# Introduction to Data Science: CptS Syllabus First Offering: Fall 2015

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1 Course Information Introduction to Data Science: CptS Syllabus First Offering: Fall 2015 Credit Hours: 3 Semester: Fall 2015 Meeting times and location: MWF, 12:10 13:00, Sloan 163 Course website: Relevant course material, including this syllabus, and course related resources will be made available at the course website. Additionally, the online portal OSBLE ( will be used for posting lecture material, assignments, announcements, etc and for handling submissions. Instructor Information Assefaw Gebremedhin Office: EME 59 assefaw AT eecs DOT wsu DOT edu Homepage: Office Hours: Tuesdays 2:00 3:00pm, or by appointment. Course Description Data Science is the study of the generalizable extraction of knowledge from data. Being a data scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and other branches of computer science along with a good understanding of the craft of problem formulation to engineer effective solutions. This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving problems. To make the learning contextual, real datasets from a variety of disciplines will be used. Learning Outcomes At the conclusion of the course, students should be able to: Describe what Data Science is and the skill sets needed to be a data scientist. Explain in basic terms what Statistical Inference means. Identify probability distributions commonly used as foundations for statistical modeling. Fit a model to data. Use R to carry out basic statistical modeling and analysis. Explain the significance of exploratory data analysis (EDA) in data science. Apply basic tools (plots, graphs, summary statistics) to carry out EDA. Describe the Data Science Process and how its components interact. Use APIs and other tools to scrap the Web and collect data. Apply EDA and the Data Science process in a case study. 1

3 Topics and course outline: 1. Introduction: What is Data Science? - Big Data and Data Science hype and getting past the hype - Why now? Datafication - Current landscape of perspectives - Skill sets needed 2. Statistical Inference - Populations and samples - Statistical modeling, probability distributions, fitting a model - Intro to R 3. Exploratory Data Analysis and the Data Science Process - Basic tools (plots, graphs and summary statistics) of EDA - Philosophy of EDA - The Data Science Process - Case Study: RealDirect (online real estate firm) 4. Three Basic Machine Learning Algorithms - Linear Regression - k-nearest Neighbors (k-nn) - k-means 5. One More Machine Learning Algorithm and Usage in Applications - Motivating application: Filtering Spam - Why Linear Regression and k-nn are poor choices for Filtering Spam - Naive Bayes and why it works for Filtering Spam - Data Wrangling: APIs and other tools for scrapping the Web 6. Feature Generation and Feature Selection (Extracting Meaning From Data) - Motivating application: user (customer) retention - Feature Generation (brainstorming, role of domain expertise, and place for imagination) - Feature Selection algorithms Filters; Wrappers; Decision Trees; Random Forests 7. Recommendation Systems: Building a User-Facing Data Product - Algorithmic ingredients of a Recommendation Engine - Dimensionality Reduction - Singular Value Decomposition - Principal Component Analysis - Exercise: build your own recommendation system 8. Mining Social-Network Graphs - Social networks as graphs - Clustering of graphs - Direct discovery of communities in graphs - Partitioning of graphs - Neighborhood properties in graphs 9. Data Visualization - Basic principles, ideas and tools for data visualization 3

4 - Examples of inspiring (industry) projects - Exercise: create your own visualization of a complex dataset 10. Data Science and Ethical Issues - Discussions on privacy, security, ethics - A look back at Data Science - Next-generation data scientists Books The following book will be used as a textbook and primary resource to guide the discussions, but will be heavily supplemented with lecture notes and reading assignments from other sources. The lecture notes and reading material will be posted on the course s website or the associated OSBLE page as the course proceeds. Cathy O Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline. O Reilly Additional references and books related to the course: Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of Massive Datasets. v2.1, Cambridge University Press (free online) Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN Foster Provost and Tom Fawcett. Data Science for Business: What You Need to Know about Data Mining and Data-analytic Thinking. ISBN Trevor Hastie, Robert Tibshirani and Jerome Friedman. Elements of Statistical Learning, Second Edition. ISBN (free online) Avrim Blum, John Hopcroft and Ravindran Kannan. Foundations of Data Science. (Note: this is a book currently being written by the three authors. The authors have made the first draft of their notes for the book available online. The material is intended for a modern theoretical course in computer science.) Mohammed J. Zaki and Wagner Miera Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press Jiawei Han, Micheline Kamber and Jian Pei. Data Mining: Concepts and Techniques, Third Edition. ISBN Policies Missing or late work Submissions will be handled via the OSBLE page of the course. Students are expected to submit assignments by the specified due date and time. Assignments turned in up to 48 hours late will be accepted with a 10% grade penalty per 24 hours late. Except by prior arrangement, missing or work late by more than 48 hours will be counted as a zero. 4

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