Learn Data Science in Python :: Python Certificate Course
Start your journey in data science and learn the basics of Python.
Whether you have experience in programming or are looking to get started for the first time, the Python for Data Science class will put you on the fast track to honing your python and data analysis skills. In this class, you’ll get hands-on programming experience in Python that you’ll be able to immediately apply in the real world. The class will cover the fundamentals of Python and several tools used in data science.
Anyone interested in learning more about python, data science, or interested about the rapidly expanding world of data science should join the class!
In this Python For Data Science course, you’ll learn:
- Collect data from a variety of sources (e.g., Excel, web-scraping, APIs and others)
- Explore and analyze large data sets
- Clean and “munge” the data to prepare it for analysis
- Apply machine learning algorithms to gain insight from the data
- Use matplotlib to create beautiful data visualization
This is a very practical and hands-on class that has lots of class exercises. You’ll build your own library of Python scripts that can be reused after you’ve done with the course.
Prereqs & Preparation
You must bring a laptop with a text editor.
Sublime Text is recommended and has a free trial version (http://www.sublimetext.com/).
In addition, students should install Anaconda, which is a free package that includes python and a number of tools that will be used in class (http://continuum.io/downloads).
Session I: Intro to Python Fundamentals
- Introduction to Data Types
- If Statements
- For Loops
- Understanding lists, tuples, and dictionaries
Session II: Data Collection and Exploration
- Importing data from a variety of sources
- Data exploration
- Visualizing data using Matplotlib
Session III: Data Cleaning and Visualization
- Data manipulation using Pandas
- Data cleaning and formatting
- Feature engineering
Session IV: Machine Learning
- Overview of machine learning
- Implementing machine learning algorithms in Python
- Measuring algorithm performance with cross-validation