Creating sql queries relating to interest rates

Assignment Help Database Management System
Reference no: EM133865322 , Length: word count:1000

Data Acquisition and Management

Type - Practical SQL Coding and Report

Task

You are required to:
Complete each of the SQL queries, both provided and self-constructed.
In your report, explain and summarise the business insights found from executing the SQL queries and the data visualisations.
Submit your query script file (.sql) with all your queries using the SQL link within the assessment time limit of 2 hours, i.e. In class.

Background
The SQL Server contains data on Peer-to-Peer Lending. Connection details will be provided on the day of the assessment.

Assessment Instructions
Follow the instructions to create the lending club table and populate it with data from a dataset that would be available to you on the day of the assessment.

Section #1: Server Connection and Simple SQL Queries

Part 1: Updating the table to add a column

Part 2: Creating a SELECT Query

Section #2: SQL for Business Insights

Create an SQL statement that answers the following business questions:

Part 1: Creating and analyzing SQL Queries relating to Loan and Funded Amounts
Part 2: Creating SQL Queries relating to Loan Terms
Part 3: Creating SQL Queries relating to Interest Rates Part 4: Creating SQL Queries relating to Loan Statuses Part 5: Creating SQL Queries relating to Loan Grades
Part 6: Creating SQL Queries relating to Loan Defaults/Delinquencies

Section #3: SQL and Data Visualization for Business Insights 1000 words
Part 1: Data Acquisition and Data Visualisations
Create a SELECT statement that returns all rows and columns of data.
Export and save the results of the query as CSV file.
Import the CSV file in either PowerBI or Tableau public.
Using the above CSV file, carefully examine the columns that offer potential for creating at least three distinct visualizations.
These visualizations should aim to uncover valuable business insights to assist the manager in evaluating and approving loan applications more effectively. For instance, one visualization could analyse customer demographics to identify those most prone to default on their loans.

Reference no: EM133865322

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