Introduction to data science fundamentals, techniques and applications; data collection, preparation, storage and querying; parametric models for data; models and methods for fitting, analysis, evaluation, and validation; dimensionality reduction, visualization; various learning methods, classifiers, clustering, data and text mining; applications in diverse domains such as business, medicine, social networks, computer vision; breadth knowledge on topics and hands-on experience through projects and computer assignments. STARS Syllabus

Prerequisites: (CS 101 or CS114 or CS 115) and (MATH 230 or MATH 255 or MATH 260) and (MATH 225 or MATH 241 or MATH 220)
Credits: 3

Course Management Systems: Moodle
Course Website: http://www.cs.bilkent.edu.tr/~ge461/2024Spring

Instructor Team

• S. Aksoy, C. Alkan, S. Arashloo, F. Can, E. Çiçek, T. Çukur, S. Dayanık, H. Dibeklioğlu, A. Dündar, İ. Körpeoğlu, C. Tekin, E. Tüzün
  
• Course Coordinator (contact point): S. Aksoy (saksoy AT cs.bilkent.edu.tr)

TAs

• TBD

Classroom and Hours

• Clasroom: EE-317
• Class hours:
  • Mon 13:30-15:20
  • Thu 08:30-10:20

Grading Policy

• Final: 40 %
• Projects: 60 %. Multiple computer/programming/exercise assignments of various sizes.
• There will be 5 projects. Each project is 12 %.

Attendance

• Attendance is mandatory. A student who misses more than 9 hours will fail the course automatically.

Exam

• TBD

Projects

• Multiple computer/programming/exercise assignments of various sizes.
• A project can be assigned earlier than the indicated date on the weekly plan.
• Projects can be individual or group based. Instructors will decide.
• Projects will be uploaded to Moodle.
• Programming languages like Python, Java, R or Matlab can be used in the projects.
• Gaining hands-on experience and experimenting will be important. Real world data sets can be used (economical/financial data sets, medical/biological data sets, image/video data sets, social network data sets, IT data sets, etc.).

Other

• Grades will be posted in SAPS.
• There is no mandatory textbook for the course.

Introduction; what is data science; data science applications. [Çiçek, Tüzün]
Topic Details: Introductory concepts in data science and applications. Overview of data science process.
Slides and Additional Material:
Topic Details: Software engineering applications.
Slides and Additional Material:ge_461_-_lecture_1_-_course_information_compressed.pdf
Project/Exercise-Problem-Set/Homework: None this week.
References:
Events:

Data science applications; data science pipeline. [Alkan, Dibeklioğlu]
Topic Details: Genomics applications.
Slides and Additional Material:
Topic Details: Computer vision applications.
Slides and Additional Material: ge461_applications_vision_2024s.pdf
Project/Exercise-Problem-Set/Homework: None this week.
References: "Big Data: Astronomical or Genomical?", Stephens et al., 2015
Events:

Data representation; preprocessing; preparation; crowdsourcing. [Arashloo, Çiçek]
Topic Details: Normalization, Noise Removal (Filtering), Anomaly Detection, Data Compression, Noise Removal (ICA).
Slides and Additional Material:2024_pre-processing.pdf
Topic Details: Crowdsourcing applications and usage in data science.
Slides and Additional Material:ge461-crowdsourcing.pdf
Project/Exercise-Problem-Set/Homework: None this week
Events:

Data collection; storage; querying; SQL, NoSQL; cloud; distributed storage and computing. [Körpeoğlu]
Topic Details: RDMBs, SQL; SQLite, Pandas; NoSQL; MapReduce and Hadoop; Spark.
Slides and Additional Material: Slides
Project/Exercise-Problem-Set/Homework:
References: SQLite Pandas MapReduce ApacheHadoop ApacheSpark
Events:

Basic models; parametric models; fitting. [S. Dayanık]
Topic Details: Exploratory data analysis, loess smoother, chi-squared test of independence, linear regression and least squares method, factors and dummy variables, all illustrated on Dodgers Advertising and Promotion case study with R, RStudio, and SQLite
Slides and Additional Material: Week05 Dodgers.zip
Project/Exercise-Problem-Set/Homework: Dodgers Project

• Include all variables and conduct a full regression analysis of the problem.
• Write a report for Dodgers management. Discuss your findings in plain English and support them with data analysis.
• Submit Rmd and html files in a single zip file to Moodle by 19:00 Saturday on March 16. Late submissions will not be accepted.
• Groups up to three people are fine. Name the zip file with the BilkentIDs of group members.
  

