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Data Science Fundamentals (Paid)

Feb 20, 2019 · Seattle, United States of America

This is a paid course learn more and register here: https://www.galvanize.com/seattle/data-science-fundamentals

Interested in learning more data science? In this six-week part-time course, you’ll learn how to program for the field in Python and be better prepared to learn data science like a professional.

This workshop is for analysts, product managers, statisticians, business managers, and anyone else with little to no exposure to Python. It is taught by Galvanize's data science instructors and supported by the broader community.

What You’ll Learn / Takeaway:
Whether you’ve programmed in other languages or Python is your first, this class will teach you the nuances of Python and how to use them to your advantage in your data science projects. Here’s what you’ll learn:

Environment Setup
Data Scientist Workflow
Ins and Outs of Coding Pythonically
Object Oriented Programming
Popular Data Science Libraries including Matplotlib, Pandas, Numpy, Sklearn
Who Should Take this Class?
For those of you interested in learning to program in Python, so you may be better prepared for self-study in data science, this course will help you get up to speed.

Those of you who are interested in gaining the skills required for admittance to the Seattle Data Science Immersive. This course is designed to help you meet that bar.

A desire to learn.

Note: Previous programming experience can be helpful but not necessary.

Setup (please have this in advance):
• Bring your laptop and power cable - strong preference for macs or linux.**

• Install Anaconda with Python version 3.7 on your machine: http://docs.continuum.io/anaconda/install.html
• Install a text editor: https://code.visualstudio.com/
• If you have trouble with installation, we will assist on the first day.

**Instructors may not have the expertise to assist with troubleshooting on windows machines, and machines that do not use the above-mentioned computer specs.

Weekly Agenda
Mondays & Wednesdays, 6:30 pm – 8:30 pm

Week 1: February 20th

Week 2: February 25th and February 27th

Week 3: March 4th and March 6th

Week 4: March 11th and March 13th

Week 5: March 18th and March 20th

Week 6: March 25th and March 27th

Full Course Outline:

Week 1:

First class: Introduction to Git, GitHub, Unix, downloading software (Anaconda, Git, Atom)

Second class: Variable Assignment, Variable Declaration, Loops, Control Flow, Logic

Week 2:

First Class: Introduction to Python Data structures, Python Libraries, Lists, Strings

Second class: Tuples, Dictionaries, and Sets

Week 3:

First class: Functions and Scope
Second Class: Lab 1: Linking it all together

Week 4:

First class: Object Oriented Programming (OOP)

Second Class: More OOP

Week 5:

First Class: Lab 2 – OOP

Second class: Introduction to Pandas, DataFrames

Week 6:

First class: Lab 2 – OOP

Second class: Tying it all together

Cost of the entire course: $1,900.00 + Eventbrite registration fees.


For questions about this part-time course and our Data Science Immersive program, please email our Enrollment Officer, Lauren Lark at [masked].

Refund Policy

-Cancel more than 48 hours before first class: Full Refund (minus processing fee).
-Cancel within the first week: Charged for 1 day of class, and reimbursed the remaining fees paid for the workshop.
-Cancel after first week: Can apply cost to different program, no refund.

This is a paid course learn more and register here: https://www.galvanize.com/seattle/data-science-fundamentals

Event organizers
  • Learn to Code Seattle

    Have you ever thought about learning to code but weren't sure how to get started? Come meet other aspirational coders and explore the fundamentals of programming and develop an attitude that will enable us to quickly learn new technologies. Each Meetup will be led by an expert who will guide us in a new skill or concept, and help us get started with programming, Here are some example of topics we will cover: • Intro to HTML and CSS • Intro to Javascript • Intro to Git and GitHub • Intro to SQL •  intr

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