Welcome to BANA 4080

Brad Boehmke


  • Phonetically: “Bem” + “Key”

  • Alternatives:

    • Dr. / Professor B
    • Brad

  • Contact:

    • Read Communication Expectations Canvas page first!
    • Email: boehmkbc@ucmail.uc.edu
    • Office: Lindhall 3412




Fun Fact: Golf Obsessed


Meet Your TA


👋 Eirlys Vo

  • Senior in Business Analytics
  • Has already taken this course
  • Passionate about helping you succeed
  • Great resource for coding questions, labs, and homework

📧 Email: vopq@mail.uc.edu
🕐 Office Hours: TBD


Important

Don’t hesitate to reach out — they’re here to support you!

Today’s Agenda


  • What is data mining and why does it matter?
  • Course overview, goals, & roadmap
  • AI & Tooling
  • Q&A

What is Data Mining

Data Mining is All Around Us

Organizations use data mining to drive decisions every day.

Real-World Examples:

  • 🛒 Kroger analyzes loyalty card data to personalize digital coupons.
  • 🎶 Spotify recommends music based on your listening history and those like you.
  • 🏥 Hospitals use patient data to predict readmission risks.
  • 🏈 NFL teams analyze player movement data to improve performance and strategy.
  • 📦 Amazon tracks browsing behavior to recommend products and optimize inventory.


Important

Every time you browse, click, buy, swipe, or stream — you’re generating data.

What Is Data Mining?

The process of uncovering meaningful patterns, trends, and relationships in large data sets

Why is it important?

  • 📈 Helps organizations make better decisions
  • 🔍 Reveals insights that would otherwise go unnoticed
  • 🤖 Powers personalization, prediction, and automation
  • 💰 Drives business value in nearly every industry


Important

Data mining turns raw information into actionable knowledge

Activity

Where Do You See Data Mining?

🤔 Think about your daily routine — when are you being “mined”?

Instructions:

  1. Form groups of 2–3 students
  2. Brainstorm at least 3 examples where you think data mining is happening in your life
  3. We’ll share a few examples as a class

💬 Look for clues in:

  • Shopping & entertainment
  • Health & fitness
  • Social media & tech
  • Education or travel

Please think about this for 5 minutes.

So What Does Data Mining Actually Look Like?

The process of turning raw, messy data into insight someone can act on

flowchart LR
  A[Raw Data] --> B[Clean & Prepare]
  B --> C[Explore & Visualize]
  C --> D[Model & Predict]
  D --> E[Insight & Action]

Every example you just brainstormed runs through this same pipeline — and this course walks you through it end-to-end.

Important

The first half of this course covers getting data ready. The second half covers learning from it.

The Challenge Ahead

Meet Taylor

Taylor is a college junior who just landed a summer internship at a marketing analytics firm.

Taylor has a solid foundation:

  • Business knowledge ✓
  • Statistical thinking ✓
  • Critical thinking ✓

Taylor knows how to think about data!

Taylor’s First Week on the Job

The manager drops three raw data files and says:

“We’re trying to understand what drives repeat purchases. Can you dig into this and pull together something useful by Friday?”

Taylor opens the files and freezes. 😰

What’s missing? The hands-on ability to take raw, messy data and turn it into insight someone can act on.


Important

That gap is exactly what this course closes.

Does this scenario sound familiar?

It’s your turn to experience this…

You’ll get three datasets:

  • 🧾 Customer Transactions (messy!)
  • 🛒 Product Information
  • 👥 Customer Demographics

Download the data from

https://tinyurl.com/retail-data

Your mission:

  1. Get this data into a form where you can start answering: “What drives repeat purchases?”
  2. Can you get some initial insights from the data?

Group Activity: Dig Into the Data

Work in groups of 2–3. Use any tools you have (Excel, Python, intuition) and try to answer:

  • 🛒 Which products have the highest repeat purchase rate?
  • 👥 Are certain types of customers buying these products more frequently?
  • 📅 Is there a time pattern — do repeat purchases cluster around certain days or weeks?
  • 🧹 What data quality issues did you run into? Missing values? Inconsistent formats?
  • 🔀 How did you connect information across the three files?

Please work on this for 15 minutes.

Important

Don’t worry about getting the “right” answer — focus on what’s hard about the process.

Debrief: What Did You Learn?

Let’s talk through what you found:

  • 🛒 Were you able to identify which products had the highest repeat purchase rate? What made it hard?
  • 👥 Did any customer segments stand out? How did you figure that out?
  • 📅 Did you find any time patterns — and how did you look for them?
  • 🧹 What data quality issues slowed you down?
  • 🔀 How did you connect the three files — and what would have helped?

Tip

If you couldn’t complete the task — or didn’t know where to start — that’s exactly the point. By the end of this course, this will feel straightforward.

Key Takeaways:

  • Real-world data is never clean or ready to analyze
  • Good data work starts with understanding the structure and quality of your data
  • This course will teach you to clean, explore, model, and communicate insights from messy data with Python

Tip

We’ll revisit this exact challenge at the end of the semester — and it’ll feel completely different.

Course Overview

Why This Matters for YOUR Career

Data is everywhere — but insight is rare.

No matter your major, this course gives you a competitive edge:

  • 📊 Business Analytics Students → Speak the language of data science teams
  • 📈 Marketing Majors → Understand customer behavior through data, not just theory
  • 💵 Finance Majors → Model risk, detect fraud, forecast performance with code
  • 👩‍💼 Management Majors → Lead data-driven decisions instead of following them
  • 🎯 All Majors → Collaborate effectively with technical teams


Important

Today’s business leaders are expected to be data-literate decision makers

What You’ll Learn in BANA 4080

flowchart LR
  subgraph DM[Data Mining]
    direction LR
    subgraph Data
    end
    subgraph Cleaning
    end
    subgraph Wrangling
    end
    subgraph EDA
    end
    subgraph Modeling
    end
    subgraph Interpretation
    end
  end
  A[Stakeholders] --> DM
  B[Organizational Requirements] --> DM
  DM --> Decisions --> Value
  Data --> Cleaning --> Wrangling --> EDA --> Modeling --> Interpretation
  Interpretation --> Data

By the end of this course, you’ll be able to:

  • Write basic Python code to work with data
  • Clean, wrangle, and analyze messy real-world datasets
  • Visualize insights clearly and effectively
  • Understand how various ML/AI models are used in organizations
  • Build simple ML/AI models for prediction and pattern discovery
  • Communicate data-driven findings to others

Important

This course is not about memorizing syntax — it’s about thinking with data

AI Reality Check

What About AI? Won’t It Do This for Me?

“Why do I need to learn coding when ChatGPT can just do it for me?”

It’s a fair question. Let’s talk about it honestly.

🤖 AI tools are incredible accelerators, but they’re not magic:

  • They don’t understand your business context
  • They can’t ask the right questions about your data
  • They sometimes just make stuff up
  • They’re only as good as your prompts and interpretation

AI Reality Check: It’s Like Autocorrect for Code!

Warning

Ever had your phone turn “on my way!” into “omg my weasel!”? 🦫

That’s exactly how AI coding tools work — they predict what comes next based on patterns they’ve seen.

Sometimes they nail it… sometimes you get digital weasels.

AI tools are assistants, not autopilots:

✅ AI can help you:

  • Write boilerplate code
  • Debug errors
  • Learn new syntax
  • Generate ideas

❌ AI cannot:

  • Understand YOUR data
  • Know YOUR business goals
  • Ask the right questions
  • Guarantee correct answers

How We’ll Use AI in This Course

You’ll learn to use AI tools as learning partners, not crutches:

✅ Smart AI Use:

  • Check your understanding
  • Help debug when stuck
  • Explain concepts differently
  • Generate practice examples
  • Always understand what the code does

❌ Avoid This:

  • Copy-paste without understanding
  • Skip the learning struggle
  • Rely on AI for everything
  • Submit AI code you can’t explain

Important

The future belongs to people who know how to collaborate with AI, not be replaced by it.

Course Roadmap & Learning Mindset

Learning to Code: A Reality Check

Let’s be honest — learning to code can be frustrating at first.

You might feel:

  • 😤 Confused by error messages
  • 🤯 Like everyone else “gets it” but you
  • 😮‍💨 Stuck on simple problems
  • 🙄 Like you’re just copying examples

This is normal. It’s expected.

Learning to Code = Learning a New Language

You’ll start by copying examples and Googling errors.

Over time, you’ll stop memorizing and start thinking in code.

This course is designed for beginners — we’ll get you there step by step!

Course Roadmap

Course Roadmap

Your journey through BANA 4080:

  1. Weeks 1–3: Python basics & Working with data
  2. Weeks 3–4: Data wrangling & Exploratory analysis
  3. Weeks 5-6: Data visualization & Efficient programming
  4. Week 7: Mid-term
  5. Weeks 8-12: ML & AI
  6. Weeks 13-14: Final project

Important

This course builds your skills step-by-step — like a training plan for thinking with data.

How You’ll Learn

Each week follows a consistent rhythm:

  • 🧠 Tuesday (Lecture): Learn concepts, explore examples, discuss ideas
  • 💻 Thursday (Lab): Practice coding, get hands-on, work with real data

Assessments include:

  • 📚 Weekly reading quizzes
  • 📝 Biweekly homework assignments
  • 💭 Discussion forums
  • 📊 Midterm and final project
  • ❌ No conventional tests!

Important

Expect to build something meaningful — not just learn theory.

Resources

Everything You Need Is in One of Two Spots


📍 Course Canvas Page

📘 Course Textbook

Book: https://bradleyboehmke.github.io/uc-bana-4080/

Step 1

Who has read through the “Start Here!” module?

Let’s hit on a few important items

Tools & Setup Preview

Why Learn to Code? 🤔


  • Coding = flexibility + power
  • Handle real-world data: big, messy, inconsistent
  • Automate repetitive tasks
  • Think algorithmically and analytically

Why Python? 🤔


  • Widely used
  • Easy-to-read syntax (great for beginners)
  • Massive ecosystem: pandas, numpy, matplotlib, scikit-learn
  • Community support: tutorials, libraries, AI tools
  • Most organizations are shifting toward Python as the primary language for their data science and engineering codebases

Important

Python is the most valuable tool in your analytics toolbox.

How You’ll Run Python: Google Colab

What is Colab?

  • 💻 Free cloud-based Python environment from Google
  • 🚫 No software installation needed to get started
  • ✅ Works in your browser – just click and code

Why Colab First?

  • Easy, consistent experience for everyone on Day 1
  • Allows us to focus on learning — not debugging installs
  • We’ll gradually move toward installing tools locally (e.g., Anaconda, Jupyter, VS Code)

Important

You’ll be up and coding on Day 1 — no setup headaches!

Next Steps

Your Learning Journey Starts Now

📖 What Next?

  1. Read the “Start Here!” module
  2. Start working through Module 1’s readings - Chapters 1-3
  3. Get up and running in Colab

🗓️ Thursday Lab:

  • Your first Python code
  • Working in Google Colab
  • Collaborative problem-solving

💡 Remember: We’re building skills step-by-step!

Q&A

Q&A 🙋‍♀️

  • Open floor for any questions regarding the course structure, expectations, or content.
  • Discussion on how this course aligns with your academic and career goals.
  • Or anything else…golf?