#FREE Introduction to Data Analysis with Python #Developer #Python

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.

LEVEL –  Intermediate

COURSE TOPICS:

Writing and running Python in iPython
Using Python lists and dictionaries
Creating NumPy arrays
Indexing and slicing in NumPy
Downloading and parsing data files into NumPy and Pandas
Using multilevel series in Pandas
Aggregating data in Pandas

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LEARN THIS COURSE FOR FREE *10 days of free unlimited access to “Introduction to Data Analysis with Python”

Instructor’s Welcome Note:

– 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 in 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 your (mumbles) 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 Pandas for its own practical project. So, let’s get started with Introduction to Data Analysis with Python.

 

 

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