Data Analytics Capstone Project
Timeline
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July 1, 2020Experience start
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July 10, 2020Project Scope Meeting
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July 31, 2020Data Understanding/Data Preparation
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August 22, 2020Displaying Data using Data Analytics Tools
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September 5, 2020Gathering more value from data using Python
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September 19, 2020Managing Data in the Cloud
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October 3, 2020Experience end
Timeline
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July 1, 2020Experience start
-
July 10, 2020Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
-
July 31, 2020Data Understanding/Data Preparation
Students should generate documentation on data understanding for the data assets available, provided, or to be gathered. Documentation should also be provided on the data preparation steps utilized to prepare the data for further analysis.
-
August 22, 2020Displaying Data using Data Analytics Tools
Students should generate a data visualization example using Power BI outlining the intended/exercised data to generate insight and recommendations.
-
September 5, 2020Gathering more value from data using Python
Students will demonstrate their approach to extracting more value from their data using Python for data analysis.
-
September 19, 2020Managing Data in the Cloud
Students will demonstrate ways to manage data in the cloud using MS Azure as a platform, and provide recommendations on cloud solutions.
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October 3, 2020Final Report/Demonstration
Students should generate documentation on the evaluation of the model and results, along with the outcomes of deployment of the model on production data. Any presentation materials and generated reports should be ready for presentation.
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October 3, 2020Experience end
Experience scope
Categories
Information technology Data analysisSkills
business analytics business consulting data analytics storytelling and data visualization data analysisAre you a firm that is looking to explore the value of data analytics? Students in the SAIT Data Analytics certificate are trained in data extraction and transformation as well as data preparation, data modeling and reporting on pre-existing data gathered by the organization. These students will work with your organization to analyze your data sets and provide any recommendations they may have as a result of the analysis.
Learners
The final project deliverable will include:
- A report outlining the work they performed, analysis they conducted including visualizations and recommendations they may have as a result of analysis. The students will provide a proof of concept of the solution.
- A 20-minute presentation of the project and results to the industry partner and classmates.
Project timeline
-
July 1, 2020Experience start
-
July 10, 2020Project Scope Meeting
-
July 31, 2020Data Understanding/Data Preparation
-
August 22, 2020Displaying Data using Data Analytics Tools
-
September 5, 2020Gathering more value from data using Python
-
September 19, 2020Managing Data in the Cloud
-
October 3, 2020Experience end
Timeline
-
July 1, 2020Experience start
-
July 10, 2020Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
-
July 31, 2020Data Understanding/Data Preparation
Students should generate documentation on data understanding for the data assets available, provided, or to be gathered. Documentation should also be provided on the data preparation steps utilized to prepare the data for further analysis.
-
August 22, 2020Displaying Data using Data Analytics Tools
Students should generate a data visualization example using Power BI outlining the intended/exercised data to generate insight and recommendations.
-
September 5, 2020Gathering more value from data using Python
Students will demonstrate their approach to extracting more value from their data using Python for data analysis.
-
September 19, 2020Managing Data in the Cloud
Students will demonstrate ways to manage data in the cloud using MS Azure as a platform, and provide recommendations on cloud solutions.
-
October 3, 2020Final Report/Demonstration
Students should generate documentation on the evaluation of the model and results, along with the outcomes of deployment of the model on production data. Any presentation materials and generated reports should be ready for presentation.
-
October 3, 2020Experience end
Project Examples
Requirements
Beginning in June, students in groups of 4 will spend 102 hours assisting your company by providing analytical research and recommendations tailored to one of your company’s data opportunities or challenges.
Students will develop the following skills and competencies and will develop the knowledge, skills, and aptitude to apply fundamental principles of data analytics to support business decision-making processes, creating accurate and meaningful storytelling with actionable insights. They will accomplish this using a foundation of data management and ethics. The students are developing skills in the Microsoft stack and IBM SPSS, and will be expected to use these technologies for the project.
Program Outcomes
- Understand database concepts and how to design and implement databases to maintain data integrity.
- Develop skills to query data using SQL scripting.
- Manipulate data using ETL principles (extract, transform, load) to develop a data repository that can then be analyzed in a business context that is relevant to decision-making.
- Apply fundamental data analytics principles, aligning data and business processes to create accurate, actionable insights.
- Use industry recognized programs and tools to extract meaning from data (SPSS, Power BI).
- Present data that communicate data analysis effectively and accurately for a business audience using visualizations (dashboards) and reports.
- Develop skills in Python programming, specific to data analysis functions.
- Introduce cloud principles for managing data in the cloud, using Microsoft Azure as the platform.
Students may work with the company in one of three ways
- Assist organizations in data gathering research and/or prepare data for future use by the organization. Students can help design and model databases to gather and store data for future analysis.
- Assist organizations in preparing existing data for analysis. Students may perform data quality checks, data cleaning, and data transformation exercises on existing data to ready data for analysis by the organization.
- Assist organizations in data analysis. Using organizational data, students may conduct data analysis and design data analytics reports to be delivered to the firm.
Project examples include but are not limited to:
- Analysis of customer segmentation relative to different products and services, to enhance marketing campaigns and refocus your products/services.
- Investigate predictive models to understand trends in sales, attrition rates, and profits that impact your business.
- Propose new ways to visualize data through tables and plots that can provide new insights for managers.
Participating industry partners provide data sets.
Additional company criteria
Companies must answer the following questions to submit a match request to this experience:
Provide data sets for students to analyze
Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.
Be available for a quick phone call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.
Timeline
-
July 1, 2020Experience start
-
July 10, 2020Project Scope Meeting
-
July 31, 2020Data Understanding/Data Preparation
-
August 22, 2020Displaying Data using Data Analytics Tools
-
September 5, 2020Gathering more value from data using Python
-
September 19, 2020Managing Data in the Cloud
-
October 3, 2020Experience end
Timeline
-
July 1, 2020Experience start
-
July 10, 2020Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
-
July 31, 2020Data Understanding/Data Preparation
Students should generate documentation on data understanding for the data assets available, provided, or to be gathered. Documentation should also be provided on the data preparation steps utilized to prepare the data for further analysis.
-
August 22, 2020Displaying Data using Data Analytics Tools
Students should generate a data visualization example using Power BI outlining the intended/exercised data to generate insight and recommendations.
-
September 5, 2020Gathering more value from data using Python
Students will demonstrate their approach to extracting more value from their data using Python for data analysis.
-
September 19, 2020Managing Data in the Cloud
Students will demonstrate ways to manage data in the cloud using MS Azure as a platform, and provide recommendations on cloud solutions.
-
October 3, 2020Final Report/Demonstration
Students should generate documentation on the evaluation of the model and results, along with the outcomes of deployment of the model on production data. Any presentation materials and generated reports should be ready for presentation.
-
October 3, 2020Experience end