Data Analytics for Business
General
- Undergraduate; 4th year, 3rd year
- 30 learners; teams of 4
- 30 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 3/2 project matches
- Anywhere
- Academic experience
- Any
- Any
Categories
Skills
Project timeline
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February 9, 2022Experience start
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February 15, 2022Project Scope Meeting
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April 12, 2022Experience end
Timeline
-
February 9, 2022Experience start
-
February 15, 2022Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
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April 12, 2022Experience end
Overview
- Details
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Students will be able to take organizational datasets and form business problems around the data and the business. They can then use statistical and data analytics techniques to explore and model the data to provide insights and solutions to the business problems. The analysis of the data includes data cleaning, exploratory analysis and visualization, and various classification and regression models (Bayes, KNN, Tree-based methods, logistic regression, clustering, SVM, text analytics, ML, and AI). These analyses are mainly done through R programming.
A project appropriate for this course ideally includes an introduction to the business and its value chain, and more importantly, a relatively clean dataset (a table is preferred over a database) which usually includes an outcome variable of interest (inputs used to predict an output, e.g. prediction of users' type or demand). Projects without data are not acceptable.
- Learner skills
- Business analytics, Data analysis, Research
- Deliverables
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- A report on students’ findings and details of the analytics solution. This includes written synthesis of the business and the problem, exploratory data analysis and visualization, and one or more data models for prediction based on the provided data.
- A final presentation of the solution and recommendations to your organization.
- The code (in R) used for the analysis.
Project Examples
It is required that the project includes a high-level proposal/business problem statement including relevant data sets and definitions (or a data dictionary or a sample) and expected deliverables. Business datasets could be provided based on a non-disclosure agreement or in an anonymized/synthetic data format that is relevant to your organization and the project. The course instructor will review the data to confirm the scope and timing of the proposed problem and its alignment with the course requirements.
Students are capable to take the organizational data and transform it to provide insights and create models to make predictions for the business. Analytics solution may be applicable for (however they are not limited to) the following topics:
- Customer acquisition, retention, and churn
- Clustering of customer types for marketing
- Prediction of customer types (financial default prediction, etc.)
- Prediction of demand and sales data
- Analysis of textual data
- Analysis of image or voice data
- Market basket analysis
Any other context and topic which includes historical data is acceptable. Please feel free to contact the instructor for additional information.
Additional company criteria
Companies must answer the following questions to submit a match request to this experience:
A representative of the company will be available to answer questions from students in a timely manner for the duration of the project.
A representative of the company will be available for a pre-selection discussion with the administrator of the course to review the project scope.