{"@attributes":{"version":"2.0"},"channel":{"title":"ERC","link":"https:\/\/erclearninglab.com","description":"ERC","pubDate":"Thu, 29 Jan 2026 18:23:46 +0000","generator":"https:\/\/erclearninglab.com","language":"en","item":[{"title":"Desktop","link":"https:\/\/erclearninglab.com\/Desktop","pubDate":"Fri, 09 Aug 2024 18:37:42 +0000","guid":"https:\/\/erclearninglab.com\/Desktop","description":"\n.bottom-dock {\n position: fixed;\n bottom: 0;\n left: 0;\n right: 0;\n background: linear-gradient(to bottom, #e8e8e8, #f5f5f5);\n padding: 8px 20px;\n box-shadow: 0 -2px 10px rgba(0,0,0,0.1);\n z-index: 1000;\n border-top: 1px solid #ddd;\n text-align: center;\n}\n.bottom-dock a {\n text-decoration: none;\n color: #333;\n transition: opacity 0.2s;\n margin: 0 8px;\n display: inline-block;\n}\n.bottom-dock a:hover {\n opacity: 0.5;\n}\n.icon-link {\n font-size: 1.2em;\n}\n\/* Ensure all images inside .image-link are same size *\/\n.image-link img {\n width: 40px; \/* You can adjust the size here *\/\n height: 40px;\n object-fit: contain;\n display: inline-block;\n}\n\n\n\n \n \n \n \n \n \n \n\n\n\n\"ReadMe\"\n\"HowDoI..?\"\n\"CheatSheets\"\n\"CompleteGuides\"\n\"SubmitFeedback!\"\n\"Recordings\"\n"},{"title":"HowDoI","link":"https:\/\/erclearninglab.com\/HowDoI","pubDate":"Fri, 09 Aug 2024 18:37:43 +0000","guid":"https:\/\/erclearninglab.com\/HowDoI","description":"\nHow do I...?\n\n\n\n\n "},{"title":"CompleteGuides","link":"https:\/\/erclearninglab.com\/CompleteGuides-1","pubDate":"Fri, 09 Aug 2024 18:37:43 +0000","guid":"https:\/\/erclearninglab.com\/CompleteGuides-1","description":"\n\nGetting started\n\n\n \nQGIS\n\n \n \n Getting started with QGIS\n \n \n \n \n R\n\n \n \n Getting started with data analysis & visualization in r\n \n \n \n Getting started with regression analysis in R\n \n \n \n \n Stata\n\n \n \n Getting started with data analysis & visualization in stata\n \n \n \n Preprocessing data and running regressions in stata\n \n \n \n \n Excel\n\n\n \n \n Getting started with ExceLPython\n\n\nGetting started with pythonUnderstanding Data\n\n \n \n Getting started using (US) Census Data\n \n\n \n \n \n>"},{"title":"CheatSheets","link":"https:\/\/erclearninglab.com\/CheatSheets","pubDate":"Mon, 12 Aug 2024 15:51:07 +0000","guid":"https:\/\/erclearninglab.com\/CheatSheets","description":"\n\t\n\t\n\t\n\t\n\t\n\n\ncheat sheetsprintable one pagers to help you find useful functions and tools\n\n\t\n\n\n        \n\t\n\t\n\t\n\n\n\n\n\n"},{"title":"workshop recordings","link":"https:\/\/erclearninglab.com\/workshop-recordings-1","pubDate":"Thu, 09 Oct 2025 14:59:35 +0000","guid":"https:\/\/erclearninglab.com\/workshop-recordings-1","description":"\n\t\n\t\n\n\nWorkshop RecordingsLong-form videos from our in-person open workshops to help you learn softwares\n\n\t\n\t\n\t\n\t\n\t\n\n\n\n\n\n\"\n\"Finding\n\"\n\"R\"\n\"Stata\"\n\"SQL\"\n"},{"title":"Getting Started with R","link":"https:\/\/erclearninglab.com\/Getting-Started-with-R","pubDate":"Mon, 09 Jun 2025 16:05:38 +0000","guid":"https:\/\/erclearninglab.com\/Getting-Started-with-R","description":"\n Getting Started with R\n \n Base R\n \n \n Data Transformation\n \n \n Tidyr\n \n \n ggplot2 Cheat Sheet\n \n"},{"title":"SPSS","link":"https:\/\/erclearninglab.com\/SPSS","pubDate":"Tue, 13 May 2025 16:25:42 +0000","guid":"https:\/\/erclearninglab.com\/SPSS","description":"SPSS Cheat Sheet\n\n"},{"title":"R","link":"https:\/\/erclearninglab.com\/R","pubDate":"Thu, 05 Jun 2025 16:34:48 +0000","guid":"https:\/\/erclearninglab.com\/R","description":"\n\nR Cheat Sheets\n\n\n\n\n\n\n\t Getting started with R\n\tTidying data with tidyr\n\tData visualization with ggplot2\n\t\n\nTransforming data with dplyr\n\n\n"},{"title":"Data Analysis in Stata ","link":"https:\/\/erclearninglab.com\/Data-Analysis-in-Stata","pubDate":"Mon, 05 May 2025 20:23:11 +0000","guid":"https:\/\/erclearninglab.com\/Data-Analysis-in-Stata","description":"Introductory Data Analysis and Exploration in Stata\n\n"},{"title":"Getting started with Python (Rayhana)","link":"https:\/\/erclearninglab.com\/Getting-started-with-Python-Rayhana","pubDate":"Thu, 