SKILL PATH
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
45 hours
27
6
Intermediate
Start learning for free!
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 explore different data aggregation techniques, combine data sets, transform and reshape data, and clean strings to handle missing data.
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.
Walk through a data cleaning project from start to finish and hone your data cleaning skills through lots of deliberate practice.
It's not just what you learn,
but how you learn it.
Retain
Learn by writing and validating code,
not by watching videos.
Reinforce
Challenge yourself with dozens of
practice problems.
Reference
Revisit what you've learned anytime you need a refresher.
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Start learning Data Analysis and Visualization in Python today!
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