Course materials for General Assembly's Data Science course in San Francisco (7/29/15 - 10/14/15).

• Dates: 07/29 - 10/14, Monday - Wednesday 6:30-9:30
• Location: 225 Bush Street
• Instructor: Francesco Mosconi
• Expert-in-Residence:
 Justin Breucop

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.

• Own laptop
• Continuum Analytics Anaconda python
• Git & Github

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, earning a satisfactory grade.
• Complete and submit the course final project, earning a satisfactory grade by 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.

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

 | **UNIT 1**                                          | **DATA**

1 | | 7/29: Introduction to Data Science, Git setup 2 | 8/3: Data Format, Access & Transformation + Python review | 8/5: Cleaning and exploring data + Linear Algebra review | | | UNIT 2 | MACHINE LEARNING 3 | 8/10: Introduction to Machine learning, Classification with K-Nearest Neighbors | 8/12: Cross Validation and Naïve Bayes 4 | 8/17: Regression and Regularization | 8/19: Logistic Regression 5 | 8/24: Imbalanced Classes and Evaluation Metrics | 8/26: Advanced Classifiers 6 | 8/31: Ensemble Techniques | 9/2: Review of classification and regression | | | UNIT 3 | APPLICATIONS 7 | 9/7: Labor Day (NO CLASS) | 9/9: K-Means Clustering and Unsupervised learning 8 | 9/14: Dimensionality Reduction | 9/16: Recommendation systems 9 | 9/21: Neural Networks & Deep learning | 9/23: Natural Language Processing and Text Mining 10 | 9/28: Time Series Analysis | | | | UNIT 4 | AT SCALE 10 | | 9/30: Database Technologies 11 | 10/5: Map Reduce | 10/7: Paralell and distributed computing 12 | 10/12: Final project working session | 10/14: Final project presentations

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. Justin will be in Slack during class to handle questions. All instructors will be available on Slack during office hours (listed above).

• Data Science at the Command Line
• Learning Bash Scripting For Beginners
• Advanced Bash Scripting Guide

• Elements of Statistical Learning

• Top 10 Data Mining Algorithms in Plain English

• Data Science from Scratch Use the discount code "ASSEMBLY" for 40% off print version and 50% off ebook