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Hands-On Data Analysis with Pandas

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  • Understand how data analysts and scientists think about gathering and understanding data
  • Perform data analysis and data wrangling in Python 
  • Combine, grouping, and aggregating data from multiple sources
  • Create data visualizations with pandas and matplotlib
  • Learn how to apply machine learning algorithms to make predictions and look for patterns.
  • Use Python Data Science libraries to analyze real-world datasets.
  • Use pandas to solve several common data representation and analysis problems

Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate great value for companies.

This book will show you how to analyze your data and get started with machine learning in Python using the powerful pandas library. We will extend pandas offerings with other Python libraries such as matplotlib, NumPy, and scikit-learn to perform each phase and operation of data analysis tasks. You will learn data wrangling, how to manipulate your data, clean it, visualize it, find patterns, and make predictions based on the past data using real-world examples. You will learn how to conduct data analysis, and then take our analyses a step further as we explore some applications of anomaly detection, regression, clustering, and classification.

Towards the end of the book, you will be able to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

  • Perform efficient data analysis and manipulation tasks using pandas 
  • Apply pandas to different real-world domains using step-by-step demonstrations 
  • Get comfortable using pandas and Python as an effective data exploration and analysis tool
Page Count 434
Course Length 13 hours 1 minutes
Date Of Publication 28 Jun 2019


Stefanie Molin

Stefanie Molin is currently a Data Scientist / Software Engineer (and hacker in training) in NYC tackling tough problems in Information Security particularly revolving around anomaly detection and building tools for gathering data, data analysis, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and machine learning in both R and Python in the AdTech and Financial Services industries. She holds a B.S. in Operations Research from Columbia University’s engineering school with minors in Economics and Entrepreneurship and Innovation.

In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken both among people and computers.