29 Jan 2026 18:23:46 +0000","guid":"https:\/\/erclearninglab.com\/Getting-started-with-Python-Rayhana","description":"\n.guide-callout {\n border: 2px solid rgb(70, 130, 180);\n background-color: rgb(234, 244, 251);\n padding: 16px;\n max-width: 760px;\n margin: 1.5em 0px;\n}\n\n.reminder-box {\n border: 1px solid #b7c9d8;\n background: #f8fbfd;\n padding: 12px 14px;\n margin: 1.25em 0;\n}\n\n.code-block {\n border: 1px solid #cfd8e3;\n background: #f7f9fb;\n margin: 1em 0;\n max-width: 850px;\n border-radius: 6px;\n overflow: hidden;\n}\n\n.code-header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n background: #eaf4fb;\n border-bottom: 1px solid #cfd8e3;\n padding: 8px 12px;\n 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#a31515;\n}\n\n.hljs-number {\n color: #098658;\n}\n\n.hljs-comment {\n color: #008000;\n}\n\n.hljs-title,\n.hljs-function {\n color: #795e26;\n}\n\n.hljs-variable,\n.hljs-params {\n color: #001080;\n}\n\n.toc-box {\n border: 2px solid rgb(70, 130, 180);\n background-color: rgb(234, 244, 251);\n padding: 16px;\n max-width: 760px;\n margin: 1.5em 0px;\n}\n\n.toc-box ol {\n margin-bottom: 0;\n}\n\n\n\n\n\n\n\nfunction copyCode(button) {\n const codeBlock = button.closest(\".code-block\");\n const code = codeBlock.querySelector(\"code\").textContent;\n\n navigator.clipboard.writeText(code).then(function () {\n const originalText = button.textContent;\n button.textContent = \"Copied!\";\n\n setTimeout(function () {\n button.textContent = originalText;\n }, 1500);\n });\n}\n\nfunction runHighlighting() {\n if (window.hljs) {\n document.querySelectorAll(\"pre code\").forEach(function (block) {\n hljs.highlightElement(block);\n });\n }\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", runHighlighting);\nwindow.addEventListener(\"load\", runHighlighting);\n\n\n\n\nA Guide to Your First Python Data Analysis Project\n\nTable of Contents:\n \n Understanding the Fundamentals of Data Analysis\n Setting Up Your Python Analysis Environment\n Loading and Exploring the Spotify Dataset\n Identifying and Handling Missing Data\n Filtering and Subsetting Data\n Analytical Questions and Insights\n Visualizing Data\n Exploring Relationships Using Regression\n Summary and Next Steps\n Glossary\n \n\n\nAnalyzing Spotify Song Attributes with Pandas, Matplotlib, and Seaborn\n\n\n This guide walks you through a full, real-world data analysis workflow using Python. Our goal is to explore a large dataset of Spotify songs and uncover patterns in characteristics like energy, danceability, popularity, and genre. Along the way, we will learn essential analysis skills including loading data, examining its structure, cleaning it, filtering it, summarizing it, visualizing relationships, and interpreting a simple regression.\n\n\n\n\n1. Understanding the Fundamentals of Data Analysis\n\n\n Before writing any code, it is useful to understand a few foundational concepts. These concepts shape how we analyze, filter, and visualize data.\n\n\nTypes of Data in a Dataset\n\n\n Every dataset is made up of different kinds of variables, and each type determines what you can do analytically.\n\n\nNumerical Variables\n\n\n These include values that can be counted or measured, such as tempo, energy, loudness, danceability, and popularity.\n\n\nNumerical variables allow you to compute:\n\n\n averages\n correlations\n minimum and maximum values\n distributions\n regression models\n\n\n\n They also work well in visualizations like histograms, line charts, bar charts, heatmaps, and scatterplots.\n\n\nCategorical Variables\n\n\n These represent groups, categories, or labels, such as genre, artist_name, key, or mode. They are essential for:\n\n\n\n filtering subsets of the data\n grouping values by category\n comparing differences between groups\n computing category-level statistics, such as average danceability by genre\n\n\n\n Together, numerical and categorical variables form the backbone of most data analysis tasks. Recognizing them helps you choose appropriate methods and avoid errors. For example, you would not calculate the \u201cmean genre,\u201d nor would you plot text as a scatterplot axis.