Applied Data Science with Venture Applications
IEOR 135/ 290-002

Instructor: Ikhlaq Sidhu
Department of Industrial Engineering & Operations Research

3 Units, Lecture and Lab:

Prerequisite: Interested students should have working knowledge of Python in advance of the class, and also should have completed a fundamental probability or statistics course.

Teaching Team:

  • GSI: Sana Iqbal:
  • Visiting Scholar: Alexander Fred-Ojala,
  • Kevin Bozhe Li,
  • Tensor Flow Lead: Nathan Cheng,
  • NLTK Lead: Sam Choi,
  • Blockchain: Ronen Kirsh,
  • Blockchain: Nadir Akhtar,

Office Hours: Tuesdays 11am – 12pm, Etcheverry Hall 4176B (Breakout room)


Course Description:

This course is designed primarily for upper-level undergraduate engineering and technical students. Graduate students at a mezzanine level can also take a co-located section of the course. The course material offers an understanding at the intersection of foundational math mathematical concepts and current computer science tools, with applications of real world problems.  Math concepts include filtering, prediction, classification, decision-making, Markov chains, LTI systems, spectral analysis, and frameworks for learning from data.  Computer science tools for this course include open source tools such as Python with Numpy, Scipy, Pandas, SQL, NLTK, Tensor Flow, and Spark.  The course includes a team based data application project.

The lectures present alternating and related topics between mathematical frameworks and the same concept within code examples. One goal is that students who understand math concepts can bring them to life with scalable CS tools.  And, students who are comfortable with computer software code can create systems by understanding selected, structured mathematical frameworks. This course is designed to be more applied than a traditional ML algorithms course as it includes a systems view and covers implementation concepts.

Applications of this course are broad.  They include industry sectors such as finance, health, engineering, transportation, energy, and many others.  The lab section of the course meets in parallel with the lecture.  In the lab, the first 4 weeks are used to generate a story and low-tech demo for a real-world project that performs actions on data, and the following 8 weeks will be an agile sprint, with a demonstration of working project code by the end of the class. The skill set learned in this class can be applied to a broad range of industry sectors such as finance, health, engineering, transportation, energy, and many others.


  • Handouts in class
  • General Information:
  • Github for Code and Slides:
  • Anaconda Python Environment on personal computer


Class attendance and participation are expected, and sign-ins for sessions are tracked.  Absences for unavoidable reasons should be preapproved whenever possible via an email to the GSI


  • Homework: 35%
  • Attendance: 20%
  • Low Tech Validated Solution 10%
  • Final Project Demo, depth: 35%


SCHEDULE (Subject to Change)

  • On a weekly basis, class sessions may start with a “meet a mentor” and/or “application model case study” section.*
  • All slides and notebook samples will be updated at this site:
Topic 1: Introduction: Overview of Frameworks for obtaining insights from data (Slides).  Python Review with Notebook
Code Anaconda, Python.  Setting up Anaconda EnvironmentPython Review BKHW
DUE HW 1 Assigned
 Project  Office Hours Session that week for Environment Set Up
Topic 2: Tools: NumPy with Notebook.
Theory: Prediction
Code NumPy BKHW including prediction example
DUE Bring 3 ideas to next class.  HW 1 Due
 Project  Form Teams
Topic 3: Tools: Data signals in Tables.  Slides: Pandas Overview
Theory: Data as a Signal with Correlation
Code Pandas BKHW.  Stock Market live download to Pandas, DataFrame, Quant trading algorithm, correlation exercise
 Project  Form Teams Part II
Topic 4: Theory: ML Overview Algorithm Comparison
Tools: SKL with Classification and Regression
Code SKL Classification and Regression Notebook
DUE HW 3 Due
 Project Validate and Adjust
Topic 5: Tools: Trees and Classification
Theory: Spectral Signals, LTI Fundamentals
Code Decision Tree BKHW, Example with Titanic Data set
DUE HW 4 Due
 Project Low Tech Demo and Validation Results
Topic 6: Theory: Classification, Loss, Reward, Logistic Regression, Regularization
Tools: Image Processing/Classification Toolset
Code BKHW: Logistic Classification, Image Fundamentals
 Project Agile sprint with reflection
Topic 7: Theory: TBD
Tools: Web Scraping
Code BKHW: Web scraping and web crawling
 Project Agile sprint with reflection
Topic 8: Theory: TBD, Word2Vec
Tools: NLTK, Natural Language Processing
 Project Agile sprint with reflection
Topic 9: Theory: Neural Networks and CNNs
Tools: Tensor Flow
Code BKHW: Cats and Dogs
 Project Agile sprint with reflection
Topic 10: Theory: Block Chain
Tools: Block Chain as a database
Code BKHW: TBD (probably Programming in Solidity)
 Project Agile sprint with reflection
Topic 11: Theory: Data and Control Systems
Tools: Spark
DUE HW10 Due
 Project Agile sprint with reflection
Topic 12: Project Presentations – Demo Day(s)
Code Presentation including running code and code samples
DUE   Includes preparation time in last week
 Project Final Presentations
  • To include,  if possible tool: Connecting Pandas to SQL for Long-term storage.  AWS / SQL / Parallelization.
  • Example application topics may include examples such as recommendation engines, digital mirror, customer journey, bloom filters, fuzzy join applications.