References: Posit (former RStudio) R SQLite R for Data Science Modern Data Science with R

• Thomas W. Miller, Modeling Techniques in Predictive Analytics With Python and R: A Guide to Data Science
• Baumer, Daniel, Kaplan and Horton, Modern Data Science with R, Second Edition
• Wickham, Cetinkaya-Rundel, and Grolemund, R for Data Science, Second Edition
  

Events:

Application to customer choice problems (conjoint analysis) [S. Dayanık]
Topic Details: Part worths, part importance, their estimations from product rankings with multiple regression
Slides: Conjoint Analysis and Market Simulation
Project/Exercise-Problem-Set/Homework:
References:

• B. K. Orme, Getting Started With Conjoint Analysis: Strategies for Product Design and Pricing Research
• Miller, Marketing Data Science: Modeling Techniques in Predictive Analytics With R and Python

Events: Spring Break (Mar 7-8)

Authorship problem, text analysis, and topic modeling [S. Dayanık]
Topic Details: New product design with market simulation to increase overall market share; who wrote the Federalists papers (identiciation of authorships by means of Bayesian classifiers, kNN)
Slides and Additional Material:
Project/Exercise-Problem-Set/Homework:
References:

• Hamilton, Jay, and Madison, The Federalist Papers
• Mosteller and Wallace, Applied Bayesian and Classical Inference
  

Events:

Dimensionality reduction; visualization. [Aksoy]
Topic Details: Feature reduction, feature selection, high-dimensional data visualization.
Slides and Additional Material:
Project/Exercise-Problem-Set/Homework:
References: Matlab: dimensionality reduction, Scikit-learn: decomposition, Scikit-learn: decomposition examples, Scikit-learn: manifold learning, t-SNE
Events:

Unsupervised learning, clustering. [Aksoy]
Topic Details: K-means clustering, mixture models, hierarchical clustering.
Slides and Additional Material:
Project/Exercise-Problem-Set/Homework:
References: Matlab: cluster analysis, Scikit-learn: clustering
Events:

Machine learning; supervised learning; classifiers; deep learning. [Dündar]
Topic Details: Bayesian decision theory, linear discriminants, introduction to neural networks, support vector machines, decision trees.
Slides and Additional Material:
Project/Exercise-Problem-Set/Homework:
References:
Events: Bilkent Day (April 3)

Ramadan Holiday

Machine learning; supervised learning; classifiers; deep learning. [Dibeklioğlu]
Topic Details: Activation functions, convolutional neural networks, recurrent architectures.
Slides and Additional Material:
Project/Exercise-Problem-Set/Homework:
References:
Events:

Machine learning in healthcare. [Çukur]
Topic Details: Healthcare analytics: diagnostics, medical imaging, in-patient care, hospital management, risk analytics, wearables. Deep learning architectures for medical applications;
Slides and Additional Material:
Project/Exercise-Problem-Set/Homework:
References: Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Ch. 11 and 14; Mead, Analog VLSI and Neural Systems, Ch. 4; Bishop, Pattern Recognition and Machine Learning, Ch. 5
Events: National Sovereignty and Children's Day (Apr 23)

Data mining; online data stream classification; applications. [Can]
Topic Details: Concept drift, ensemble-based classification, text mining.
Slides and Additional Material:
Project/Exercise-Problem-Set/Homework:
References:
Events: Labor and Solidarity Day (May 1)

Reinforcement learning; applications. [Tekin]
Topic Details: Applications of Reinforcement Learning, Markov Decision Processes, Value Iteration, Q Learning
Slides and Additional Material:
Project/Exercise-Problem-Set/Homework:
References:
Events:

To be used if needed

• Principles and Techniques of Data Science - Online
• Python for Data Analysis, by Wes McKinney
• Doing Data Science, by Cathy O’Neil and Rachel Schutt. O’Reilly. 2014.
• Data Science from Scratch, second edition, O'Reilly, 2019.
• Python Data Science Handbook, O'Reilly, 2016.
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly, 2017.
• Machine Learning: a Probabilistic Perspective
• An Introduction to Statistical Learning, R, Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
• Pattern Recognition and Machine Learning, Christopher Bishop
• Neural Networks and Learning Machines
• Introduction to Data Science - Data Analysis and Prediction Algorithms with R, Rafael A. Irizarry. Online Book.
• Mining Massive Datasets, third edition, Ullman et al., 2020.

• CS194, Introduction to Data Science, Berkeley
• Data 8: Introduction to Data Science, Berkeley
• Data 100: Principles and Techniques of Data Science, Berkeley
• CS24200, Introduction to Data Science, Purdue
• Data Science 101, Stanford
• CS109, Data Science, Harvard
• Introduction to Data Science I, Maryland
• Introduction to Data Science II, Maryland
• Introduction to Data Science, WSU
• An Overview of Data Science, Oxford
• Applied Data Science, Cambridge
• Data Analysis, Delft
• Programming and Data Science for 99 Percent, Delft
• Foundations of Data Science, Wisconsin
• Introduction to Machine Learning, Bilkent
• Neural Networks, Bilkent
• Statistical Learning and Data Analytics, Bilkent
• Applied Data Analysis, Bikent
• Machine Learning, Bilkent
• Pattern Recognition, Bilkent
• CS1951A Data Science, Brown
• CMU15-388/688 Practical Data Science, CMU
• Introduction to Data Science, Russia
• LIS690, Introduction to Data Science, Kentucky

• Pandas: Python Data Analysis Library
• Machine Learning on AWS
• Apache Spark