\n\n\nWhat Pandas Does and Why We Use It\n\nWhat is a library in Python?\n\n\n A library is a collection of pre-written code that allows us to use tools someone else already made instead of building everything from scratch.\n\n\n\n Pandas is a Python library designed for working with tabular data. If Excel could be expanded and connected to other analytical tools, that would be Pandas. It allows us to:\n\n\n\n Load data from CSV files, URLs, Excel files, and more\n Organize data into tables called DataFrames\n Filter, sort, and group data\n Inspect and summarize tables\n Clean and fix messy datasets\n Compute aggregated statistics\n Reshape and merge datasets\n\n\n2. Setting Up Your Python Analysis Environment\n\n\n Before we begin working with the Spotify dataset, let\u2019s practice writing and running a few basic lines of Python code. This will help you become comfortable with code cells, variables, lists, and simple calculations.\nPrinting text: The print() function displays output.\n\n\n\n\n Python\n Copy\n \n print(\"I am learning Python for data analysis.\")\n\n\n\n \nCreating variables: A variable stores information that we can reuse later.\n\n\n\n\n Python\n Copy\n \n song_title = \"Blinding Lights\"artist = \"The Weeknd\"popularity = 95\n\nprint(song_title)print(artist)print(popularity)\n\n\n\n \nPython can store different types of information, including text and numbers. Text values are placed inside quotation marks, while numbers do not need quotation marks.\n\n\n\n\n \nDoing basic math: Python can also be used like a calculator.\n\n\n\n\n Python\n Copy\n \n x = 10y = 5\n\nprint(x + y)print(x - y)print(x * y)print(x \/ y)\n\n\n\n \nCreating lists: A list stores multiple values in one variable.\n\n\n\n\n Python\n Copy\n \n genres = [\"Pop\", \"Rock\", \"Jazz\", \"Hip-Hop\"]print(genres)\n\n\n\n \nYou can access a single item from a list using its position. Python starts counting at 0.\n\n\n\n\n Python\n Copy\n \n print(genres[0])print(genres[1])\n\n\n\n\"Pop\" is in position 0, and \"Rock\" is in position 1.\n\n\n\nPython Comparison & Logic Operators\n\n\n\n = \u2192 to store a value in a variable\n == \u2192 equal to\n != \u2192 unequal to\n > \u2192 greater than\n < \u2192 smaller than\n & \u2192 and\n | \u2192 or\nTo complete this project, you will use Python along with three key libraries: Pandas for data handling, Matplotlib for basic visualizations, and Seaborn for statistical graphics built on top of Matplotlib.\n\n\n\n If you are working in Google Colab or Jupyter Notebook, these libraries are typically pre-installed. If working locally, they can be installed using:\n\n\n\n \n Python\n Copy\n \n pip install pandas matplotlib seaborn\n\n\n\n Once installed, you can import them. Importing a library as something gives it a nickname, so you can refer to it with a shorter name in your code.\n\n\n\n \n Python\n Copy\n \n import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n\n The imports establish the tools you will rely on throughout the rest of the guide.\n\n\n3. Loading and Exploring the Spotify Dataset\n\n\n The dataset for this project is a CSV file. A CSV file stands for \u201ccomma-separated values\u201d and stores data in a simple table format, where each row is a line and each value is separated by a comma. Our Spotify dataset includes more than one hundred thousand tracks from Spotify across multiple genres. Each song includes attributes like acousticness, tempo, loudness, energy, popularity, and danceability.\n\n\n\n First, download the Spotify dataset from the Dropbox link and save it somewhere easy to find on your computer, such as your Downloads folder or the same folder as your notebook.\n\n\n\n Then, use the file path to load the CSV file. A file path tells Python where a file is saved on your computer. If the CSV file is in the same folder as your notebook, you can use just the file name. If it is saved somewhere else, such as your Downloads folder, you need to give Python the full path to that location.\n\n\n\n \n Python\n Copy\n \n df = pd.read_csv(\"Users\/erc\/Downloads\/spotify_tracks_dataset.csv\")\ndf\n\n\n\n You now have a DataFrame, Pandas\u2019 core data table object, ready for analysis.\n\n\nExamining the Structure of Your Dataset\n\n\n Exploration is a critical step because it helps you understand what you are working with before making any assumptions or conducting deeper analysis.\n\n\nPreviewing the First Few Rows\n\n\n \n Python\n Copy\n \n df.head(5)\n\n\nThis gives you a sense of what types of variables exist.\n\n\n\nListing All Column Names\n\n\n \n Python\n Copy\n \n df.columns\n\n\n\n This helps you understand the set of variables in the dataset that you can use to ask questions.\n\n\nUnderstanding Data Types and Missing Values\n\n\n \n Python\n Copy\n \n df.info()\n\n\nThis command allows us to see:\n\n\n which columns are integers, floats, or text\n whether any columns contain missing values\n the overall size of the dataset\n\n\n\n The DType tells us what kind of data is stored in each column. Object columns usually contain text, float columns contain decimal numbers, and int columns contain whole numbers.\n\n\nSummary Statistics for Numerical Variables\n\n\n \n Python\n Copy\n \n df.describe()\n\n\nThis provides key descriptive measures including:\n\n\n mean\n standard deviation\n quartiles\n min and max values\n\n\n\n Beginning our analysis with these summaries will help us have an idea of the data\u2019s distribution and scale.\n\n\n4. Identifying and Handling Missing Data\n\n\n Almost all real-world datasets contain missing information. Missing values can interrupt mathematical calculations, skew charts, and cause functions to fail.\n\n\nChecking for Missing Values\n\n\n \n Python\n Copy\n \n df.isna().sum()\n\n\n\n This produces a count of missing entries in each column. If there are few, you can drop rows without significant loss.\n\n\nRemoving Missing Data\n\n\n \n Python\n Copy\n \n df = df.dropna()\n\n\n\n This removes all rows containing missing values. Be cautious \u2013 doing so without inspection may unintentionally remove important or rare data points.\n\n\n5. Filtering and Subsetting Data\n\n\n How can we create a smaller dataset that only includes Rock songs? We filter by genre!\n\n\nFiltering by Genre\n\n\n This line checks each row in the genre column to see if it equals \"Rock.\" The matching rows are kept and saved in a new DataFrame called rock_songs.\n\n\n\n Remember: use two equals signs, ==, to check for equality. A single equals sign, =, is used to assign a value.\n\n\n\n \n Python\n Copy\n \n rock_songs = df[df['genre'] == 'Rock']\nrock_songs.head()\n\n\nFiltering by a Numerical Threshold\n\n\n Now let\u2019s create a dataset that only includes songs with an energy level higher than 0.8. Energy is a numeric variable, meaning it is stored as a number. Because of this, we can use comparison symbols like greater than > or less than < to filter the data.\n\n\n\n \n Python\n Copy\n \n high_energy = df[df['energy'] > 0.8]\nhigh_energy.head()\n\n\n\n high_energy keeps only songs with an energy level above 0.8.\n\n\nFiltering by Multiple Conditions\n\n\n What if we wanted to look at songs that are both Rock songs and high energy?\n\n\n\n \n Python\n Copy\n \n high_energy_rock = df[(df['genre'] == 'Rock') & (df['energy'] > 0.8)]\nhigh_energy_rock.head()\n\n\n\n This narrows the data to just the rows meeting both conditions: the genre must be \"Rock\" and the energy value must be greater than 0.8. The & symbol means \u201cand,\u201d so both conditions must be true for a song to be included. Each condition is placed inside parentheses to help Python understand the full filter.\n\n\n6. Analytical Questions and Insights\n\n\n Once the dataset is clean and well understood, we can begin exploring meaningful questions.\n\n\nWhat Are the Most Popular Songs?\n\n\n \n Python\n Copy\n \n top_10_popular = df.sort_values(by='popularity', ascending=False)\ntop_10_popular[['track_name', 'artist_name', 'popularity']].head(10)\n\n\n\n Sorting helps identify rankings within the dataset. Saving the result as a new variable makes it easier to reuse later.