OMIS-482: Predictive Business Analytics with Machine Learning

Class Introduction:

Welcome to a transformative learning adventure designed to unveil the intricacies of predictive modeling analytics. This website stands as your gateway to a world where data speaks volumes, patterns emerge from chaos, and predictions become possible. We are about to embark on a journey that will not only educate but also inspire, as we cover the major topics that form the backbone of predictive modeling analytics.

This course, meticulously structured and rich in content, is entirely based on the powerful R programming language, a tool embraced by statisticians, data scientists, and researchers worldwide. Fear not if these terms seem unfamiliar or if programming sounds like a foreign language; this class is crafted with you in mind. We start from the basics, building a strong foundation that will support your learning journey, ensuring that every step you take is one of confidence and understanding.

We are excited to announce that we worked hard to create a custom built course that leverages state of the art technologies and tools. Thanks to them we can ensure that you will have continuous access to all the course materials from any device, anytime, anywhere. This eliminates the need for software installations on your personal devices, making your learning experience smooth and hassle-free. For those of you interested in exploring further, R and RStudio, an IDE part of Posit, are free and open-source software that you can install and use. They are a powerful tools that will enhance your coding experience and provide you with additional capabilities accessible during and after the semester ends.

As we navigate through the content, you will find that the website is not just a repository of knowledge, but also a companion in your learning. Interactive activities, real-world examples, intriguing challenges and hands-on exercises are interwoven throughout the semester, making the learning process not just educational, but also engaging and enjoyable. Yes, coding can be fun, and you are about to discover just how rewarding it can be.

This course is a celebration of learning, a space where curiosity is nurtured, and mistakes are embraced as stepping stones to mastery. We believe in the power of practice, and as the old adage goes, practice indeed makes perfect. The activities and challenges presented in this class are designed to provide you with ample opportunities to apply what you’ve learned, solidifying your understanding and enhancing your skills.

Diversity is the spice of life, and so is the case in learning. This course does not assume any previous knowledge of R programming or data analytics. Whether you are a complete beginner or have dabbled in data science before, you will find the content accessible, challenging, and most importantly, rewarding. Every concept is explained with clarity, every challenge is an opportunity to learn, and every success is a moment to celebrate.

We understand that the journey of learning coding and data analytics can be daunting, but you are not alone. This website is your companion, your guide, and your mentor. It is packed with all the materials you need to succeed, carefully designed and curated by your professor to ensure a comprehensive and enjoyable learning experience.

So, open your minds, ready your computers, and let’s dive into the fascinating world of predictive modeling analytics. Together, we will demystify the complexities of R programming, unravel the mysteries of data, and uncover the secrets of predictive modeling. Welcome to a journey of discovery, learning, and growth. Welcome to the world of data analytics. Let the adventure begin!

Learner Personas

Each one of us is unique! We celebrate that and acknowledged that in the creation of this book. The below personas were kept in mind when design and creating the content for this class. They reflect the diverse backgrounds, academic pursuits, motivations and career aspirations of business students, ensuring that the course content can be tailored to meet their specific needs and interests.

  1. Name: Mario
    • Age: 22
    • Background: Senior majoring in Business Administration. First-generation college student of Italian descent. Works full-time in a local supermarket to support himself and his family.
    • Learning Goals: Wants to understand predictive modeling to make data-driven decisions in business settings.
    • Challenges: Balancing a full-time job and academic responsibilities. Has basic Excel skills but is new to R programming.
    • Motivation: Aims to be the first in his family to graduate from college and aspires to manage his own business in the future.
  2. Name: Aisha
    • Background: Senior in Marketing. Indian-American, with a keen interest in digital marketing. Works part-time at a digital marketing agency.
    • Learning Goals: Wants to leverage data analytics for targeted marketing and customer segmentation.
    • Challenges: Has experience with social media analytics but is new to more advanced predictive modeling techniques.
    • Motivation: Eager to excel in digital marketing and bring innovative data-driven strategies to her workplace.
  3. Name: Shanice
    • Background: Shanice, a 22-year-old senior majoring in Finance. African-American, actively involved in the college’s investment club. Receives financial aid and works part-time in a bank.
    • Learning Goals: Aims to apply predictive modeling in financial analysis and investment strategies.
    • Challenges: Has a strong background in quantitative methods but limited experience in programming.
    • Motivation: Aspires to work in investment banking and believes that strong data analytics skills are crucial.
  4. Name: Taylor
    • Background: Taylor, a 22-year-old senior majoring in International Business, has a bicultural background, with parents who immigrated from Korea. She has a basic understanding of R programming.
    • Learning Goals: Taylor aims to better understand how data analytics can be applied in international business, particularly in diverse cultural contexts.
    • Challenges: Finding resources that apply data analytics in international scenarios can be challenging. Taylor is also looking to deepen their understanding of R programming.
    • Needs: Taylor is driven by a desire to forge their own path and make a name for themselves, separate from their family’s influence.
  5. Name: Serena
    • Background: Serena is a 22-year-old senior majoring in Marketing. Raised in a multicultural family, Serena has a unique worldview and is interested in consumer behavior. She has some prior experience with R from a previous internship.
    • Learning Goals: Serena aims to integrate data analytics into marketing strategies. She wants to solidify her R programming skills and understand how to analyze consumer data effectively.
    • Challenges: Balancing theory with practical application can sometimes be challenging for Serena. She is looking for a course that provides hands-on experience and real-world examples.
    • Motivations: Serena is driven by a desire to set a positive example for her younger siblings and to capitalize on the educational opportunities afforded to her.
  6. Name: Bryan
    • Background: Bryan is a 21-year-old junior majoring in Management Information Systems. He is the first in his family to attend college and he is working part-time to support his education.
    • Learning Goals: Bryan is keen to learn about technology and its applications in business. He sees this course as an opportunity to gain practical skills in R programming and predictive modeling.
    • Challenges: Balancing work, school, and personal life is a constant struggle for Bryan. Bryan needs a learning resource that is flexible and accessible at any time.
    • Motivations: Bryan is motivated by the desire to stand out in his field and secure a good job after graduation.

Class Rules

Your Work Should Be Your Work :

  • All assignments must be completed individually. Collaboration is encouraged for learning but not for submitting assignments.

  • You are allowed to use AI tools to assist in your learning. However, ensure you fully understand the concepts and processes AI helps you with. AI tools are for learning enhancement, not for doing your work for you.

  • AI tools are strictly prohibited in examinations and formally graded assignments. You should rely solely on your knowledge and understanding to complete them.

Responsible Use of Class Resources:

  • Use class resources solely for practicing and completing class assignments. Avoid using them for activities not related to this class.

  • R and RStudio are open-source, but the class resources leverage a paid service and we have a limited budget for this class.

  • Excessive or non-class related usage can lead to budget overruns. They will create frustrations and issue for everybody.

Monitoring and Support:

  • As your instructor, I will have access to your work. This will allow me to monitor your progress and provide help when needed.

  • Keep an open line of communication. I am here to help! If you’re struggling with the material or the technology, let me know sooner rather than later. Feel free to reach out via MS Teams or by booking an office hours appointment.

Respect and Integrity:

  • Be kind, respectful and polite.

  • Maintain a high level of academic integrity and professionalism for your peers, me and the resources provided.

  • Any form of dishonesty or disrespect will be addressed according to college policies.

By following these rules, you can ensure a productive, fair, and efficient learning environment for everyone in the class.

Focus on Learning:

My primary focus and concern are your learning. I truly care that all of you understand and master all the materials that we will cover this semester. There are different components that are included in your learning evaluation:

  • Weekly coding checkups: demonstration of understanding/mastering the weekly materials by completing custom designed coding checkups that will walk you through a semester long and complete data analytics project.

  • In-class participation: willingness to understanding/mastering by asking questions and/or demonstration of understanding/mastering by answering questions/solving activities during class time.

  • Teams participation: willingness to understanding/mastering by asking questions and/or demonstration of understanding/mastering by answering questions/solving activities on the MS Teams class channel.

  • Professionalism: attitude towards learning (e.g., being on time, staying engaged, paying attention, being respectful etc.)

  • Others: participation to other learning enhancement initiatives (e.g., challenges, completing surveys etc.)

A Powerful Testimony

If you didn’t buy in yet… this video is for you:

Class Details

Meeting Location Time
Week’s First Class Barsema Hall 333 Tuesday 9:30 - 10:45 A.M
Week’s Second Class Barsema Hall 333 Thursday 9:30 - 10:45 A.M

Office Hours

Instructor Booking URL
Dr. B &/or Diviya Book Here
Important

When booking office hours, double-check the name of the staff member you want to book the office hours with using the “Select Staff” drop down. Moreover, please keep in mind that the default office hours format is virtual (MS Teams meeting). If you prefer to meet in person, please check with us our in-person availability via Teams before booking.

Ok, great! This should be enough to start your journey. Looking forward to a great semester!

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