- users with specific data analytics software applications to support evidence based (data driven) decision making. However, many organisations are still struggling to make the transition to a data driven decision making paradigm.

Your role is the Lead Data Analyst in CoreLogic NZ Limited, who has office in both Wellington and Auckland city. The head office address is located at Level 2, 275 Cuba St, PO Box 4072, Wellington 6140, New Zealand. CoreLogic is a leading property information, analytics and services provider in the New Zealand. Now you are required to conduct a relevant literature review regarding data driven decision-making (3D) and prepare a strategic briefing report of about 1300 words for the Frank Martell, Chief Executive Officer, of CoreLogic NZ Limited on key aspects of data driven decision making as outlined below:

Task 1.1) Identify from the existing literature and discuss the relevant decision making theories and frameworks which would inform a deep or through understanding of the decision making process in organisations (about 600 words).

Task 1.2) Provide a comprehensive definition of data driven decision making and explain briefly how your definition has been informed by specific literature on data driven decision-making (about 100 words).

Task 1.3) Based on an understanding of how decisions are made in organisations, critically analyze and evaluate how changes in organisational decision making process would be important for the CoreLogic NZ Limited for its successful transition to a data driven decision (3D) making paradigm (about 600 words).

Task 2 Exploratory Data Analysis, Decision Tree and Linear Regression model Analysis

Now-a-days Kiwi commercial banks are using data mining techniques, machine learning, statistics and artificial intelligence for analysing the credit worthiness of a borrower. Assume you are a credit analyst of ASB bank and now you are required to do the following tasks 2.1 to 2.3 before approving credit decision to each new loan applicant by using ASB bank's creditdata.csv data set provided in uPortal UUIS8008 Assignment-1 URL link and Data Dictionary in Appendix-A.

Task 2.1) Using RapidMiner Studio data mining tool conduct an in-depth exploratory data analysis of the creditdata.csv data set. Summarise the key findings of your exploratory data analysis - in terms of describing key characteristics of each of the variables in the creditdata.csv data set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values, inconsistency and relationships with other variables if relevant, - in a table named Table 2.1: Results of Exploratory Data Analysis for the creditdata.csv Data Set.

Also, Identify top five (5) key variables, which contribute to determining whether a potential loan applicant is a good credit risk or a bad credit risk and the rationale for why you have selected your five top variables for predicting credit risk. Discuss each of your five top variables in terms of the results of your exploratory data analysis (About 700 words).

Task 2.2) Build a Decision Tree model for predicting Credit Risk based on the creditdata.csv data set using RapidMiner and an appropriate set of data mining operators and a reduced set of variables from creditdata.csv determined by your exploratory data analysis in Task 2.2. Provide the following for Task 2.2:

(i) (1) Final Decision Tree Model process, (2) Final Decision Tree diagram, and (3) Decision tree rules.

(ii) Briefly explain your final Decision Tree Model Process, and discuss the results of the Final Decision Tree Model drawing on the key outputs (Decision Tree Diagram, Decision Tree Rules, model performance) for predicting credit risk. This discussion should be based on the contribution of each of the top five variables to the Final Decision Tree Model and relevant supporting literature on the interpretation of decision trees (About 300 words).

Task 2.3) Build a Linear Regression model for predicting the creditworthiness of an applicant using a RapidMiner data mining process and an appropriate set of data mining operators and a reduced set of variables from the creditdata.csv data set determined by your exploratory data analysis in Provide the following for Task 2.3:

(i) A screen capture of Final Linear Regression Model process and briefly describe your Final Linear Regression Model process

(ii) A table named Table 2.4 named Results of Final Linear Regression Model for Task
2.4 for creditdata.csv data set.

(iii) Discuss the results of the Final Linear Regression Model for creditdata.csv data set drawing on the key outputs (coefficients, standardised coefficients, t-statistics values, p- values and significance levels etc.) for predicting credit risk and relevant supporting literature on the interpretation of a Linear Regression Model (About 300 words).

Task 3 Sales Reports using Tableau Desktop

The aim of every retail superstore business is attracting new customers, retaining existing customers, and selling more to each customer. To ensure this, a retail superstore business needs to offer customers the products they want at the right prices. Moreover, it needs to ensure the right customer experience as well as profitability. In order to achieve the aforementioned objectives, many retail businesses today are using data analytics and visualization tools like Tableau Desktop. Assume you are a junior data analyst of New Zealand based Business Intelligence (BI) consultant company of retail superstore located in United States of America. You are an expert for Tableau Desktop analysis and now you are required to create the sales report by covering the sub tasks 3.1 to 3.4.

With the given Excel file SalesSuperstore_US.xlsx on the UUIS8008 course study desk Assignment-1 link and by using Tableau Desktop produce the four following reports with appropriate accompanying graphs based on a Tableau workbook sheet view for each. Briefly comment on each report in about 150 words in terms of what trends and patterns are apparent in each report.
The SalesSuperstore_US.xlsx file contains the following dimensions and information with 10,000 data rows:
1. Customer Name, ID, Customer Segment

2. Location - Region, State, City, Post code

3. Product Category, Sub Category, Name, ID, Unit Price

4. Order Information

5. Shipping Information

6. Sales, Quantity, discount and Profit Information

Task 3.1) Create a report and accompanying table/graph using Tableau that shows a trend analysis for sales by different product categories, sub categories for each quarter over the years 2015 to 2018 and comment on key trends and patterns apparent in this report (About 150 words).

Task 3.2) Create a report and accompanying table/graph using Tableau that shows for each Product Category Average Profit and Total Sales for each month over the years 2015 to 2018. Find out the highest and lowest profitable category by specific time and sales volume in your graphical view. Also comment on the other key trends and patterns apparent in this report (About 150 words).

Task 3.3) Develop a geographical map presentation using Tableau that shows graphically the relative size by city within each state, by product total sales and profit for year 2016 and comment on key trends and patterns in this report (About 150 words).

Task 3.4) Prepare a report and accompanying table/graph using Tableau that shows for Product Sub Categories that are office supplies based average quantity, total sales and average profit for year-quarter-month (YY-QQ-MM) basis over the years from 2015 to 2018 and comment on key trends and patterns in this report (About 150 words).

Attachment:- Specifications and data.rar