Develop Data Literacy: Students will learn how to handle and explore real-world data, a crucial skill in today's business environment. Apply Statistical Techniques: The project provides an opportunity to use statistical analysis tools to uncover insights from data. Practice Critical Thinking: Students will need to interpret patterns and trends in the coffee shop data, fostering critical thinking skills.
Introduction: Briefly introduce the project, highlighting the importance of data analysis in business communication. Data Acquisition: Introduce the free coffee shop data set. You can find various public datasets related to coffee shops online, for example, on https://data.gov/ or Kaggle (https://www.kaggle.com/).
Data Exploration: Guide students through basic exploration techniques:
Descriptive Statistics: Calculate measures like mean, median, standard deviation for various variables (e.g., sales per day, average customer spend).
Visualization: Create charts and graphs (histograms, bar charts, scatter plots) to visualize data distribution and relationships.
Data Cleaning: If necessary, teach students how to identify and deal with missing data or outliers.
Data Analysis: Encourage students to formulate questions and analyze the data to answer them. For example: What are the busiest days/times for the coffee shop? Do certain types of drinks sell better on specific days? Is there a relationship between weather and coffee sales?
Conclusion and Communication: Ask students to summarize their findings, discuss potential limitations of the data, and present their results in a clear and concise manner (e.g., report, presentation).
Provide Resources: Offer tutorials or guides on basic data analysis tools like Excel or Google Sheets. Facilitate Collaboration: Encourage students to work in small groups for peer learning and support. Real-World Connection: Help students see how their analysis can be used by the coffee shop to make data-driven decisions.
Overall, this project allows IBC students to gain valuable data analysis skills in a relatable context, making statistics more engaging and relevant to their future careers.