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LinkedIn Learning

Python Data Analysis

via LinkedIn Learning

Overview

Interested in using Python for data analysis? Learn how to use Python, NumPy, and pandas together to analyze data sets large and small.

Data science is transforming the way that government and industry leaders look at both specific problems and the world at large. Curious about how data analysis actually works in practice? In this course, instructor Michele Vallisneri shows you how, explaining what it takes to get started with data science using Python.Michele demonstrates how to set up your analysis environment and provides a refresher on the basics of working with data structures in Python. Then, he jumps into the big stuff: the power of arrays, indexing, and tables in NumPy and pandas—two popular third-party packages designed specifically for data analysis. He also walks through two sample big-data projects: using NumPy to identify and visualize weather patterns and 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.Note: This version of the course was updated to reflect recent changes in Python 3, NumPy, and pandas.

Syllabus

Introduction
  • Get started in data analysis with Python
  • What you need to know
  • What's new in this update
1. Installation and Setup
  • Install Anaconda Python on OS X
  • Install Anaconda Python on Windows
  • Working with Jupyter Notebooks
  • Using the exercise files
  • Using Python in the cloud
2. Data Structures in Pure Python
  • Warmup with Python loops
  • Sequences: Lists, tuples, and the slicing syntax
  • Dictionaries and sets
  • Comprehensions
  • Advanced Python containers
3. Wordplay: Anagrams and Palindromes
  • Anagrams overview
  • Loading a dictionary
  • Finding anagrams
  • Challenge: Palindromes
  • Solution: Palindromes
4. Arrays with NumPy
  • NumPy overview
  • Creating NumPy arrays
  • Indexing NumPy arrays
  • Doing math with NumPy arrays
  • Special arrays: Records and dates
5. Use Case: Weather Data
  • Overview of use case
  • Loading station and temperature data
  • Filling missing values
  • Smoothing time series
  • Weather charts
  • Challenge: Weather anomalies
  • Solution: Weather anomalies
6. pandas
  • pandas overview
  • DataFrames and Series
  • Indexing in pandas
  • Plotting
7. Use Case: Baby Names
  • Overview of use case
  • Loading data sets
  • Comparing name popularity
  • Yearly top ten names
  • Challenge: Unisex baby names
  • Solution: Unisex baby names
Conclusion
  • Next steps

Taught by

Michele Vallisneri

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