Professor : Sameer Maskey, PhD
Teaching Asst : Morgan Ulinski (Computer Science)
Teaching Asst. : Jigar Patel (Business School)
Teaching Asst. : Samuel Messing (Computer Science)
Time : 4:10 – 6pm, Wednesdays
Location : 313 Fayerweather (all remaining classes)
Office Hours : 2-4pm Wednesdays, 457 CSB Building
Mentors/Advisors : 21 Mentors - http://www.cs.columbia.edu/~smaskey/dste/mentors-advisors/
Course Information
This course will pair up MBA students from Columbia Business School with Master’s/PhD students from Computer Science department to form teams of two (or more) who will be guided through an entrepreneurial experience of building a technology startup. The course will be very hands on! The course will also have a team of 21 Industry Advisors/Mentors (CEOs, CTOs and VC Partners of various firms) who will engage with students to help them convert their idea into a sustainable technology business.
Data Science is an emerging interdisciplinary field across statistics, computer science and business. The course will not only focus on theoretical aspects of data sciences but also on applying them in building products and improving business processes. Student teams (composed of CS/Engineering and Business students) will use data driven methods to test feasibility of the idea/innovation, build the product, develop customers, study sales channels and try to raise capital during the span of 4 months. Industry mentors will critique the student teams and their ideas through various stages of the startup implementation addressing such questions related to feasibility, market attractiveness, customer acquisition, metrics, launch strategy and more. The students will be able to interact with CEOs for business mentorship, CTOs for technical mentorship and VC firm partners for advice on the capital raising process.
Books
- Technology Ventures: From Idea to Enterprise, 3rd edition By Thomas Byers, Richard Dorf, Andrew Nelson
Other Recommended readings:
- The Startup Owner’s Manual: The Step-By-Step Guide for Building a Great Company By Steve Blank and Bob Dorf
Syllabus
| Week | Date | Topic | Slides | Assignment | Notes | |
| Week 1 | 30-Jan | Data, Scoring Methods, Data Science and Tech Startup Evaluating Startup Ideas – Guest Lecture (1 hr) – Wim Sweldens (Wiki) |
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Assignment-1 |
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| Week 2 | 6-Feb | Business Model Canvas, Customer Validation, Linear Classifiers, Intellectual Property, Technology Ventures Guest Lecture (45 mins) – Orin Herskowitz Reading
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(Guest Lecture) |
Assignment-2 |
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| Week 3 | 13-Feb | Minimum Viable Product Development, Technology behind Startups, Scaling Startup Technology Guest Lecture (1 hr) – Hrishi Dixit Reading
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(Guest Lecture) |
Initial Pitch Presentation Guidelines |
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| Week 4 | 20-Feb | First Pitch DayJudges : Amol Sarva, Ben Siscovick, Dave Lerner, Paul Tumpowsky | ||||
| Week 5 | 27-Feb | Fisher’s Linear Discriminant, VCs and StartupsGuest Lecture – Charlie O Donnell | |
Assignment3 |
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| Week 6 | 6-Mar | Big Data and Business, Classification techniques in product development and customer developmentReading :
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| Week 7 | 13-Mar | No Class (Business School 2nd Year student Break) | No Lecture | |||
| Week 8 | 20-Mar | Spring Break | No Lecture | |||
| Week 9 | 27-Mar | Student Board Meeting | |
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| Week 10 | 3-Apr | Mid/Second Pitch Day Judges : Amol Sarva, David Lerner, Samir Hosri, Ted Shergalis, Wim Sweldens | |
Second PitchGuidelines |
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| Week 11 | 10-Apr | Data Science and Business Applications, Scaling Startup TechnologyReadingPaper 1, Paper 2, Article 3 | (Guest Lecture – Luis Sanz) |
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| Week 12 | 17-Apr | Data to Clusters, Guest Lectures (Paul Tumpowsky, Shari Coulter Ford) | (Guest Lecture – Shari Coulter Ford ) |
Assignment4 |
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| Week 13 | 24-Apr | Technology Choices, Legal Aspects of a Startup (Guest Lecture - Jane Jablons) | (Guest Jane Jablons) |
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| Week 14 | 1-May | Recap of the semester | |
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| Week 15 | 7-May | DSTE conference and Pitch Day (3.5 hour event – Tuesday) | Final Pitch Guidelines |
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| Week 16 | 14-May | Final Exam Week (No class) |
Extra Lecture by TAs (Samuel Messing & Morgan Ulinski) – Intro to Web Programming for Business :
| Week | Date | Topics/Slides | |||
| Week1 | Feb 22 | Life of a Web Request – Samuel Messing Amazon EC2 : Using AWS to host your web app – Morgan Ulinski |
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| Week 2 | March 1 | Introduction to Javascript – Samuel Messing - |
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| Week 3 | Using Web Frameworks – Samuel Messing – Github link - https://github.com/smessing/ |
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Course Stages :
| Stage | Date | Topics | |||
| Stage 1 | (2 weeks – Jan 30 – March Feb 13) | Probabilistic models for your business canvas, Problem definition, Data collection, Data science methods for testing your hypothesis |
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| Stage 2 | (5 weeks – Feb 4 – March 10) | Minimum Viable Product development, Quantifying customer feedback with classification and clustering techniques |
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| Stage 3 | (2 weeks – March 11 – March 31) | Agile development, Data analysis of feature surveys, Sequential prediction algorithms (costs, revenue, traction) |
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| Stage 4 | (2 weeks – April 1 – April 29) | Launching the product, Data driven marketing techniques, Data science in A/B testing |
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| Stage 5 | (2 weeks – April 1 – May 5) | Try to raise funds with VC network provided in the class | |||
