Interactive Course

Analyzing Police Activity with pandas

Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.

  • 4 hours
  • 16 Videos
  • 50 Exercises
  • 3,761 Participants
  • 4,100 XP

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Course Description

Now that you have learned the foundations of pandas, this course will give you the chance to apply that knowledge by answering interesting questions about a real dataset! You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. During the course, you will gain more practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset from start to finish, preparing you for your data science career!

  1. 1

    Preparing the data for analysis

    Free

    Before beginning your analysis, it is critical that you first examine and clean the dataset, to make working with it a more efficient process. In this chapter, you will practice fixing data types, handling missing values, and dropping columns and rows while learning about the Stanford Open Policing Project dataset.

  2. Visual exploratory data analysis

    Are you more likely to get arrested at a certain time of day? Are drug-related stops on the rise? In this chapter, you will answer these and other questions by analyzing the dataset visually, since plots can help you to understand trends in a way that examining the raw data cannot.

  3. Exploring the relationship between gender and policing

    Does the gender of a driver have an impact on police behavior during a traffic stop? In this chapter, you will explore that question while practicing filtering, grouping, method chaining, Boolean math, string methods, and more!

  4. Analyzing the effect of weather on policing

    In this chapter, you will use a second dataset to explore the impact of weather conditions on police behavior during traffic stops. You will practice merging and reshaping datasets, assessing whether a data source is trustworthy, working with categorical data, and other advanced skills.

  1. 1

    Preparing the data for analysis

    Free

    Before beginning your analysis, it is critical that you first examine and clean the dataset, to make working with it a more efficient process. In this chapter, you will practice fixing data types, handling missing values, and dropping columns and rows while learning about the Stanford Open Policing Project dataset.

  2. Exploring the relationship between gender and policing

    Does the gender of a driver have an impact on police behavior during a traffic stop? In this chapter, you will explore that question while practicing filtering, grouping, method chaining, Boolean math, string methods, and more!

  3. Visual exploratory data analysis

    Are you more likely to get arrested at a certain time of day? Are drug-related stops on the rise? In this chapter, you will answer these and other questions by analyzing the dataset visually, since plots can help you to understand trends in a way that examining the raw data cannot.

  4. Analyzing the effect of weather on policing

    In this chapter, you will use a second dataset to explore the impact of weather conditions on police behavior during traffic stops. You will practice merging and reshaping datasets, assessing whether a data source is trustworthy, working with categorical data, and other advanced skills.

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Lloyd's Banking Group

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Harvard Business School

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Decision Science Analytics @ USAA

Kevin Markham
Kevin Markham

Founder of Data School

Kevin is the founder of Data School, an online school for learning data science with Python. He is passionate about teaching people who are new to the field, regardless of their educational and professional backgrounds. Currently, he teaches machine learning and data analysis to over 10,000 students each month through the Data School YouTube channel. He has a degree in Computer Engineering from Vanderbilt University.

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