Computer Science
ITS 530 UC Exploratory Data Analysis and Visualization for E Commerce Data Paper

its 530

University of the Cumberlands

ITS

Question Description

I’m working on a Computer Science question and need guidance to help me study.

Problem : Many modern programs and technologies are used for organizing, manipulating, analyzing and visualizing data. Your assignment is to research, select a data visualization tool and explain how your selected technology can be used for data exploration in a specific domain. Include at least 5 principles or techniques you have learned in the course for effective data visualizations. Review the above prompt for your final project and identify the domain for your research paper by working with your group members. Your topic must be researchbased and should include a minimum of 6 references out of which 4 must be peerreviewed.

Topic : "Exploratory Data Analysis and Visualization for E-Commerce Data"

Requirements:

Provide a 500 word (or 2 pages double spaced) minimum reflection.

Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.

Share a personal connection that identifies specific knowledge and theories from this course.

Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment.

You should NOT, provide an overview of the assignments assigned in the course. The assignment asks that you reflect how the knowledge and skills obtained through meeting course objectives were applied or could be applied in the workplace.

Unformatted Attachment Preview

Research Project Proposal Template Group Name: Group 4 Group Members Sravani Medasani Kishore Kumar Miryala Parimala Rapol Jagadeesh Rapolu Ajay Reddy Sama Intasab Rose Zafar 1. Research Topic Describe the specific topic Exploratory Data Analysis and Visualization for E-Commerce for your research (3-5 Data sentences). This paper dives into the analysis and visualization of big data The research topic should sets focusing on the E-commerce Domain. Tableau has been our be appropriate for the choice as it stands to be one of the efficient data visualization course. The relationship tools when compared to others specifically in the E-commerce between/among the course Domain. This paper will answer some of the frequently asked concepts should be clearly industry questions to solve business problems. Big data can be specified. structured and unstructured. This paper also describes how Tableau will support platforms used for Big data such a NoSQL, Spark, Hadoop. 2. Research Problem Statement Write a brief research problem statement/Thesis statement (2-3 sentences). The main issues faced when we do data analysis and visualizations are with the dashboard performance. Huge data sets are available in the ecommerce domain. So, the visualization tools support both Real-Time and Historical data from the databases. The problem arises when the organization needs to decide the type of visualization tool as well as the type of dataset connection to get the most out of data and improve the performance of dashboards. What are you trying to investigate? This paper tries to investigate all the possible ways to design a dashboard with good performance solving the business questions that drive business decisions. 3. Significance/Backgro und Describe the significance of this topic in relation to course concepts. Significance should be explicitly stated, not implied (3-5 sentences). This paper dives into the day-to-day questions that arise for every E-commerce Business. The common questions that arise can only be solved with Visualization tools. Tableau, our primary Data Visualization tool demonstrates the best way to utilize the data visualization concepts such as creating customized Metrics and Dimensions based on the Data Types in a Data Set, Chart Types, Color Palette, Typography, and also following Editorial Thinking. 4. Research Questions List the primary research question(s). The research question(s) should be aligned to the research problem. Write the direction of inquiry in your research. What are you attempting to answer through these questions? - Which Data Visualization tool compares to be the best for Data Importation, Visualization, and Updating? What are the Top 10 Paid Marketing Campaigns contributing to high traffic to the Website? What are Top 10 Paid Marketing Campaigns contributing to Purchases/Conversions What are the Top 10 Referrals to the site? What percentage of Users purchased the first time they Add to Cart? What percentage of abandoned Users returned from Email Campaign and successfully made a purchase? What are the Top 10 Product Categories selling this month? What are the Top 10 Product Categories that generated the most revenue? 5. References Provide at least 8 current (within 5 years), scholarly, peer reviewed PRIMARY resources to support statements. Follow APA style in citing all resources. Nair, L. R., Shetty, S. D., & Shetty, S. D. (2016). Interactive Visual Analytics on Big Data: Tableau vs D3.js. Evergreen, S. D. H. (2020). Effective Data Visualization, Second Edition. Wang, Y., Han, F., Zhu, L., Deussen, O., Chen, B.: Line graph or scatter plot? Automatic selection of methods for visualizing trends in time series. Kirk, A. (2019). Data visualisation: a handbook for data driven design. London: SAGE Publications Ltd. Heidi Lam, Daniel Russell, Diane Tang, Tamara Munzner, "Session Viewer: Visual Exploratory Analysis of Web Session Logs", Visual Analytics Science and Technology. VAST. IEEE Symposium on, pp. 147-154. Lee, A. (2019, December 11). Exploratory Data Analysis on ECommerce Data. Retrieved from https://towardsdatascience.com/exploratory-data-analysis-on-ecommerce-data-be24c72b32b2 PepsiCo cuts analysis time by up to 90% with Tableau Trifacta. (n.d.). Retrieved from https://www.tableau.com/solutions/customer/pepsico-cutsanalysis-time-90-tableau-trifacta?utm_medium=homepage European Commission "E-commerce market study". [Online] Available from: http://ec.europa.eu/consumers/consumer_evidence/market_studie s/e_commerce/index_en.htm, 2015, [Accessed: 13th May 2015] ...
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Final Answer

Attached.

Exploratory Data Analysis and
Visualization for E-Commerce Data
Student Name:
Institution Affiliation:
Date:

Research Process
• In conducting the research on tableau visualization technique for ecommerce data,
we conducted research using different databases to gather data on the above.

• We reviewed each article to get to the bottom line and to understand the various
data visualization tools that are can be used to visual data apart from tableau

• In the process of conducting this research, we focused on the key word “data
visualization” and reviewed various tools used and why data visualization is
important.

