Lecture 1 notes from 09/16 BUS STATS

Course Overview

• Course purpose: Introduce business analytics and how data analysis has transformed businesses. Course created about five years ago in response to the growing importance of data in business fields and the need to understand techniques, challenges, and their practical use.
• Emphasis on discussion and participation: open to questions, curiosity, and interactive discussions; aims to be engaging and fun.
• Scope: broad coverage intended to give a foundation, with room to go deeper into technical topics if students wish.

Course Goals

• Primary goal: Help students explore the context and importance of data analytics and become proficient with strong tools in the field.
• Practical skills: Learn to handle data and perform basic analysis so you can confidently open a data file (e.g., a few megabytes in Excel), inspect it, and perform basic tasks without fear.
• Statistical literacy: View statistics as the language of data analytics; learn to interpret and use it in business contexts.
• Application in fields: Although introductory, examples will be tied to fields like accounting and management, with a focus on real-world applications in the second half of the course.
• Mindset and communication: Emphasize a rigorous way of thinking about data, asking the right questions, and expressing results with precise, domain-appropriate language.

Course Structure and Schedule

• Overall design: Two-part quarter.
  • Part 1 (fundamentals, weeks 1–4): Role of big data, ethical issues, and the basics of data analytics; introduction to what big data is and why ethics matter.
  • Part 2 (tools, weeks 2–4 and beyond): Basics of statistics, how we answer questions in business analytics, and visualization.
• Weekly cadence:
  • Each week includes lecture, class activities, and readings posted for that week.
  • Readings are supplementary; lecture notes posted after each class (sometimes before).
  • Most midterm/major assessment content focuses on lecture notes.
  • Beginning in week 3–4, Excel-based practice files will be posted so you can work in real time during class.
• Midterm exam: Week 5, Wednesday, October 8 (approximate date mentioned).
  • Structure: three parts — multiple choice, short answer, and Excel-based problems.
  • Requirement: bring laptops to complete the Excel portion.
• Projects: Weekly projects designed to be completed in groups (ideally groups of 3 to share the grade). If group work fails, students may submit the project individually.
• Group formation: Students are encouraged to talk to neighbors in the first four weeks to form groups; you can switch groups if needed and submit individually if a group is not functioning.
• Tutoring: There may be tutors available for this course to help with assignments and the more challenging parts.

Learning Tools and Resources

• Lectures and notes:
  • Lectures are recorded; Zoom link is provided so the instructor can record, not necessarily for live attendance.
  • Lecture notes will be posted after each class; sometimes posted before.
• Readings: Supplemental readings posted; the primary assessment material will be lecture notes.
• Office hours: Thursdays, 1–2 PM (virtual). Zoom link will be posted for office hours.
• Communication: Email the instructor (address on syllabus) for questions; regular announcements and weekly reminders about expectations.
• Software and hardware:
  • Excel: Primary analytical tool for the course. Students should bring laptops and use Excel (not the online version) for homework and in-class work.
  • If a student lacks a laptop, they should inform the instructor in advance to arrange alternatives.
  • Microsoft Office is provided for free to students; download instructions will be provided.
• Assessments and participation:
  • 5% of the grade comes from mixed-video training (micro-credential style learning modules).
  • Participation points are earned by engaging in class discussions and asking questions; attendance and active participation contribute to your grade.
  • Late or incomplete video training will incur penalties; aim to complete it by the stated deadline.
• External learning resources:
  • Optional two-hour video by Dennis Taylor posted alongside slides; can be used for additional learning and to earn the 5% video training credit.
  • The department may require or encourage these extra modules for a certification that can be posted to a student folder.

Tools and Practices for Success

• Regular, dedicated study time is encouraged; lack of procrastination and consistent effort improves outcomes.
• Study groups: Form study groups early to discuss concepts and practice problems.
• Active participation: In-class questions and participation contribute to the grade; instructors will track participation.
• Practice with Excel: Expect to use Excel for problem sets and the midterm; practice with the posted Excel files starting in Week 3.
• Data literacy mindset: Learn to interpret data in the context of business questions, and to express results with precise business language.

