DATA ANALYSIS & VISUALIZATION WITH PYTHON
Data visualization is an important skill for data professionals, and Python offers some great tools for creating different kinds of visuals. There are two important types of visualization to learn:
Exploratory Data Visualization — the first step in analysis workflow for many projects, this makes it easier to understand patterns in your data.
Explanatory Data Visualization — you can use data from your analysis to share easy-to-understand insights
SKILL PATH: DATA ANALYSIS & VISUALIZATION WITH PYTHON
Here's what you'll learn to do.
Even if you’ve never written a line of code in Python, you’ll catch on quickly, and we’ll have you writing code in minutes.
- How to use the Python libraries, pandas, and NumPy for data analysis
- The most common data visualization techniques to explore data
- Using data visualization to convey insights and tell a story
- How to manipulate and clean data
Course Structure: Data Analysis & Visualization with Python
Learn how vectorized operations make working with data faster, selecting data by value using boolean indexing and using pandas and NumPy methods to analyze data.
Learn how to visualize time series data with line plots, visualizing frequency distributions with bar plots and histograms, and how to speed up your exploratory data visualization workflow using Pandas.
Learn how to better create graphs for an audience by using information design principles, creating visual patterns using Gestalt principles, and using Matplotlib built-in styles — case study on the FiveThirtyEight style.
Learn to use regular expressions to clean and manipulate text data, understand how to use lambda functions and list comprehension with pandas, and strategies for working with missing data.
It's not just what you learn,
but how you learn it.
Learn by writing and validating code,
not by watching videos.
Challenge yourself with dozens of
Revisit what you've learned anytime you need a refresher.
Join over 1 million data learners!
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