• We finally presented sample tableau dataset and highlighted some of the challenges
in ecommerce industry.

Introduction
• Data is a fundamental asset to every organization.
• In today’s world that is entire of changing technology, extensive information
is being produced that is challenging to store, manage, and utilize for
decision reasons.

• It is essential to analyze and visualize this massive amount of data so that it
can be used for decision making.

• Some example of data visualization tools includes leaflet, Vega, Power BI,
Tableau, and Echarts.

Importance of Data Analysis and
Visualization
• Data analysis helps the business to understand the problems facing the
organization.

• Data analysis helps the industry to understand the products needed in the

market, the trends, and the vital things that are required by the customers.

• Data analysis helps the business to determine the type of advertisement that
useful and will be appealing to the customers.

• Visualization is vital since it converts large and small data into tables and
maps that can be used for decision making.

Data Visualization Tools
Echarts

• Echart is a data visualization tool that is rich in different chart type used for regular
statistics.

• It is not flexible and difficult for users to customize complex relational tables.
Leaflet

• Leaflet is a data visualization tool that is targeted for map application and it is
compatible with mobile phones.

• Every user for leaflet is supposed to have a secondary development capabilities.

Data Visualization Tools Conti…
Vega

• Vega is a data visualization tool that uses a set of interactive graphical
grammars that define the rule of mapping from data to graphic.

Power BI

• This type of data visualization uses multiple data sources.
• However, Power BI can only be used as a separate BI tool and cannot be
integrated with the existing systems.

Data Visualization Tools Conti…
Tableau

• Tableau is an intelligence tool for business for visually analyzing data.
• Tableau creates and distributes interactive and shareable dashboards, showing
trends, changes and densities of data in graphs and charts.

• The data visualization tool also connects to files, relational data sources and
significant data sources to get and process data.

• Tableau does not compel the users to write custom code and allows data mixing and
real-time collaboration.

Challenges in E-Commerce Industry
• E-commerce industry depends heavily on Google for traffic.
• E-commerce is faced with absence of online verification of customers.
• E-commerce does not maintain the loyalty of the customer.

Research Question
1. Which Data Visualization tool compares to be the best for Data Importation, Visualization,
and Updating?

2.
3.
4.
5.
6.

What are the Top 10 Paid Marketing Campaigns contributing to high traffic to the Website?
What are Top 10 Paid Marketing Campaigns contributing to Purchases/Conversions?
What are the Top 10 Referrals to the site?
What percentage of Users purchased the first time they Add to Cart?
What percentage of abandoned Users returned from Email Campaign and successfully made a
purchase?

7. What are the Top 10 Product Categories selling this month?
8. What are the Top 10 Product Categories that generated the most revenue?

Conclusion
• The information presented in the sample set is useful and shows how Tableau can
be used to analyze data.

• Tableau allows mixing real-time data collaboration and thus facilitates the process
of analyzing different types of data.

• Tableau thus, offers an effective and efficient way to visualize this data in manner
that further foster analysis and comprehension of the data.

• More research is needed in the area of data visualization particularly with the use of
tableau to get to understand how tableau can further support other development in
data research and presentation.

References
• Ali, S. M., Gupta, N., Nayak, G. K., & Lenka, R. K. (2016, December). Big data visualization: Tools and challenges. In 2016
2nd International Conference on Contemporary Computing and Informatics (IC3I) (pp. 656-660). IEEE.

• Anand, A., MacKinlay, J. D., & Wongsuphasawat, K. (2015). U.S. Patent Application No. 14/242,857.
• Caldarola, E. G., & Rinaldi, A. M. (2017, July). Big Data Visualization Tools: A Survey. In Proceedings of the 6th International

Conference on Data Science, Technology and Applications (pp. 296-305). SCITEPRESS-Science and Technology Publications, Lda.

• Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., & Phan, H. A. (2015). Tensor decompositions
for signal processing applications: From two-way to multiway component analysis. IEEE signal processing magazine, 32(2),
145-163.

• Hara, N. C., Boué, G., Laskar, J., & Correia, A. C. M. (2017). Radial velocity data analysis with compressed sensing
techniques. Monthly Notices of the Royal Astronomical Society, 464(1), 1220-1246.

• Kim, J., Stolte, C. R., MacKinlay, J. D., Stewart, R., Beran, B., Talbot, J., & Rueter, M. (2019). U.S. Patent No. 10,332,284.
Washington, DC: U.S. Patent and Trademark Office.

• Kumar, S., Toshniwal, D., & Parida, M. (2017). A comparative analysis of heterogeneity in road accident data using data
mining techniques. Evolving systems, 8(2), 147-155.

References Conti…
• Perkel, J. M. (2018). Data visualization tools drive interactivity and reproducibility in online publishing. Nature, 554(7690),
133-134.

• Pruneau, C. A. (2017). Data analysis techniques for physical scientists. Cambridge University Press.
• Rautenhaus, M., Böttinger, M., Siemen, S., Hoffman, R., Kirby, R. M., Mirzargar, M., ... & Westermann, R. (2017).

Visualization in meteorology—a survey of techniques and tools for data analysis tasks. IEEE Transactions on Visualization
and Computer Graphics, 24(12), 3268-3296.

• Stewart, R. (2017). U.S. Patent Application No. 14/603,302.
• Talia, D., Trunfio, P., & Marozzo, F. (2015). Data analysis in the cloud: models, techniques and applications. Elsevier.
• Terlecki, P., Xu, F., Shaw, M., Kim, V., & Wesley, R. (2015, May). On improving user response times in Tableau. In Proceedings
of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 1695-1706).

• Waese, J., & Provart, N. J. (2016). The Bio-Analytic Resource: Data visualization and analytic tools for...

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