Syllabus

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-9780073380186

    • 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)
pdf link Assignment-1 pdf link
Week 2 6-Feb Business Model Canvas,  Customer Validation, Linear Classifiers, Intellectual Property, Technology Ventures
Guest Lecture (45 mins) – Orin Herskowitz
Reading

pdf link (Guest Lecture) pdf link Assignment-2 pdf link
Week 3 13-Feb Minimum Viable Product Development, Technology behind Startups, Scaling Startup Technology
Guest Lecture (1 hr) – Hrishi Dixit
Reading

pdf link(Guest Lecture) pdf link Initial Pitch Presentation Guidelines pdf link
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  pdf link  Assignment3 pdf link
Week 6 6-Mar  Big Data and Business, Classification techniques in product development and customer developmentReading :

 pdf link
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  pdf link
Week 10 3-Apr     Mid/Second Pitch Day Judges : Amol Sarva, David Lerner, Samir Hosri, Ted Shergalis, Wim Sweldens  pdf link Second PitchGuidelinespdf link
Week 11 10-Apr  Data Science and Business Applications, Scaling Startup TechnologyReadingPaper 1, Paper 2, Article 3  pdf link (Guest Lecture – Luis Sanz) pdf link
Week 12 17-Apr Data to Clusters, Guest Lectures (Paul Tumpowsky, Shari Coulter Ford)   pdf link (Guest Lecture – Shari Coulter Ford )pdf link  Assignment4 pdf link
Week 13 24-Apr  Technology Choices, Legal Aspects of a Startup (Guest Lecture - Jane Jablons)    pdf link (Guest  Jane Jablons) pdf link
Week 14 1-May  Recap of the semester  pdf link
Week 15 7-May   DSTE conference and Pitch Day (3.5 hour event – Tuesday)  Final Pitch Guidelinespdf link
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 pdf linkAmazon EC2 : Using AWS to host your web app – Morgan Ulinski pdf link
Week 2 March 1 Introduction to Javascript – Samuel Messing - pdf link
Week 3  Using Web Frameworks – Samuel Messing –  pdf linkGithub link - https://github.com/smessing/intro-rails-lecture

 

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
Stage 2 (5 weeks – Feb 4 – March 10) Minimum Viable Product development, Quantifying customer feedback with
classification and clustering techniques
Stage 3 (2 weeks – March 11 – March 31) Agile development, Data analysis of feature surveys, Sequential prediction algorithms
(costs, revenue, traction)
Stage 4 (2 weeks – April 1 – April 29) Launching the product, Data driven marketing techniques, Data science in A/B
testing
Stage 5 (2 weeks – April 1 – May 5) Try to raise funds with VC network provided in the class