Utilize Python’s most efficient libraries—pandas, matplotlib, and Seaborn—for data visualization and time series analysis.

Description

Visualization is a critical component in exploratory data analysis, as well as presentations and applications. If you are struggling in your day-to-day data analysis tasks, then this is the right course for you. This fast-pace guide follows a recipe-based approach, each video focusing on a commonly-faced issue.

This course covers advanced and powerful time series capabilities so you can dissect by any possible dimension of time. It introduces the Matplotlib library, which is responsible for all of the plotting in pandas, at the same time focusing on the pandas plot method and the Seaborn library, which is capable of producing aesthetically pleasing visualizations not directly available in pandas. This course guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.

Style and Approach

The author relies on his vast experience teaching Python in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations reside in Jupyter Notebooks, an excellent interface for exploring data.

What You Will Learn

• Create beautiful and insightful visualizations through pandas' direct hooks to Matplotlib and Seaborn
• Utilize pandas' unparalleled time series functionality
• Split data into independent groups before applying aggregations and transformations to each group
• Prepare real-world messy datasets for machine learning
• Combine and merge data from different sources through pandas' SQL-like operations

Authors

Theodore Petrou

Theodore Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. He is also the head of Houston Data Science, a meetup group with more than 2,000 members that has the primary goal of getting local data enthusiasts together in the same room to practice data science. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data.

Some of his projects included using targeted sentiment analysis to discover the root cause of part failures from engineer text, developing customized client/server dashboarding applications, and real-time web services to avoid mispricing sales items. Ted received his Masters degree in statistics from Rice University, and used his analytical skills to play poker professionally and teach math before becoming a data scientist. Ted is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow.