BIS 445 DeVry All Week I Labs

BIS 445 DeVry All Week I Labs 

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BIS 445 DeVry All Week iLabs 

BIS445 

BIS 445 DeVry Week 1 iLab 

Part a: Using action queries in SQL 

Note! Submit your assignment to the Dropbox located on the silver tab at the top of this page. (See the Syllabus section 

"Due Dates for Assignments & Exams" for due dates.) 

Remember This! Connect to the iLab here. 

iLAB OVERVIEW 

Scenario and Summary 

You have been requested to import a comma delimited text file into an existing SQL 2008 database. Using the SQL 

2008 Import Utility, you will create field mappings for a new SQL 2008 table and then upload your text file into this new 

table. After you upload your text file, you will perform some action queries to add, delete, and modify data in your new 

table. 

Upon completing this lab, you will be able to: 

Import text file data into an existing SQL 2008 database and create a new SQL 2008 table. 

Use action queries to add, delete, and modify data. 

Deliverables 

Submit the YourName_Lab1A_Questions.docx to the Week 1 iLab Dropbox. 

Section Deliverable 

Points 

Part A Import comma delimited text file. 6 

Part B Review uploaded data. 6 

Part C Add records into a table. 6 

Part D Delete records in a table. 6 

Part E 

Modify records in a table. 


Create a data warehouse using a star schema and then analyze data warehouse information.




iLAB OVERVIEW 


You will start with a comma delimited file that looks like this: 

When you are finished moving the data into the data warehouse, you will have split the customer, order, and product 

information into their own tables and, as a result, create a star schema that looks like this: 

As you can see, the FactOrders table has become a fact table containing statistics we might want to analyze as 

managers of a company. The fact table contains the primary keys of the other tables as foreign keys. Also in the fact 

table (FactOrders), we have stored the result of Price*Quantity in a newly created column called Sales_Total. 

Sales_Total is a fact we would like to analyze using an OLAP tool, such as a pivot table, in the future. 

The primary key of the FactOrders table is a Surrogate_Key which has been generated by the database management 

system. 

In the star schema, the products and customers table have become dimension tables. Furthermore, the star schema 

removes the duplicated customer and product names that existed in the original comma delimited file and puts them in 

normalized customer and product tables. 

Last of all, the star schema violates the third normal form, which says that no calculated fields should exist in the design 

(schema). In this case, the Sales_Total column is a calculated field. 

This violation isn't a problem because in a Decision Support System, software that accesses a data warehouse, it is 

acceptable to violate normal forms if it speeds up SQL queries by minimizing table joins and storing calculations in the 

database. 

Your task is to create a star schema that is capable of holding the information in the comma delimited file, and then 

load the star schema with the data from the text file. You will load the comma delimited data file into a temporary table 

in the SQL database. After you design the star schema, you will use SQL statements to populate the star schema with 

the necessary data from the temporary table. Finally, you will analyze the data by order year in order to find out which 

customers have the highest to lowest sales. 

Deliverables 

Submit the YourName_Lab2_Questions.docx to the Week 2 iLab Dropbox. 


Using Enterprise Data to Create Pivot Tables 




iLAB OVERVIEW 


You are working as a data analyst for a large manufacturing firm. You have been asked to develop a series of pivot 

table queries, which will present aggregated views of corporate data that can be used for decision support and trend 

analysis by decision-makers. 

Using SQL, you can create aggregated views of your data. Pivot tables are especially useful if you are analyzing large 

volumes of data. For example, if you wanted to determine the average value of sales for each sales person in your 

database, you could use a pivot table to aggregate thousands or millions of sales records. This is much faster than 

attempting to scroll through all of the available records.

You will log into our Citrix Server, access SQL Server, and the BIS445_AdventureWorks database, and then develop 

five pivot tables. 

Deliverables 


Section Deliverable 

Points 

Part A Create a query to determine cost data for manufacturing a product. 10 

Part B Develop a query to analyze average, minimum, and maximum costs for each product. 10 

Part C Determine average sales amount for each salesperson. 10 

Part D Determine average cost based upon a number of days to manufacture a product. 15 

Part E Analyze the number of purchase orders placed by each vendor. 15 

iLAB STEPS 

Preparation 

Back to Top 

Download the Week3_Lab3_Questions.docx from DocSharing. You will answer the questions and provide screen prints 

as required for each part of the lab. 


