Berkeley Syllabus

Applied Data Science with Venture Applications
IEOR 135/ 290

Instructors: Ikhlaq Sidhu & Alexander Fred-Ojala
Department of Industrial Engineering & Operations Research

3 Units, Lecture and Lab

Link to current schedule of topics and homeworks


Prerequisites: 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:

  • Ikhlaq Sidhu, IEOR, (Instructor)
  • Alexander Fred-Ojala, (Instructor)
  • Tanya Piplani, (GSI)
  • Srikar Varanasi, (GSI)
  • Asef Ali, (Data-X Lab)

Extended Team:

  • Sumayah Rahman,
  • Sana Iqbal,
  • Kevin Bozhe Li,

Office Hours:

Tanya / Srikar (HW + project related): Wed 10:30-11:30AM, 205 South hall.
Alex (Project + Tech Setup):  Fri 3.30-4.30PM, SCET (Cali Memorial Stadium #122, Gate 2)
Ikhlaq Sidhu: by appointment via Melissa Glass,


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, entropy as part of information theory, 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.

Find our amazing projects from previous semesters here.



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

Grading: (Required to be taken on Letter Grade only)
The class will be graded according to the categories below. At the end of the class there will be a poster presentation + live demo during reading week where invited judges will provide assessment of each project.

  • Homework: 40%
  • Quizzes: 10%
  • Low Tech Validated Solution (Demo + MVP): 20%
  • Final Project + Write up + Code Review: 30%

Based on our previous experience in the course, we have decided to use the following percentile thresholds for the final grading. We plan to award A (top 30%), A- (next 30%), B+ (next 25%) and case by case grading for the rest. We reserve the right to increase or decrease these thresholds based on the performance of the class.

Piazza (Spring 2019)



  • 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
Theory: Overview of Frameworks for obtaining insights from data (Slides).
Tools: Python Review
Code 1. Introduction to GitHub
2. Setting up Anaconda Environment
3. Coding with Python Review
 Project Office Hours Session that week for Environment Set Up
Topic 2: Tools: Linear Regression, Data as a Signal with Correlation
Reading  Text Book Chapter 2 | Page 33 -45
 Project Bring three ideas to the class.
Topic 3: Theory: Numpy
  1. Coding with Numpy
  2. Coding with Pandas
  3. Coding with Matplotlib
Reading DataCamp, tutorialpoint, Text Book Chapter 1 | Page 3 -13
 Project Share ideas and finalize projects
Topic 4: Theory: Classification and Logistic Regression
Code Coding with Pandas, Matplotlib
Reading Text Book Chapter 3| Page 81 -95
 Project Develop insightful story and brainstorm solutions


Topic 5: Theory: Correlation
Reading Correlation Reading
 Project Team break out discussions


Topic 6: Theory: Prediction & Intro to Skikit-Learn
Code Coding with Skikit-Learn
Reading Prediction Slides


Topic 7: Theory: Matplotlib / Data Visualization
Code Coding with Matplotlib
Topic 8: Theory: Low Tech Demo Presentations
 Project Low Tech Demo Presentations


Topic 9: Theory: Classification & Prediction
Code Reference Titanic Notebook


Topic 10: Theory: Machine Learning & Cross Validation
Reading Machine Learning Reading


Topic 10: Theory: Decision Trees, Information Theory, Random Forest
Reading Text Book Chapter 6, Chapter 7 , Slides
Topic 11: Tools: Web Scraping  & Web Crawling
Code Web Scraping Notebook  , Breakout
Reading  Text Book Chapter 4| Page 105 – 110


Topic 12: Theory:
1. Introduction to Natural Language Processing – NLTK overview and Word2vec
2. Sentiment Analysis
Tools: NLTK, Gensim, Tensorflow
Code Coding with NLTK, Gensim, Tensorflow
Reading Links    , Slides
 Project Agile sprint with reflection


Topic 13: Theory: Polynomial Regression, Bias Variance Tradeoff, Regularization
Code  Regularization Notebook
Reading Slides
Topic 14: Theory: Introduction to Neural Networks- ANN, CNN, RNN
Tools:  Tensorflow
Code Coding with Tensorflow for image classification
Reading Text Book Chapter 10| Page 256-272, Chapter 13| Page 357-359 . , Slides
 Project Low Tech Demo and Validation Results


Topic 15: Theory:
1. Introduction to database
2. Introduction to SQL
3.  Introduction to Block Chain as a database
4. Big Data Analysis with Spark
Tools: SQL libraries in python, Solidity
Code Coding with python for SQL  and  Spark
Reading Text Book
 Project Agile sprint with reflection
Topic 16: Theory: Spectral Signals, LTI -Fundamentals and Applications
Tools: Temporal and Spatial Signal processing
Code Coding with python for signal processing
Reading  Text Book
 Project  Agile sprint with reflection
Topic 17: Theory: Reinforcement Learning primer
Code TBD
Reading Text Book Chapter 16| Page 443-450
Project Agile sprint with reflection
Topic 18: 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.