Are you looking for a gigantic boost in your productivity? Are you searching for some interesting and fun tricks to solve your data problems? If so, then this course is indeed a perfect choice for you. This course provides you with unique, idiomatic, and amazing solutions for both fundamental and advanced data manipulation tasks with Pandas.

Pandas is a popular Open Source Python package that provides fast, high performance data structures for performing efficient data manipulation and analysis. It has quickly emerged as a popular choice of tool for analysts to solve real-world analytical problems. The Pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features.

This comprehensive 3-in-1 course is a step-by-step, a highly practical course showing you the whys and how's of applying Pandas for your data analysis tasks. Solve most complex scientific computing problems with ease using the power of Pandas. Manipulate, analyze and visualize your data using the popular Pandas library. Enhance your data exploration and machine learning skills by gaining surprising insights from Pandas and using expert tips and tricks.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learning Pandas, covers powerful Data Analysis with Python Library in an engaging and exciting way. Analyze and model your data, and organize the results of your analysis in the form of plots or other visualization means. Throughout the course, you’ll implement simple yet highly effective examples and use-cases which are relevant in the real-world scenario, as you build on your understanding of Pandas. By the end of this course, you’ll have a firm understanding of the basics of Pandas. You’ll be ready to start using Pandas for different data science tasks with confidence.

The second course, Data Analysis and Exploration with Pandas, covers idiomatic solutions to common data problems while working on real-world datasets to get surprising insights from the Pandas library. 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. Many advanced solutions combine several different features across the Pandas library to generate results.

The third course, Advanced Techniques for Exploring Data Sets with Pandas, covers popular datasets in R, while mastering advanced techniques used for them. Manipulate and reshape data using Pandas methods. You’ll also learn how to deal with missing data from your datasets, how to draw charts and plots using Pandas and Matplotlib, and how to create some cool visualizations for your audience. Finally, you will wrap-up your newly gained Pandas knowledge by learning how to get data out of Pandas into some popular file formats.

By the end of the course, you’ll get insights and solutions to common data problems while working on real-world datasets using Pandas library.

About the Authors

• Harish Garg is a Data Scientist and a Lead Software Developer with 17 years' software industry experience. He worked for McAfee\Intel for 11+ years before starting his own software consultancy. He is an expert in creating data visualizations using R, Python, and web-based visualization libraries.
  
  
• 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 dash boarding applications, and real-time web services to avoid mispricing sales items. Ted received his Master's 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.