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Posts

  • Layouts, Interactions, and Annotations

    Learn how to combine multiple Bokeh plots into different kinds of layouts on a page, how to easily link different plots together, and how to add annotations such as legends and hover tooltips. This is the Summary of lecture "Interactive Data Visualization with Bokeh", via datacamp.

    Jun 30, 2020

  • Basic plotting with Bokeh

    This chapter provides an introduction to basic plotting with Bokeh. You will create your first plots, learn about different data formats Bokeh understands, and make visual customizations for selections and mouse hovering. This is the Summary of lecture "Interactive Data Visualization with Bokeh", via datacamp.

    Jun 30, 2020

  • Visualization in the data science workflow

    Often visualization is taught in isolation, with best practices only discussed in a general way. In reality, you will need to bend the rules for different scenarios. From messy exploratory visualizations to polishing the font sizes of your final product; in this chapter, we dive into how to optimize your visualizations at each step of a data science workflow. This is the Summary of lecture "Improving Your Data Visualizations in Python", via datacamp.

    Jun 30, 2020

  • Showing uncertainty

    Uncertainty occurs everywhere in data science, but it's frequently left out of visualizations where it should be included. Here, we review what a confidence interval is and how to visualize them for both single estimates and continuous functions. Additionally, we discuss the bootstrap resampling technique for assessing uncertainty and how to visualize it properly. This is the Summary of lecture "Improving Your Data Visualizations in Python", via datacamp.

    Jun 29, 2020

  • Using color in your visualizations

    Color is a powerful tool for encoded values in data visualization. However, with this power comes danger. In this chapter, we talk about how to choose an appropriate color palette for your visualization based upon the type of data it is showing. This is the Summary of lecture "Improving Your Data Visualizations in Python", via datacamp.

    Jun 28, 2020

  • Customizing Seaborn Plots

    In this final chapter, you will learn how to add informative plot titles and axis labels, which are one of the most important parts of any data visualization! You will also learn how to customize the style of your visualizations in order to more quickly orient your audience to the key takeaways. Then, you will put everything you have learned together for the final exercises of the course! This is the Summary of lecture "Introduction to Data Visualization with Seaborn", via datacamp.

    Jun 27, 2020

  • Visualizing Two Quantitative Variables

    In this chapter, you will create and customize plots that visualize the relationship between two quantitative variables. To do this, you will use scatter plots and line plots to explore how the level of air pollution in a city changes over the course of a day and how horsepower relates to fuel efficiency in cars. You will also see another big advantage of using Seaborn - the ability to easily create subplots in a single figure! This is the Summary of lecture "Introduction to Data Visualization with Seaborn", via datacamp.

    Jun 26, 2020

  • Introduction to Seaborn

    What is Seaborn, and when should you use it? In this chapter, you will find out! Plus, you will learn how to create scatter plots and count plots with both lists of data and pandas DataFrames. You will also be introduced to one of the big advantages of using Seaborn - the ability to easily add a third variable to your plots by using color to represent different subgroups. This is the Summary of lecture "Introduction to Data Visualization with Seaborn", via datacamp.

    Jun 26, 2020

  • Visualizing a Categorical and a Quantitative Variable

    Categorical variables are present in nearly every dataset, but they are especially prominent in survey data. In this chapter, you will learn how to create and customize categorical plots such as box plots, bar plots, count plots, and point plots. Along the way, you will explore survey data from young people about their interests, students about their study habits, and adult men about their feelings about masculinity. This is the Summary of lecture "Introduction to Data Visualization with Seaborn", via datacamp.

    Jun 26, 2020

  • Highlighting your data

    How do you show all of your data while making sure that viewers don't miss an important point or points? Here we discuss how to guide your viewer through the data with color-based highlights and text. We also introduce a dataset on common pollutant values across the United States. This is the Summary of lecture "Improving Your Data Visualizations in Python", via datacamp.

    Jun 26, 2020

  • Sharing visualizations with others

    This chapter shows you how to share your visualizations with others - how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data. This is the Summary of lecture "Introduction to Data Visualization with Matplotlib", via datacamp.

    Jun 26, 2020

  • Quantitative comparisons and statistical visualizations

    Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations. This is the Summary of lecture "Introduction to Data Visualization with Matplotlib", via datacamp.

    Jun 26, 2020

  • Plotting time-series

    Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data. This is the Summary of lecture "Introduction to Data Visualization with Matplotlib", via datacamp.

    Jun 26, 2020

  • Introduction to Matplotlib

    This chapter introduces the Matplotlib visualization library and demonstrates how to use it with data. This is the Summary of lecture "Introduction to Data Visualization with Matplotlib", via datacamp.

    Jun 26, 2020

  • Model-free Policy Iteration with TD Methods

    A summary of "Understanding Deep Reinforcement Learning"

    Jun 24, 2020