Data Analytics: Key Concepts and Real-World Context

• What is data analytics?
  • It is the process of analyzing data to generate insights and answer questions.
  • It uses statistics and computing to understand data and inform decisions in business contexts.
• Why data analytics in business matters:
  • It provides a competitive advantage by enabling data-driven decisions (examples include Google, Amazon, Microsoft, Meta).
  • It helps answer core business questions: who are customers, what do they want, how price-sensitive are they, how to reach and retain customers, and how to optimize production and distribution.
• Data vs information: Data are raw facts; information is data that have been processed to answer questions.
• Data analytics vs physics analogy:
  • In physics, experiments collect data to test theories; in business analytics, data are collected from real-world operations to test business hypotheses and inform decisions.
• The two main reasons for using data analytics in business:
  • Reduce information gaps and uncover unknowns about customers, markets, and processes.
  • Reduce reliance on human biases and subjective judgments by providing evidence-based insights.
• Practical business impact examples:
  • Marketing: identifying customer preferences, price sensitivity, and effective targeting.
  • Customer acquisition and retention: identifying potential customers and strategies to keep existing ones.
  • Operations: optimizing production, distribution networks, and warehouse placement.
  • Human resources: predicting employee turnover and improving the workplace to retain staff.
• Ethical, privacy, and policy considerations:
  • The data revolution raises privacy concerns (e.g., how data is collected, stored, and used) and necessitates ethical considerations and governance.
  • High-profile examples illustrate both the power and the risk of data analytics (e.g., targeted advertising; privacy-breach concerns).
• Data growth and storage implications (examples from the instructor):
  • Data growth over the years has exploded, with large-scale growth in data volume and availability.
  • Example data points cited during the talk (values given in zettabytes):
  • $D_{1997} = 0.03 ext{ zettabytes}$
  • $D_{2018} = 333 ext{ zettabytes}$
  • $D_{2020} = 2.59 ext{ zettabytes}$
  • $D_{ ext{this year}} = 175 ext{ zettabytes}$
  • The rapid growth in data storage has implications for energy use and infrastructure (e.g., data centers and electricity costs).
• Data monetization and business models:
  • Big tech monetizes data by offering targeted advertising and by selling insights rather than raw data; platforms like Google monetize by delivering targeted ads to users based on their data.
  • The value lies in knowing who to target, when, and how much to pay for advertising on a platform.
• Case study: Target pregnancy example (and associated ethical concerns):
  • Retailers can use purchase history and other signals to predict sensitive conditions (e.g., pregnancy) and tailor promotions accordingly.
  • This can create powerful marketing opportunities but also raises serious privacy and ethical concerns when a third party learns personal information about a consumer (e.g., a teenager or family member learning of a pregnancy before the individual or their family).
• Industry trends and job outlook:
  • US Bureau of Labor Statistics projections indicate strong growth in data-related roles (e.g., data scientists at ~36% growth; positions offering competitive salaries and opportunities for advancement).
  • Emerging roles and the need for data skills across industries suggest a positive career outlook for those with data analytics competencies.
• The role of AI and the future of work:
  • AI will automate some tasks, but it will also create new opportunities and roles.
  • Successful data professionals will combine domain knowledge with data skills and the ability to harness AI/ML tools to extract actionable insights.
  • The most valuable skills will include critical thinking, data storytelling, and understanding which analyses answer the right business questions.
• Summary perspective:
  • We are in an era of data proliferation that demands a disciplined approach to data analytics.
  • The goal is to move from data to truth and insight, supporting better business decisions while navigating privacy and ethical considerations.

Practical Implications and Real-World Relevance

• Why modern businesses invest in data analytics:
  • To understand customer preferences and tailor offerings (e.g., product iterations, pricing, marketing channels).
  • To optimize operations, distribution, and workforce management for efficiency and cost savings.
  • To identify and capitalize on new market opportunities through data-driven evidence.
• Balancing innovation with ethics:
  • As data collection and analytics capabilities expand, so do the responsibilities to protect privacy and prevent harm.
  • Policies and governance frameworks are essential to ensure responsible use of data.
• Educational takeaways for students:
  • Build foundational data skills (especially in Excel) while developing the ability to reason with data and communicate insights clearly.
  • Recognize the importance of data literacy in a wide range of business roles and industries.
  • Prepare for a future where data and AI are integral to decision-making and operations.

Key Figures and Terminology (glossary-style quick references)

• Data analytics: The process of analyzing data to generate insights and answer questions.
• Business analytics: The broader application of data analytics to business problems and decision-making.
• Big data: Extremely large and complex data sets that require advanced methods to store, process, and analyze.
• Mixed and video training: A 5% portion of the grade tied to completing specified video-based learning modules.
• Excel-based work: Core tool for analysis in this course; in-class and homework tasks rely on Excel (not the online version).
• Data monetization: The practice of extracting value from data by turning insights into business actions, such as targeted advertising.
• AI/ML in analytics: Tools that enable automated pattern discovery and predictive insights, influencing job roles and skill requirements.

Important Dates and Requirements (summary)

• Midterm: Week 5, Wednesday, October 8 (approximate; verify in the syllabus).
• Excel: Primary platform for in-class work and homework; online Excel versions may lack features needed for the course; bring a laptop to classes and exams.
• Laptop requirement for exams: Students must bring their own laptops; arrangements can be made if a student lacks a device.
• Office hours: Thursdays 1–2 PM (virtual). Zoom link will be posted.
• Participation: Active engagement and questioning are part of the grade.
• Course communications: Syllabus and announcements posted; instructor will email and post reminders throughout the term.
• Tutoring: Availability announced; designed to help with challenging topics and assignments.

Practical Takeaways for Studying

• Focus on understanding the context and questions you’re trying to answer with data, not just the mechanics of the tools.
• Practice with Excel using the provided files to gain fluency with data inspection, basic calculations, and reporting before attempting more advanced analyses.
• Engage with peers and form study groups early to enhance learning and group project outcomes.
• Stay aware of ethical considerations and privacy concerns as you encounter real-world data problems and case studies.