CS70: Discrete mathematics and Probability Theory, fall 2013. Previous sites: ml, instructor and Lecture, instructor: Umesh vazirani, lecture: tuth 11:00 am-12:30 pm, wheeler Auditorium. Office: 671 Soda hall, office hours: tu 1:30 pm-2:30. Announcements: Dates to remember: Written homeworks are posted Mondays and are due the following Monday at 5pm. Online homeworks are post Sundays and are due the following Friday at 1pm. Midterm 1 review session: Friday sept 27th 5-7pm in 2050 vlsb. Midterm 1: Oct 1st (in class). Midterm 2: nov 5th (in class).
Homework 5 Supplementary materiel: Parallelized Stochastic Gradient Descent Class 8: Objectives: guest speaker Chris Volinsky discusses data mining and machine learning research at. Applications include improving the understanding of urban environments, and discovering social communication patterns. Additionally Chris discussed his experiences building recommender systems to win the 1m netflix Prize. Recommender Systems and the netflix Prize shaping Cities of the future using Mobile data Assessment 2 Class 9: Objectives: guest speaker Kristen Sosulski discuesses data visualization, presenting techniques for telling stories with data, conveying a particular message to an intended audience. She talked about general considerations when constructing visualizations, and gave concrete examples for creating visualizations using matplotlib in python. Hands on with Data visualization in Python Supplementary materiel: Interactive data visualization for the web mbostock: Awesome visualizations using.js.js tutorials Stanford Data visualization course notes Class 10: Objectives: guest speaker Troy raeder discusses online advertising and using data science for targeting online display. Online targeted Display advertising for Prospecting Related Articles: a very Short History of Data Science a taxonomy of Data Science The future of Informatics Three sexy skills of Data geeks The Unreasonable Effectiveness of Data more data beats Better Algorithms - or does It? Mining of Massive datasets Code a facebook app in 20 Minutes with Python Probability and Statistics cookbook meet the new Boss: Big Data visual Python Tutor The command Line Crash course learn Linux the hard way.
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Assignment 1 Data Science api list Supplementary material: building restful Web Services in Python Heroku- cloud Application Deployment Made easy Class 5: Objectives: Big data! Learning about just what big data is, scales of data and what problems this presents. What statement are some techniques for dealing with these challenges. Distributed file systems, hadoop and MapReduce. Discussing implementation of distributed MapReduce tasks using Hadoop Streaming. Big Data, hadoop and MapReduce homework 4 Data center Architecture mapReduce supplementary material: Textbook: Mining Massive datasets Textbook: Data-Intensive text Processing with MapReduce Cloudera big Data Glossery ibm: What is Big Data? Cern generating a petabyte of Data each Second Facebook ingests 500 Terabytes every day class 6: Objectives: guest speaker Dr Jason davis discusses experiences building and deploying data-driven systems.
Topics include collecting data, web metrics, statistics, ab testing, and the precision / recall tradeoff. Big Data Science at Etsy Planning, running, and Analyzing Controlled Experiments on the web (part1) (part 2) (part 3) Online controlled Experiments: Introduction, learnings, and Humbling Statistics Class 7: Objectives: Data mining, and predictive modeling. Information gain and decision tree models. Linear models, support vector machines and logistic regression. Probability estimation using trees and linear models. Model evaluation, accuracy, ranking metrics, holdout testing. Model complexity and overfitting.
Class 3: Objectives: to understand database uses and technology. To learn when databases are used and why. How do databases represent the Entity-relationship diagram? Covering the basic select queries and all that is needed to perform rich analytical queries. Relational Databases and sql, using Mysql Workbench, homework.
Supplementary material: hbr: Getting Control of Big Data. A regular Expression Reference, big Opportunities for Big Data Experts. Online regular Expression Testing, a visual Explanation of sql joins, class 4: Objectives: building a basic understanding of predictive modeling, the distinction between model training, evaluation, and use. Examples of target variables and independent variables. Web apis and services. Http and restful technology. Using web services in programs in order to gather diverse data and perform interesting computations.
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A python Cheat Sheet, data Is Useless Without the skills to Analyze. Class 2: Objectives: to understand data, what it represents and japanese how it is organized. We discuss the primitive components paperless of data and some data structures used for collecting these individual elements. We then talk about how data is represented, discussing common data-representation schemes. After talking about data structures, we talk about unstructured and semi-structured data: text, web logs, and html. We discuss regular expressions, their syntax, their use for filtering and matching text, their usefulness in extracting data and replacing data in text. Details of Data: Components and Collections. E-commerce er diagram, representing Data: csv, xml, json yaml. Regular Expressions, class 2 Lab Exercise, homework.
Data Science libraries for Windows. Nyc data Science meetups, class 1: b, objectives: to go over the topics covered in this course, the philosophy and course policies. We motivate the importance of this course with a detailed example, web page classification, going over many the steps and choices required to build and deploy a data-driven predictive system in the wild. This lecture conclude with a lab designed to get every student set up with python and the libraries that we will use throughout this course. Course learning Objectives, supplementary Programming Excercises. Homework 1, an Example Predictive system, supplementary material: Data Scientist: The sexiest Job of the 21st Century.
This class is an introduction to the practice of data science. The student will leave the class with a broad set of practical data analytic skills based on building real analytic applications on real data. These skills include accessing and transferring data, applying various analytical report frameworks, applying methods from machine learning and data mining, conducting large-scale rigorous evaluations with business goals in mind, and the understanding, visualization, and presentation of results. The student will have experience processing big data, the latest buzz concept in a field awash with buzz. Specifically, the student will be able to analyze data that are too big to fit in the computers memory, and therefore thwart many standard analytical tools. The student will have experience with unstructured data, for example processing text for applications such as sentiment analysis of user-generated content on the web. Syllabus, post Sandy Class Schedule.
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Practical Data Science, fall 2012, data is the gpa new oil. Data is a new class of economic asset. Those were the conclusions of the reports issued by the world Economic Forum at davos in January 2011 and January 2012. Research published in 2011 by mit economists shows that companies adopting data-driven decision-making achieved significant productivity gains over other firms. In industry, the hottest job these days is the data Scientist. Data scientists combine technical and statistical skills, analytical thinking, and business acumen. One of the complaints about the data scientists trained in computer science departments is that theyre just technical, understanding algorithms well, but lacking important skills in problem formulation, evaluation, and analysis generally. On the other hand, those trained in business schools tend to have underdeveloped technical skills. This course will cover all of these aspects of being a data scientist.