Foundations of Data Analytics I

Spreadsheet Basics & Data Management

Introducing spreadsheet tools and techniques for loading, exploring, joining, and cleaning large datasets, ensuring a robust foundation for analysis.

Descriptive and Inferential Statistics

Combining the generation and interpretation of descriptive statistics with an understanding of correlation vs. causality in the business context.

Pivot Tables and Advanced Data Analytics

Harnessing the power of pivot tables for summarizing large datasets and diving deeper into data interpretation techniques.

Visualization Techniques for Data Insights

Exploring and generating a range of visualizations, from line graphs to histograms, tailoring the visual representation to the dataset’s story.

Probability Fundamentals in Business

Introduction to the principles of probability, computing expected outcomes, and its application in business decision-making.

Translating Data into Actionable Presentations

Guiding students in crafting compelling data narratives, ensuring findings are presented clearly to various audiences.

Behavioral outcomes

These are key behaviors that a successful student will be able to put on their resumé to stand out from the crowd. By the end of this course, you will…

• Apply action-oriented analysis for a given data problem in order to make their analytics as valuable as possible.
• Use probabilistic thinking to inform decision making.

Skill outcomes

These are the core skills that this course teaches to achieve those behaviors. By the end of this course, you will be able to…

• Use industry-standard tools for data analytics to perform and present simple data analysis.
• Load, explore, join and clean large datasets.
• Generate and interpret pivot tables for large datasets.
• Appropriately select and generate a range of data visualizations for a given dataset, including line graphs, bar charts, histograms.
• Ask meaningful business questions using an understanding of the difference between correlation and causality in a business setting.
• Calculate expected outcomes for simple probability problems.
• Present data analysis clearly and with actionable outcomes to an audience with little context.