Walmart : A case study- 2000 words

Walmart : A case study- 2000 words
Harvard Reference in Turnitin
Referencing: In the main body of your submission you must give credit to authors on whose research your work is based. Append to your submission a reference list that indicates the books, articles, etc. that you have read or quoted in order to complete this assignment (e.g. for books: surname of author and initials, year of publication, title of book, edition, publisher: place of publication).
Criteria To achieve each outcome, must demonstrate the ability to: Evaluate Walmart’s given dataset and perform data analysis.  Assess, build and depict a machine learning model  Apply Big data mining concepts and create a report.
BIBLIOGRAPHY
Essential Krishnan, K. (2013) Data Warehousing in the Age of Big Data. Elsevier.
Du, H (2010), Data Mining Techniques and Applications – An introduction.
CEngage learning
Background
Foreman, J. W. (2013) Data Smart: Using Data Science to Transform Information into Insight. Wiley.
Provost, F. and Fawcett, T. (2013) Data Science for Business. O’Reilly Media. Kimball, R., and Ross, M. (2013), The Data Warehouse Toolkit (3rd Ed.) Wiley.
Schutt, R. and O’Neill, C. (Eds.) (2013) Doing Data Science. O’Reilly Media.
Smolan, R. and Erwitt, J. (2012), The Human Face of Big Data. Sterling. Tuffery, S. (2011), Data Mining and Statistics for Decision Making. Wiley.
Witten, I.H., Frank, E. and Hall, M.A. (2011), Data Mining – Practical Machine Learning Tools and Techniques. Morgan Kaufmann
Websites
http://www.kimballgroup.com tkdd.acm.org
http://www.computer.org/tkde (IEEE)
TASK DESCRIPTION
One of the leading retail stores in the USA, Walmart, would like to predict its sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inefficiency of its current machine learning model. And so, you are tasked with developing an alternate model to predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc (dataset description given below).
One of the leading retail stores in the USA, Walmart, would like to predict its sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inefficiency of its current machine learning model. And so, you are tasked with developing an alternate model to predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc (dataset description given below).
As a Big Data Engineer responsible for Walmart’s Big Data Infrastructure and tools, use the Walmart dataset provided to create a professional standard report based on your analysis of the aforementioned datasets. For this to be achieved, you are asked to select and apply an appropriate data mining process in a Big Data context to the given problem domain and to produce an effective visualization of said analysis.
Problem : Walmart has branches all over the country but it must decide how to cater to the respective needs of its employees and customers has its core business functions continue to grow. How can Walmart , use the hidden information in the given case to demonstrate this ?
Dataset Description
Listed on Moodle is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file WalmartStoresales. Within this file you will find the fStore – the store number
Date – the week of sales
Weekly_Sales – sales for the given store
Holiday_Flag – whether the week is a special holiday week 1
– Holiday week 0 –
Non-holiday week Temperature –
Temperature on the day of sale
Fuel_Price – Cost of fuel in the region
CPI – Prevailing consumer price index
Unemployment – Prevailing unemployment rate
Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13
Labor Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13
Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13
Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13ollowing CSV headings:
A word document or PDF file must be submitted that demonstrates all completed tasks as listed above and must include the following: Abstract, Introduction , Literature Review, Methods, Results and Conclusion
Create a folder, name it as ACUA6002_Studentid. Copy your database dump, dataset and visualization reports into your assignment folder. Zip
LEARNING OUTCOMES Upon the successful completion of this module, the student should be able to demonstrate the ability to:
1. Critically discuss the concepts behind, sources of and problems related to Big Data
2. Design, build and professionally document a small data warehouse suitable for use with Big Data
3. Perform data analysis and reporting on complex data within a Big Data data warehouse
4. Demonstrate a creative use of appropriate data mining and data visualisation techniques within a Big Data context.
INDICATIVE CONTENT
• Origins and sources of Big Data; volume, velocity, variety and ambiguity problems
• Big Data architectures and technologies; Hadoop, NoSQL and others
• Data warehousing for Big Data; traditional and modern architectures; workload management; emerging technologies
• Information management; Big Data lifecycle; semantic layers and the semantic web
• Data-driven architectures; master data management and metadata management
• Analysis and visualization of Big Data; data mining, text mining, data modeling
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