Python: Data Analysis (2015)

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  • Course details

    If you're going to work with big data, you'll probably be using R or Python. And if you're using Python, you'll be definitely using Pandas and NumPy, the third-party packages designed specifically for data analysis. This course provides an opportunity to learn about them. Michele Vallisneri shows how to set up your analysis environment and provides a refresher on the basics of working with data containers in Python. Then he jumps into the big stuff: the power of arrays, indexing, and DataFrames in NumPy and Pandas. He also walks through two sample big-data projects: one using NumPy to analyze weather patterns and the other using Pandas to analyze the popularity of baby names over the last century. Challenges issued along the way help you practice what you've learned.


    • Click here to view Michele Vallisneri’s instructor page

      Michele Vallisneri

      Director's Fellow at JPL (NASA's Jet Propulsion Laboratory)

      Michele Vallisneri is a theoretical astrophysicist at NASA Jet Propulsion Laboratory.

      He obtained his PhD in physics at the California Institute of Technology in 2002. His research spans the detection and interpretation of gravitational waves with LIGO, in space, and with pulsar timing. He is an expert in data analysis, Bayesian inference, and computational physics, and he believes that elegant, transparent programming can illuminate the hardest problems. He is a Fellow of the American Physical Society, and he was awarded the NASA Exceptional Scientific Achievement Medal.

    Skills covered in this course

  • Welcome

    - Hi, I'm Michele Vallisneri and I'd like to welcome you to Introduction to Data Analysis with Python. Data science has been described as intersection of programming, statistics and topical expertise. Python is an excellent programming tool for data analysis because it's friendly, pragmatic, mature and because it's complemented by excellent third party packages that were designed to deal with large amounts of data. We will start this course by reviewing Python data containers which are useful on their own and which set the model for the more powerful data objects of NumPy and Pandas. We will then put our knowledge of containers to work in a practical project. Then, we will talk about NumPy, the package that extends Python with a fast and efficient numerical array object. And we'll take NumPy out for a spin for a real data analysis project. Last, we will look at Pandas which is suitable for any kind of data and implements many ideas from the world of relational databases. We will use…

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