Create a data warehouse using a star schema and then analyze data warehouse information. 




iLAB OVERVIEW 


The management of the Coffee Merchant needs to find the sales pattern by data mining the sales data. The purpose 

of the data mining exercise is to find any of the sales patterns. In particular, the senior management team is interested 

in the quarterly sales reports by different sales region. From the experience, the team thinks that each region would 

have different product needs at a different quarter. As a sales analyst, you need to apply the data mining techniques 

using SQL Server Reporting Services. 

Your assignment is to create a parameter, matrix report, and explain the report to find sales a pattern at a different 

region on a different fiscal year quarter. 

Deliverables 


Steps Deliverable Points 

Step 1 Translate the business requirements into operational data mining specifications. 10 

Step 2d to Step 

Find the right tables and columns from data warehouse (Coffee Merchant database). 10 

2h 

Step 4 Create a parametric, metric report. 10

Final Deliverable 

Lab 4 Student Answer Sheet 

30 

iLAB STEPS 

Preparation 

Back to Top 



Read the scenario and translate the requirements into specifications. 

Connect to iLab. 

Connect to BIS445SQL data source. 

Connect to Coffee Merchant database. 

Part A: Set up data by writing SQL script 

STEP 1: Problem Description 

Back to Top 


Predict sales patterns using simple linear regression 




iLAB OVERVIEW 


Joe Sanders scratched his head. He owned two Hungry Boy Subs franchises in a city. In an unusual arrangement, one 

of the franchises was located in the food court of the local mall. The other franchise was about two blocks down the 

street from the mall. The area in which Joe had purchased the franchises was in a new end of town, well-known for its 

good schools, professional offices, and well-kept neighborhoods. The area had been growing rapidly since Joe 

purchased the franchise about 10 months ago. 

Joe purchased the local mall's sub shop first, and found it to be immensely successful. Encouraged by the success of 

the food court shop, he went on to purchase a new franchise in a street vendor area up the street. Even though the two 

franchises were close together geographically, he felt they catered to different markets; one to mall employees and 

mall-goers, and another to passers-by who had no intention of going to the mall. 

Joe thought the sales patterns of his sales at the food court would help him forecast sales of his street location, and 

thus help him determine quantities of various products to order. Particularly, Joe wanted to forecast the number of 

drinks he would sell in each period and the percent of sales that are cold drinks. 

Therefore, Joe collected data from both franchises and decided to do an analysis to see if he could design a forecasting 

model for each location. He was also interested in analyzing buying patterns in both franchises to see if there were any 

similarities or differences. The data is found in DrinkSales.txt. 

Your task is to predict sales patterns using simple linear regression. You will import the comma delimited file into a SQL 

database table that has marketing research data on drinking patterns and perform some basic analyses on this data 

by creating a scatter plot. After you analyze this data you will use simple regression to try to predict drink patterns. 

Deliverables


Section Deliverable Points 

Part A Import comma delimited file and create SQL 2008 table. 10 

Part B Create scatter plots on drink sales. 20 

Part C Regression analyses on drink sales. 30 

iLAB STEPS 

Preparation 

Back to Top 



Create a folder on your local drive named Transfer. 

Download the Week5_DrinkSales.txt file from DocSharing to your Transfer folder. 

Login to the Citrix remote lab. 

Follow the login instructions located in the iLab tab in Course Home. 


Part a: Create a BIDS Analysis Services Project 




iLAB OVERVIEW 


You have been asked to analyze the Redwood database by using multidimensional techniques. One way to do this is 

to create a data cube that represents data in various dimensions (attributes) along with various measures over a period 

of time. 

Upon completing this lab, you will be able to: 

Create a BIDS Analysis Services project 

Define a data source and view 

Define dimensions and attributes 

Define a cube 

Deliverables 

Submit the YourName_Lab6A_Questions.docx to the Week 6 iLab Dropbox. 

Section Deliverable Points 

Part A Define a BIDS Analysis Services project 5 

Part B Define a data source 5 

Part C Define a data source view 5

Part D Define dimension(s) and add attribute(s) to dimension(s) 5 

Part E Define a cube using measure(s), dimension(s), and time 10 

iLAB STEPS

BIS 445 DeVry All Week I Labs

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