\n\n\nWhat Are the Most Danceable Songs?\n\n\n \n Python\n Copy\n \n top_5_danceable = df.sort_values(by='danceability', ascending=False)\ntop_5_danceable[['track_name', 'artist_name', 'danceability']].head(5)\n\n\nWhat Is the Average Danceability or Energy for Each Genre?\n\n\n Pandas\u2019 groupby() function allows us to calculate statistics for categories. Conceptually, the operation splits the data into groups, applies a summary function like mean, and recombines the results into a new table.\n\n\n\n \n Python\n Copy\n \n genre_analysis = df.groupby('genre')[['danceability', 'energy']].mean()\ngenre_analysis.head(10)\n\n\n\n This creates a profile of each genre, revealing patterns such as which genres tend to be more energetic or danceable.\n\n\nCounting Songs per Genre\n\n\n \n Python\n Copy\n \n df.groupby('genre').size()\n\n\n\n This shows which genres dominate the dataset.\n\n\n7. Visualizing Data\n\n\n Visualizations convert raw numbers into insights. They help you identify patterns that are not obvious from tables alone.\n\n\nBar Chart of Average Danceability by Genre\n\n\n \n Python\n Copy\n \n genre_analysis['danceability'].plot(\n kind='bar',\n title='Average Danceability by Genre',\n xlabel='Genre',\n ylabel='Danceability'\n)\nplt.xticks(rotation=75)\nplt.show()\n\n\n\n Bar charts are effective for comparing categories.\n\n\nScatter Plot of Energy vs Danceability\n\n\n \n Python\n Copy\n \n plt.figure(figsize=(8, 6))\nplt.scatter(df['energy'], df['danceability'], alpha=0.3)\nplt.title('Energy vs Danceability')\nplt.xlabel('Energy')\nplt.ylabel('Danceability')\nplt.show()\n\n\n\n Scatterplots help visualize relationships between two numerical variables. However, because this dataset is large, many points overlap on top of each other, making the chart difficult to interpret. When the points are too crowded, it becomes hard to distinguish individual songs or see whether there is a clear pattern between the variables.\n\n\nAddressing Overplotting with Hexbin Charts\n\n\n \n Python\n Copy\n \n df.plot.hexbin(\n x='energy',\n y='danceability',\n gridsize=25,\n cmap='Blues',\n figsize=(8, 6),\n sharex=False\n)\nplt.title('Density of Songs: Energy vs Danceability')\nplt.show()\n\n\n\n Hexbin charts reveal areas where many songs share similar values. The areas with the darker colors are the ones with the highest concentration.\n\n\n\n Another way to explore the relationship between energy and danceability is to group songs by energy level and calculate the average danceability for each group. This gives us a simpler view of the overall trend instead of showing every individual song.\n\n\n\n \n Python\n Copy\n \n # Calculate the average danceability for each energy group\navg_danceability = df.groupby('energy_level')['danceability'].mean()\n\n# Plot the results\navg_danceability.plot(kind='bar', figsize=(10, 6))\n\nplt.title('Average Danceability by Energy Level')\nplt.xlabel('Energy Level')\nplt.ylabel('Average Danceability')\nplt.show()\n\n\n8. Exploring Relationships Using Regression\n\n\n Regression analysis helps reveal underlying relationships between variables. In this case, we want to know whether a song\u2019s energy level predicts its danceability. While regression can be complex, Seaborn's lmplot() offers an accessible entry point: it visualizes trends without requiring mathematical background.\n\n\nBasic Regression Plot\n\n\n \n Python\n Copy\n \n sns.lmplot(\n data=df,\n x='energy',\n y='danceability',\n height=6,\n aspect=1.2\n)\nplt.title(\"Linear Regression: Energy vs Danceability\")\nplt.show()\n\n\nThe resulting chart should show:\n\n\n A fitted line that captures the overall trend\n A cloud of points representing the data\n A shaded confidence interval showing uncertainty\n\n\nUsing Sampling to Improve Visibility\n\n\n Because the dataset is large, sampling helps reduce visual clutter:\n\n\n\n \n Python\n Copy\n \n sample_df = df.sample(1000)\n\nsns.lmplot(\n data=sample_df,\n x='energy',\n y='danceability',\n scatter_kws={'color': 'pink'},\n height=6,\n aspect=1.2\n)\nplt.title(\"Regression Using a Sample of 1,000 Songs\")\nplt.show()\n\n\nComparing Regression Lines Across Genres\n\n\n This helps highlight whether the relationship between energy and danceability varies across musical styles:\n\n\n\n \n Python\n Copy\n \n sns.lmplot(\n data=df.sample(5000),\n x='energy',\n y='danceability',\n hue='genre',\n scatter_kws={'alpha': 0.2},\n height=7,\n aspect=1.3\n)\nplt.title(\"Energy vs Danceability Across Genres\")\nplt.show()\n\n\n\n Different slopes indicate different underlying patterns across genres.