snakewayne/2019.m2.bigdata

Big Data Analysis 2019-20 Module 2

Announcements

• Song Lu from China Scope will give a guest lecture on 12/12/2019.
• Homework 2 posted and will be due at 11:59pm 12/22/2019.
• Professor Tianyu Zhang will give a guest lecture on 12/5/2019.
• Homework 1 posted and will be due at 11:59pm 12/1/2019.

Lectures:

• 01 (2019/11/14): Course Overview

  • lecture_1_notes

• 02 (2019/11/18): Data Visualization

  • Reading Materials: harvard notes, useful suggestions

• 03 (2019/11/21): Quantitative Trading as a Big Data Application

  • lecture_3_notes

• 04 (2019/11/25): NoSQL Databases

  • lecture_4_notes

• 05 (2019/11/28): Hadoop

  • lecture_5_notes

• 06 (2019/12/2): Linear Regression

  • lecture_6_notes

• 07 (2019/12/5): Guest Lecture

• 08 (2019/12/9): Logistic Regression

  • lecture_8_notes
  • Reading Materials: From Professor Hung-yi Lee

• 09 (2019/12/12): Guest Lecture

  • lecture_9_notes

• 10 (2019/12/19): Review session and project guideline

  • lecture_10_notes

• 11 (2019/12/23): Midterm

• 12 (2019/12/26): Clustering and Classification

  • lecture_12_notes
  • Reading Materials: From Professor Andrew Ng
    From Professor Hung-yi Lee

• 13 (2019/12/30): Factor Investing

  • lecture_13_notes

• 14 (2020/1/2): Factor Investing Continued

  • lecture_13_notes
  • Reading Materials:Comparing Cross-Section and Time-Series Factor Models

• 15 (2020/1/3): Big Data Management

  • Short Video from Dr. Lei Xu, Data Scientist of Audaque

• 16 (2019/12/30): Practical Issues of Factor Investing

  • lecture_16_notes

Homework:

• hw #0 Create your github repository (public) as www.github.com/YOUR_ID/PHBS_BigData_2019.

• hw #1 homework_1

• hw #2 homework_2
  climate_change_1, climate_change_2

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Syllabus

Classes:

• Lectures: Monday & Thursday 1:30 – 3:20 PM
• Location: PHBS Building, Room 335 (changed from 323)
• Office Hour: Room 649, Tuesday 9-11 AM

Teaching Assistance: Ming Shao

• Email: [email protected]
• Location: PHBS Building, Room 213
• Office Hour: Thursday 9 - 11 AM

Textbooks and Reading Materials

• No offcial textbook, use the following reference materils
• http://cs229.stanford.edu/syllabus.html
• http://cs109.github.io/2015/index.html
• https://web.stanford.edu/class/cs102/index.htm
• Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today, 2016, by Berklin and Swedroe
• Pattern Recognition and Machine Learning, by Bishop (free download)

Assessment / Grading Details

• Attendance 5%, Assignments 20%, Midterm 35%, Final Project 40%