Galvanize Data Science Fundamentals - Intro to Python San Francisco 10/31/17
Are you interested in learning more data science? You know it’s necessary to be a strong programmer, but you don’t know how to get started. In this part-time course, you will learn the fundamentals of the Python programming language, a tool used by developers, engineers, and scientists worldwide.
This course is for analysts, product managers, statisticians, business managers and anyone else who wants to build their Python skills for data science.
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 San Francisco Data Science Immersive; this course is designed to help you meet that bar. The cost of this course can applied to our full-time immersive program.*
Desire to learn.
• Bring your laptop and power cable.
• Install Anaconda with Python version 2.7 on your machine: http://docs.continuum.io/anaconda/install.html
• Install a text editor: http://www.sublimetext.com/
• If you have trouble with installation, we will assist on the first day.
Tuesday & Thursday, 6:30 pm – 9:30 pm
Dates: 10/31/2017 - 12/12/2017
Duration: 6 Weeks (2 nights per week)
Day 1, October 31: Introduction to git, Github, Unix, Downloading software (Anaconda, git, sublime)
Day 2, November 2: Variable assignment, Variable declaration, Loops, Control flow, Logic
Day 3, November 7: Introduction to Python data structures, Python libraries, Lists, Strings
Day 4, November 9: Tuples, Dictionaries, and Sets
Day 5, November 14: Functions and Scope
Day 6, November 16: Lab 1: Linking it all together
Day 7, November 21: Object Oriented Programming (OOP)
Day 8, November 28: OOP
Day 9, November 30: Lab 2 – OOP
Day 10, December 5: Introduction to Pandas, Dataframes
Day 11, December 7: DataFrame operations, Numpy
Day 12, December 12: Introduction to Statistical Modeling – SciKit Learn and Statsmodels