\n\n\n\n Observation\/note: The chart above includes many different genres, which makes it difficult to tell the colors and trend lines apart. To make the chart easier to read, we could use a sample of the data, focus on fewer genres, or choose a color palette with stronger contrast.\n\n\nExtracting Numerical Regression Results\n\n\n For a more technical summary:\n\n\n\n \n Python\n Copy\n \n from scipy.stats import linregress\n\nresult = linregress(df['energy'], df['danceability'])\nprint(result)\n\n\n\n This provides the slope, intercept, correlation, and statistical significance, also known as the p-value.\n\n\n9. Summary and Next Steps\n\n\n In this guide, you learned how to complete an end-to-end data analysis workflow in Python. This included:\n\n\n\n Importing and loading data\n Exploring structure and content\n Identifying and cleaning missing values\n Filtering subsets of data\n Computing descriptive statistics\n Comparing categories\n Creating visualizations\n Interpreting regression results\n\n\n\n You now have the tools to extend this analysis by exploring other Spotify features, testing more relationships, or even building predictive models!\n\n\n10. Glossary\n\n\n CSV file: a plain-text file that stores table-like data using rows and columns, with values separated by commas\n File path: the location of a file on your computer that Python uses to find and open the file\n Python notebook: a document that lets you combine Python code, written notes, and code output in one place\n Code cell: a section in a notebook where you write and run Python code\n Comment: any text following a hashtag symbol (#), which Python does not recognize as code\n Function: a pre-defined block of code designed to perform a specific task\n Variable: a name used to store information\n DataFrame: a table-like structure in pandas that organizes data into rows and columns\n Row: a horizontal entry in a DataFrame, usually representing one observation, such as one song\n Column: a vertical section in a DataFrame, usually representing one variable, such as genre, energy, or danceability\n DType: short for data type; it tells us what kind of information is stored in a column\n Object data: text or mixed character data, such as song titles, artist names, or genres\n Float data: numeric data with decimal places, such as 0.85 or 0.42\n Integer data: whole-number data without decimal places, such as 10, 85, or 100\n Numeric data: numbers that can be used for calculations, including integers and decimals\n Boolean data: data with only two possible values, True or False\n Indexing: accessing specific rows, columns, or values in a DataFrame using square brackets\n Filtering: selecting only the rows that meet a specific condition\n Subsetting: creating a smaller version of a dataset by selecting specific rows or columns\n Condition: a rule that Python checks, such as whether a song\u2019s genre is Rock or whether its energy is greater than 0.8\n Missing value: a blank or empty value in a dataset where information is not available\n Overplotting: when too many points overlap in a chart, making it difficult to distinguish individual data points or patterns\n Hexbin chart: a chart that groups nearby points into hexagon-shaped areas and uses color to show where the data is most concentrated\n Regression line: a line that shows the general trend or relationship between two numerical variables\n Sample: a smaller portion of a dataset used to make analysis or visualization easier\n\n\n"}]}}