Plotting or data visualization is the first step for quantifying the relationship between feature and target variables.Most data science problems boil down to studying the mathematical relationship between feature and target variables.Pie Chart showing various asset classes in a portfolio. Tesla stock price for first 16 days in April 2021.įigure 10. Time-dependent plots for studying trendsįigure 9. Covariance matrix plot for selected tech stocks.ĥ. Heatmap plot for quantifying relationshipsįigure 8. Mean cross-validation scores for different regression models. Ideal and fitted plots for the crew variable using multiple regression analysis. Scatter plots for studying relationshipsįigure 6. Shows that males are on average taller than females.ģ. Probability distribution of male and female heights. The probability distribution of the sample means of a uniform distribution using Monte-Carlo simulation. Density plots for studying distribution of a Variableįigure 4. 2020 Worldwide number of jobs by skill using LinkedIn search tool. 2016 Market share of electric vehicles in selected countries. Source: Bayes’ Theorem Explained.įigure 2. This shows that we have a very unbalanced dataset, with 77% male heights, and 23% female heights. Barplots for comparing different quantitiesįigure 1. The caption for each plot contains a link that will take you to the original article where you can find more details such as dataset and source code used for generating the plots. In this section, we discuss several plots used in data science and machine learning. Examples of Plots and Data Visualizations Important data visualization tools include Python’s matplotlib and seaborn packages, and R’s ggplot2 package. Ethical Component: Here, you want to make sure your visualization tells the true story. You need to be aware of your actions when cleaning, summarizing, manipulating, and producing a data visualization and ensure you aren’t using your visualization to mislead or manipulate your audience.Labels Component:This includes things like axes labels, titles, legends, font size to use, etc.Scale Component:Here, you decide what kind of scales to use in your plot, e.g., linear scale, log scale, etc.Mapping Component:Here, you need to decide what variable to use as your independent variable ( x-variable) and what to use as your dependent variable ( y-variable). This is important, especially when your dataset is multi-dimensional with several features.Geometric Component:Here is where you decide what kind of visualization is suitable for your data, e.g., scatter plot, line graphs, bar plots, histograms, Q-Q plots, smooth densities, boxplots, pair plots, heatmaps, pie charts, etc.Data Component: An important first step in deciding how to visualize data is to know what type of data it is, e.g., categorical data, discrete data, continuous data, time-series data, etc.In this article, we examine various types of plots used in data science and machine learning.Įssential Components for Generating a PlotĪ good plot or data visualization is made up of several components that have to be pieced up together to produce an end product: comparing different quantities, studying trends, quantifying relationships, or displaying proportions. Plotting or data visualization is the oldest and most important branch of data science. Plotting and data visualization can tell different types of stories between features and target variables e.g. The predictor variables are also referred to as the independent variables, while the target variable is the dependent variable. Most of basic data science is focused on finding the relationship between features ( predictor variables) and a target variable ( outcome). In this article, we discuss the importance of plotting and data visualization in data science and machine learning.

But to get started, you only need to master a few math topics.Data science is a very quantitative field that requires advanced mathematics.

Most beginners interested in getting into the field of data science are always concerned about the math requirements.

Photo by Isaac Smith on Unsplash Key Takeaways

Scatter plots in data science