Lecture 1 notes from 09/16 BUS STATS

Vocabulary flashcards covering key concepts, terms, and examples from the data analytics lecture video.

Business analytics

The process of analyzing data to generate insights and inform business decisions, helping understand customers, optimize operations, and gain a competitive edge.

Big data

Extremely large, fast-moving, and varied data generated from digital activities and sensors, requiring advanced analytics to extract meaningful insights.

Competitive advantage

The edge a company gains over rivals by leveraging data analytics to improve decision-making, efficiency, and targeting.

Data-driven decision making

Making choices based on analyzed data and evidence rather than intuition or opinion.

Data visualization

Using charts, graphs, and other visuals to communicate data findings clearly and effectively.

Excel

Spreadsheet software used as the primary analytics tool in the course; required on laptops; submissions are in Excel format.

Midterm exam

A three-part assessment (multiple choice, short answer, and an Excel component) scheduled during the course; tests data analytics concepts and Excel skills.

Group project policy

Projects are encouraged in groups, but students can submit individually if groups fail to collaborate effectively.

Mixed and video training

A 5% portion of the grade earned by completing and posting a two-hour video training (often from external resources) related to the course content.

Data monetization

The process by which companies derive revenue from data, often by enabling targeted advertising or selling insights.

Privacy concerns

Issues related to collecting, storing, and using personal data, including consent, security, and potential misuse.

Target pregnancy example

A case where shopping data allowed Target to predict a customer’s pregnancy and tailor promotions, illustrating both power and ethical considerations of analytics.

Data collection technologies

Digital transactions, GPS, sensors, and connected devices that generate data cheaply and continuously.

Ethical issues in big data

Debates about how data should be collected, stored, used, and governed to protect individuals’ rights and avoid harm.

Data storage energy costs

The electricity and cooling required for storing and processing data, impacting costs and sometimes national energy concerns.

Nokia disruption example

A historical lesson showing how failing to adapt to smartphone-era data and consumer demand can lead to decline.

AI impact on jobs

Artificial intelligence will automate some tasks but also create new roles; success depends on skill development and data literacy.

Data literacy

Ability to read, interpret, and communicate data effectively; a foundational skill in business analytics.

Statistics as language

Viewing statistics as the vocabulary of data analysis, providing the tools to describe and reason about data.

Descriptive statistics

Statistics that summarize and describe the main features of a data set (e.g., mean, median, distribution).

Bias in decision making

Human choices influenced by biases; data analytics helps mitigate bias by providing objective evidence.

Data-driven marketing examples

Using purchase and behavioral data to tailor marketing efforts, optimize promotions, and forecast demand.

Syllabus and office hours

Course structure, expectations, announcements, and instructor availability (e.g., Thursdays 1–2 PM, virtual).

Ethics and policy in analytics

Balancing data utility with privacy and governance considerations to guide responsible data use.