Skip to content

strangemachinery/DAT_SF_18

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

19 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DAT_SF_18

Course materials for General Assembly's Data Science course in San Francisco (10/27/15 - 1/17/16).

Logistics

  • Dates: 10/27/15 - 1/17/16, Tuesday - Thursday 6:30-9:30
  • Holidays (no class): 11/26 (Thanksgiving), 12/21 - 1/1 (winter break)
  • Location: 225 Bush Street, Classroom 4
  • Instructor: Francesco Mosconi
  • Expert-in-Residence:
 Dylan Hercher

Course Description

Foundational course in data science, including machine learning theory, case studies and real-world examples, introduction to various modeling techniques, and other tools to make predictions and decisions about data. Students will gain practical computational experience by running machine learning algorithms and learning how to choose the best and most representative data models to make predictions. Students will be using Python throughout this course.

Required Setup

Completion Requirements

In order to receive a General Assembly Certificate in Data Science, upon completion of the course, students must:

  • Complete and submit 80% of all course assignments (homework, homework reviews, labs, quizzes). Students who miss more than 20% of assignments will not be eligible for the course certificate.
  • Complete and subimt the course midterm test.
  • Complete and submit the course final project, completing all functional and technical requirements on the project rubric, including delivering a presentation.

Assignments, milestones and feedback throughout the course are designed to prepare students to deliver a quality course project.

Course Outline

The weekly schedules for lecture content, lab content, and homework assignments are subject to change according to the needs & preferences of the class.

Course Schedule

Week Tuesday Thursday
1 10/27: Introduction to Data Science, Git setup 10/29: Python & Linear Algebra review
2 11/03: Data 11/05: Introduction to Machine learning

Homework Schedule

HW Topics Dataset Assigned Due Review Due
1 Github setup 7/29 8/3 8/5

Final Project Milestones

Office Hours

Instructor Times Available method
Dylan
Francesco Tuesday & Thursday slack (quickest response) or hangouts by appointment

Slack

You've all been invited to use Slack for chat during class and the day. Please consider this the primary way to contact other students. Dylan will be in Slack during class to handle questions. All instructors will be available on Slack during office hours (listed above).

Resources

Working in the terminal

Statistical Learning Theory

Algorithms

Python

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages