diff --git a/.DS_Store b/.DS_Store index e77f056..5db93b9 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/_quarto.yml b/_quarto.yml index 1803fc7..3aab25c 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -18,17 +18,35 @@ website: style: "floating" search: true contents: - - section: "Welcome!" + - section: "Welcome!" # TO ADD a description for welcome contents: - - welcome.qmd + - welcome.qmd # TO MODIFY - text: "---" - - section: "Topic 1" + - section: "1. Data Collection and Preprocessing" # contents: - project1.qmd - text: "---" - - section: "Topic 2" + - section: "2. Data Visualization Techniques" contents: - project2.qmd + - section: "3. Interactive and Web-based Applications" # + contents: + - project3.qmd + - text: "---" + - section: "4. Outside of R" + contents: + - project7.qmd + - section: "5. Parameter Analysis of Visualization Techniques" # + contents: + - project4.qmd + - text: "---" + - section: "6. Programming Techniques and Tools" + contents: + - project5.qmd + - section: "7. Statistical Analysis and Modelling" # + contents: + - project6.qmd + - text: "---" page-footer: right: "Built with [Quarto](https://quarto.org/)" left: "© Copyright 2023" diff --git a/automatic-script.md b/automatic-script.md new file mode 100644 index 0000000..968cc7d --- /dev/null +++ b/automatic-script.md @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "links = pd.read_csv('links-spreadsheet.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "109\n" + ] + } + ], + "source": [ + "print(len(links.index))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df = links[['Section','Links','Main Category','Sub Category','Title']]\n", + "df = df.dropna(subset=['Main Category'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df.head(3)\n", + "category_list = list(set(df['Main Category'].to_list()))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Interactive and Web-based Applications', 'Programming Techniques and Tools', 'Parameter Analysis of Visualization Techniques', 'Data Visualization Techniques', 'Data Collection and Preprocessing', 'Outside of R', 'Statistical Analysis and Modelling']\n" + ] + } + ], + "source": [ + "print(category_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df1 = df[df[\"Main Category\"] == \"Data Collection and Preprocessing\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "df1.head(10)\n", + "subcategory_list1 = list(set(df1['Sub Category'].to_list()))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Miscellaneous']\n" + ] + } + ], + "source": [ + "print(subcategory_list1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "category_list = sorted(category_list)\n", + "category_list.append(category_list.pop(category_list.index('Outside of R')))\n", + "\n", + "for cat_nb in range(len(category_list)):\n", + " main_category = category_list[cat_nb]\n", + " df1 = df[df[\"Main Category\"] == main_category]\n", + "\n", + " with open(f'project{cat_nb+1}.qmd', 'w') as f: #projecti.qmd\n", + " link_nb = 1\n", + " f.write(\"---\\n\")\n", + " f.write(f\"title: \\\"{main_category}\\\"\\n\")\n", + " f.write(\"---\\n\")\n", + "\n", + " subcategory_list1 = list(set(df1['Sub Category'].to_list()))\n", + "\n", + " # try:\n", + " subcategory_list1.append(subcategory_list1.pop(subcategory_list1.index('Miscellaneous')))\n", + " \n", + "\n", + " for sub_nb, subcategory in enumerate(subcategory_list1):\n", + " df1_sub = df1[df1[\"Sub Category\"] == subcategory]\n", + " if len(subcategory_list1) > 1:\n", + " f.write(f\"# Subcategory {sub_nb + 1}: {subcategory}\\n\")\n", + " for video_nb in range(len(df1_sub['Title'].to_list())):\n", + " # print(len(df1_sub['Title'].to_list()))\n", + " title = df1_sub['Title'].to_list()\n", + " section = df1_sub['Section'].to_list()\n", + " links = df1_sub['Links'].to_list()\n", + " f.write(f\"[{link_nb}. {title[video_nb]} ({section[video_nb]})]({links[video_nb]})\\n\\n\")\n", + " link_nb += 1\n", + " f.write(\"\\n\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df1_sub['Title'].to_list())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "web-scraping", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/automatic-script.html b/docs/automatic-script.html new file mode 100644 index 0000000..b68b600 --- /dev/null +++ b/docs/automatic-script.html @@ -0,0 +1,507 @@ + + + + + + + + + +EDAV Fall 2023 Community Contribution – automatic-script + + + + + + + + + + + + + + + + + + + + + + + + + + +
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{ “cells”: [ { “cell_type”: “code”, “execution_count”: 11, “metadata”: {}, “outputs”: [], “source”: [ “import pandas as pd”, “import numpy as np” ] }, { “cell_type”: “code”, “execution_count”: 12, “metadata”: {}, “outputs”: [], “source”: [ “links = pd.read_csv(‘links-spreadsheet.csv’)” ] }, { “cell_type”: “code”, “execution_count”: 13, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “109” ] } ], “source”: [ “print(len(links.index))” ] }, { “cell_type”: “code”, “execution_count”: 14, “metadata”: {}, “outputs”: [], “source”: [ “df = links[[‘Section’,‘Links’,‘Main Category’,‘Sub Category’,‘Title’]]”, “df = df.dropna(subset=[‘Main Category’])” ] }, { “cell_type”: “code”, “execution_count”: 15, “metadata”: {}, “outputs”: [], “source”: [ “df.head(3)”, “category_list = list(set(df[‘Main Category’].to_list()))” ] }, { “cell_type”: “code”, “execution_count”: 16, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “[‘Interactive and Web-based Applications’, ‘Programming Techniques and Tools’, ‘Parameter Analysis of Visualization Techniques’, ‘Data Visualization Techniques’, ‘Data Collection and Preprocessing’, ‘Outside of R’, ‘Statistical Analysis and Modelling’]” ] } ], “source”: [ “print(category_list)” ] }, { “cell_type”: “code”, “execution_count”: 17, “metadata”: {}, “outputs”: [], “source”: [ “df1 = df[df["Main Category"] == "Data Collection and Preprocessing"]” ] }, { “cell_type”: “code”, “execution_count”: 18, “metadata”: {}, “outputs”: [], “source”: [ “df1.head(10)”, “subcategory_list1 = list(set(df1[‘Sub Category’].to_list()))” ] }, { “cell_type”: “code”, “execution_count”: 19, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “[‘Miscellaneous’]” ] } ], “source”: [ “print(subcategory_list1)” ] }, { “cell_type”: “code”, “execution_count”: 20, “metadata”: {}, “outputs”: [], “source”: [ “category_list = sorted(category_list)”, “category_list.append(category_list.pop(category_list.index(‘Outside of R’)))”, “”, “for cat_nb in range(len(category_list)):”, ” main_category = category_list[cat_nb]“,” df1 = df[df["Main Category"] == main_category]“,”“,” with open(f’project{cat_nb+1}.qmd’, ‘w’) as f: #projecti.qmd“,” link_nb = 1“,” f.write("—\n")“,” f.write(f"title: \"{main_category}\"\n")“,” f.write("—\n")“,”“,” subcategory_list1 = list(set(df1[‘Sub Category’].to_list()))“,”“,” # try:“,” subcategory_list1.append(subcategory_list1.pop(subcategory_list1.index(‘Miscellaneous’)))“,” “,”“,” for sub_nb, subcategory in enumerate(subcategory_list1):“,” df1_sub = df1[df1["Sub Category"] == subcategory]“,” if len(subcategory_list1) > 1:“,” f.write(f"# Subcategory {sub_nb + 1}: {subcategory}\n")“,” for video_nb in range(len(df1_sub[‘Title’].to_list())):“,” # print(len(df1_sub[‘Title’].to_list()))“,” title = df1_sub[‘Title’].to_list()“,” section = df1_sub[‘Section’].to_list()“,” links = df1_sub[‘Links’].to_list()“,” f.write(f"{link_nb}. {title[video_nb]} ({section[video_nb]})\n\n")“,” link_nb += 1“,” f.write("\n")“,” ” ] }, { “cell_type”: “code”, “execution_count”: 21, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “8” ] }, “execution_count”: 21, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [ “len(df1_sub[‘Title’].to_list())” ] } ], “metadata”: { “kernelspec”: { “display_name”: “web-scraping”, “language”: “python”, “name”: “python3” }, “language_info”: { “codemirror_mode”: { “name”: “ipython”, “version”: 3 }, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.11.5” }, “orig_nbformat”: 4 }, “nbformat”: 4, “nbformat_minor”: 2 }

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Project 1

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Welcome to EDAV 2023 community contribution video repository

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In here you’ll find the hard work of the MSDS 2023 students.

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+ + + + + \ No newline at end of file diff --git a/docs/project2.html b/docs/project2.html index 9ff4277..7ca91e7 100644 --- a/docs/project2.html +++ b/docs/project2.html @@ -6,9 +6,8 @@ - -EDAV Fall 2023 Community Contribution - Project 2 +EDAV Fall 2023 Community Contribution - Data Visualization Techniques + + + + + + + + + + + + + + + + + + + + + + + + + +
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Interactive and Web-based Applications

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Subcategory 1: Equisse Package

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1. Easy Data Analysis and Visualization Add-ins in R (Mon/Wed)

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2. Data Visualization R Add-ins: Equisse and GGThemeAssist (Tues/Thurs)

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Subcategory 2: Shiny App

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3. How to make an Interactive Dashboard with Shiny App on R (Mon/Wed)

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4. Introducing Shiny Package in R - Interactive Web Dashboard (Mon/Wed)

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5. RShiny: Overview, Reactives, How it helps (Mon/Wed)

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6. Introduction to Features of Shiny App (Tues/Thurs)

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7. Outlier Detection App with Shiny App (Tues/Thurs)

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8. Interactive Linear Regression Model App with Shiny App (Tues/Thurs)

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Subcategory 3: Miscellaneous

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9. Implementing Interactive Filters in R (Mon/Wed)

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10. Interactive Dashboard Creation in R (Mon/Wed)

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11. Understanding Interactive 2D Splines (Mon/Wed)

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12. Embedding Visualizations into Web Apps with R (Mon/Wed)

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13. Quick Interactive Data Visualization with GGplot and GGRapture (Tues/Thurs)

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+ + + + + \ No newline at end of file diff --git a/docs/project4.html b/docs/project4.html new file mode 100644 index 0000000..ab51a8a --- /dev/null +++ b/docs/project4.html @@ -0,0 +1,521 @@ + + + + + + + + + +EDAV Fall 2023 Community Contribution - Parameter Analysis of Visualization Techniques + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Parameter Analysis of Visualization Techniques

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1. QQ Plots and Sample Quantiles Analysis in R (Mon/Wed)

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2. Deep Dive in Hexagonal Grids (Tues/Thurs)

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+ + + + + \ No newline at end of file diff --git a/docs/project5.html b/docs/project5.html new file mode 100644 index 0000000..36695ed --- /dev/null +++ b/docs/project5.html @@ -0,0 +1,524 @@ + + + + + + + + + +EDAV Fall 2023 Community Contribution - Programming Techniques and Tools + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Programming Techniques and Tools

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1. Using R in Jupyter Notebook in VSCode and Latex (Mon/Wed)

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2. Project Management in R using RProject and Renv (Mon/Wed)

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3. How to run R and Python in the same Jupyter Notebook using rpy2 (Mon/Wed)

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4. Functional Programming in R (Tues/Thurs)

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5. Quick Tips on Speeding up Data Manipulation and Visualization in R (Tues/Thurs)

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+ + + + + \ No newline at end of file diff --git a/docs/project6.html b/docs/project6.html new file mode 100644 index 0000000..6f65289 --- /dev/null +++ b/docs/project6.html @@ -0,0 +1,540 @@ + + + + + + + + + +EDAV Fall 2023 Community Contribution - Statistical Analysis and Modelling + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Statistical Analysis and Modelling

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Subcategory 1: Time Series Analysis

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1. Time Series Analysis Using Forecast Library (Mon/Wed)

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2. Introduction to ARIMA, SARIMA and Parameters Selection (Tues/Thurs)

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3. Detecting Outliers in Time Series: ‘anomaly’, ‘tsoutliers’, and ‘checkresiduals’ (Tues/Thurs)

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Subcategory 2: Miscellaneous

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4. Sentiment Analysis in Tweets about Fast Fashion (Mon/Wed)

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5. Causal Inference with Sandwich Package in R (Mon/Wed)

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6. Caret Package for Machine Learning (Tues/Thurs)

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7. Correlation between Google keyword searches and stock prices via regression (Tues/Thurs)

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8. Modelling of Body Fat Percentage (Tues/Thurs)

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+ + + + + \ No newline at end of file diff --git a/docs/project7.html b/docs/project7.html new file mode 100644 index 0000000..f6d0b1a --- /dev/null +++ b/docs/project7.html @@ -0,0 +1,545 @@ + + + + + + + + + +EDAV Fall 2023 Community Contribution - Outside of R + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Outside of R

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Subcategory 1: Geospatial Data Visualization

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1. Relating Impact with Interactive Maps (Mon/Wed)

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2. A Complete Python Folium Tutorial (Mon/Wed)

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3. Map Data Visualization with Folium in Python (Mon/Wed)

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4. Interactive visualization of Earthquakes in Turkey in Python (Tues/Thurs)

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5. Reducing Spatial Data Redundancies (Tues/Thurs)

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Subcategory 2: Miscellaneous

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6. Streamlit Data Analysis App: StudyStats, a non-coding platform for beginners (Mon/Wed)

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7. Interactive Data Visualizations in Python (Mon/Wed)

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8. Visualizing Demographic Trends with Animated Maps (Mon/Wed)

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9. Exploratory Analysis and Data Visualization (Mon/Wed)

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10. Color Interpolation for Choropleth Maps in Data Wrapper (Tues/Thurs)

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11. Interactive visualization with Plotly in Python (Tues/Thurs)

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12. Efficient Excel Chart Management with VBA Macros (Tues/Thurs)

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13. Visualization of Graph Data with Neo4J in Python (Tues/Thurs)

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+ + + + + \ No newline at end of file diff --git a/docs/projects.html b/docs/projects.html index 9850d40..d6cf9f3 100644 --- a/docs/projects.html +++ b/docs/projects.html @@ -120,7 +120,7 @@ @@ -138,7 +138,7 @@ + +
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  • diff --git a/docs/search.json b/docs/search.json index 638dbb6..710c106 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1,17 +1,73 @@ [ { - "objectID": "index.html", - "href": "index.html", - "title": "Welcome", + "objectID": "welcome.html", + "href": "welcome.html", + "title": "Welcome!", + "section": "", + "text": "This webpage contains community contributions for Fall 2023 EDAV class at Columbia University.\nThere are 108 videos in total that are divided into 7 categories.\nHopefully this will help you prepare for your final project!\n\n\n\n Back to top" + }, + { + "objectID": "project3.html", + "href": "project3.html", + "title": "Interactive and Web-based Applications", + "section": "", + "text": "Subcategory 1: Equisse Package\n1. Easy Data Analysis and Visualization Add-ins in R (Mon/Wed)\n2. Data Visualization R Add-ins: Equisse and GGThemeAssist (Tues/Thurs)\n\n\nSubcategory 2: Shiny App\n3. How to make an Interactive Dashboard with Shiny App on R (Mon/Wed)\n4. Introducing Shiny Package in R - Interactive Web Dashboard (Mon/Wed)\n5. RShiny: Overview, Reactives, How it helps (Mon/Wed)\n6. Introduction to Features of Shiny App (Tues/Thurs)\n7. Outlier Detection App with Shiny App (Tues/Thurs)\n8. Interactive Linear Regression Model App with Shiny App (Tues/Thurs)\n\n\nSubcategory 3: Miscellaneous\n9. Implementing Interactive Filters in R (Mon/Wed)\n10. Interactive Dashboard Creation in R (Mon/Wed)\n11. Understanding Interactive 2D Splines (Mon/Wed)\n12. Embedding Visualizations into Web Apps with R (Mon/Wed)\n13. Quick Interactive Data Visualization with GGplot and GGRapture (Tues/Thurs)\n\n\n\n\n Back to top" + }, + { + "objectID": "project2.html", + "href": "project2.html", + "title": "Data Visualization Techniques", + "section": "", + "text": "Subcategory 1: Plot Types and Techniques\n1. Creating Scatter Plots with Boundaries in R (Mon/Wed)\n2. Interactive Heatmap Visualization in Montgomery County with R Shiny (Mon/Wed)\n3. HexMaps: Understanding the Y-axis (Mon/Wed)\n4. Sorting Boxplots and Barplots in R (Mon/Wed)\n5. Comparison between Geom_Mosaic and Vcd::Mosaic (Mon/Wed)\n6. Unemployment Heatmaps with R and Python (Mon/Wed)\n7. Comparison Between Population Pyramid and Violin Plot (Mon/Wed)\n8. Common Uses of Heatmap (Tues/Thurs)\n9. Heatmaps in R (Tues/Thurs)\n10. Interactive Scatterplots with Highcharter Package in R (Tues/Thurs)\n\n\nSubcategory 2: Interactive and Dynamic Visualizations\n11. Interactive Data Visualizations with Plotly in R (Mon/Wed)\n12. Enhancing Visualizations with Plotly Layout Features in Python (Mon/Wed)\n13. Creating Animations using gganimate (Mon/Wed)\n14. Creating Sankey Diagrams with networkD3 in R (Mon/Wed)\n15. Gene and Protein Interaction Networks with NetworkD3 in R (Mon/Wed)\n16. Animated Demographic Trend Maps with Census Data in R (Mon/Wed)\n17. Arrow Plots and Animations for Exploratory Data Analysis and Visualization (Tues/Thurs)\n\n\nSubcategory 3: Basic and Advanced Techniques\n18. Overlaying Facetted Histograms with Theoretical Normal Density Curves (Mon/Wed)\n19. Plotting theoretical normal distribution across facets using gg4x (Mon/Wed)\n20. Bubble and 3D Plot Techniques in R with Plotly (Mon/Wed)\n21. Unlocking Inclusive Data Visualization: Color-Blind Friendly Charts in R (Mon/Wed)\n22. Combining boxplots and ridgeline plots on one plot (Mon/Wed)\n23. EDA & Visualization on Ranking Method (Mon/Wed)\n24. Comparing Facet Functions: facet_wrap() vs facet_grid() (Mon/Wed)\n25. Generative Art Techniques in R (Mon/Wed)\n26. Creating the Droste Effect in R (Mon/Wed)\n27. 3 Stages of Aesthetic Evaluation in GGplot2 (Mon/Wed)\n28. How To Create a Color Palette from an Image (Mon/Wed)\n29. MBTI Data Analysis with Circular Visualization (Mon/Wed)\n30. How to draw normal curves while using ‘facet_wrap()’ in R (Tues/Thurs)\n31. Effective color ramps (Tues/Thurs)\n32. Creating 2D and 3D Visualizations with Rayshader (Tues/Thurs)\n33. Customizing Themes in R with GGthemr (Tues/Thurs)\n34. Plotting Waterfall Chart with R ggplot2 (Tues/Thurs)\n35. TrelliscopeJS for Interactive Data Visualization (Tues/Thurs)\n36. Correlation Matrices and Hierarchical Clustering in R (Tues/Thurs)\n37. Basic Table Creation in R (Tues/Thurs)\n38. Designing Bivariate Color Palettes for Choropleth Maps in R (Tues/Thurs)\n39. Deep Dive of Parallel Coordinate Plots in R (Tues/Thurs)\n\n\nSubcategory 4: Specialized Data Types Visualization\n40. Using WordClouds to analyze data (Mon/Wed)\n41. R Visualizations for Financial Data Analysis with Plotly (Mon/Wed)\n42. NFL Visualizations with Next Gen Stats (Mon/Wed)\n43. Personal Finance Case Study: Monthly Expense Analysis in R (Mon/Wed)\n44. Explaining tm and wordcloud2 Packages in R (Mon/Wed)\n45. Interactive Visualization of Geospatial Data with GGplot2 and Plotly (Tues/Thurs)\n46. How to use Geom_map in GGplot2 (Tues/Thurs)\n47. Using R to visualize expense (Tues/Thurs)\n48. Accident Density Mapping in NYC with Plotly Interactive Plots (Tues/Thurs)\n49. Add graphs Inside Markers on a Geographic Map with LeafLet (Tues/Thurs)\n50. Maps and Projections in R (Tues/Thurs)\n51. Interactive Time Series Data Visualization with Dygraphs Package (Tues/Thurs)\n52. Choropleth Maps in R (Tues/Thurs)\n\n\nSubcategory 5: Statistical and Quantitative Analysis\n53. Exploring Categorical Datasets in R (Mon/Wed)\n54. Quantile-Based Data Partitioning Methods in R with Cut2() (Mon/Wed)\n55. Effective Data Comparison on Different Scales (Mon/Wed)\n56. Jointplot with GGside for Bivariate Analysis (Tues/Thurs)\n57. Lattice Package for Univariate and Multivariate Analysis (Tues/Thurs)\n\n\nSubcategory 6: Miscellaneous\n58. Data Visualization with Emojis (Tues/Thurs)\n59. Formatting of R Output File and Changing Order of Graph Layers (Tues/Thurs)\n60. Graph Resizing in R Markdown (Tues/Thurs)\n\n\n\n\n Back to top" + }, + { + "objectID": "project1.html", + "href": "project1.html", + "title": "Data Collection and Preprocessing", + "section": "", + "text": "1. Understanding the grepl() function in R (Mon/Wed)\n2. Strategies for Handling Missing Data (Mon/Wed)\n3. Combining Sub-Categories in Dataframes in R with grepl() (Mon/Wed)\n4. Min-max Scaling in R (Mon/Wed)\n5. Web Scraping in R (Tues/Thurs)\n6. Working with Time Zone in R with Lubridate (Tues/Thurs)\n7. Tricks of data cleaning and visualization in R (Tues/Thurs)\n\n\n\n Back to top" + }, + { + "objectID": "project4.html", + "href": "project4.html", + "title": "Parameter Analysis of Visualization Techniques", + "section": "", + "text": "1. QQ Plots and Sample Quantiles Analysis in R (Mon/Wed)\n2. Deep Dive in Hexagonal Grids (Tues/Thurs)\n\n\n\n Back to top" + }, + { + "objectID": "project1_sample.html", + "href": "project1_sample.html", + "title": "Project 1", "section": "", - "text": "How to web scrape in R" + "text": "In here you’ll find the hard work of the MSDS 2023 students." }, { - "objectID": "index.html#getting-data", - "href": "index.html#getting-data", - "title": "Welcome", + "objectID": "project1_sample.html#welcome-to-edav-2023-community-contribution-video-repository", + "href": "project1_sample.html#welcome-to-edav-2023-community-contribution-video-repository", + "title": "Project 1", "section": "", - "text": "How to web scrape in R" + "text": "In here you’ll find the hard work of the MSDS 2023 students." + }, + { + "objectID": "project7.html", + "href": "project7.html", + "title": "Outside of R", + "section": "", + "text": "Subcategory 1: Geospatial Data Visualization\n1. Relating Impact with Interactive Maps (Mon/Wed)\n2. A Complete Python Folium Tutorial (Mon/Wed)\n3. Map Data Visualization with Folium in Python (Mon/Wed)\n4. Interactive visualization of Earthquakes in Turkey in Python (Tues/Thurs)\n5. Reducing Spatial Data Redundancies (Tues/Thurs)\n\n\nSubcategory 2: Miscellaneous\n6. Streamlit Data Analysis App: StudyStats, a non-coding platform for beginners (Mon/Wed)\n7. Interactive Data Visualizations in Python (Mon/Wed)\n8. Visualizing Demographic Trends with Animated Maps (Mon/Wed)\n9. Exploratory Analysis and Data Visualization (Mon/Wed)\n10. Color Interpolation for Choropleth Maps in Data Wrapper (Tues/Thurs)\n11. Interactive visualization with Plotly in Python (Tues/Thurs)\n12. Efficient Excel Chart Management with VBA Macros (Tues/Thurs)\n13. Visualization of Graph Data with Neo4J in Python (Tues/Thurs)\n\n\n\n\n Back to top" + }, + { + "objectID": "project6.html", + "href": "project6.html", + "title": "Statistical Analysis and Modelling", + "section": "", + "text": "Subcategory 1: Time Series Analysis\n1. Time Series Analysis Using Forecast Library (Mon/Wed)\n2. Introduction to ARIMA, SARIMA and Parameters Selection (Tues/Thurs)\n3. Detecting Outliers in Time Series: ‘anomaly’, ‘tsoutliers’, and ‘checkresiduals’ (Tues/Thurs)\n\n\nSubcategory 2: Miscellaneous\n4. Sentiment Analysis in Tweets about Fast Fashion (Mon/Wed)\n5. Causal Inference with Sandwich Package in R (Mon/Wed)\n6. Caret Package for Machine Learning (Tues/Thurs)\n7. Correlation between Google keyword searches and stock prices via regression (Tues/Thurs)\n8. Modelling of Body Fat Percentage (Tues/Thurs)\n\n\n\n\n Back to top" + }, + { + "objectID": "project5.html", + "href": "project5.html", + "title": "Programming Techniques and Tools", + "section": "", + "text": "1. Using R in Jupyter Notebook in VSCode and Latex (Mon/Wed)\n2. Project Management in R using RProject and Renv (Mon/Wed)\n3. How to run R and Python in the same Jupyter Notebook using rpy2 (Mon/Wed)\n4. Functional Programming in R (Tues/Thurs)\n5. Quick Tips on Speeding up Data Manipulation and Visualization in R (Tues/Thurs)\n\n\n\n Back to top" }, { "objectID": "projects.html#project-2", @@ -26,5 +82,19 @@ "title": "Projects", "section": "Project 3", "text": "Project 3" + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "Welcome!", + "section": "", + "text": "This webpage contains community contributions for Fall 2023 EDAV class at Columbia University.\nThere are 108 videos in total that are divided into 7 categories.\nHopefully this will help you prepare for your final project!\n\n\n\n Back to top" + }, + { + "objectID": "automatic-script.html", + "href": "automatic-script.html", + "title": "EDAV Fall 2023 Community Contribution", + "section": "", + "text": "{ “cells”: [ { “cell_type”: “code”, “execution_count”: 11, “metadata”: {}, “outputs”: [], “source”: [ “import pandas as pd”, “import numpy as np” ] }, { “cell_type”: “code”, “execution_count”: 12, “metadata”: {}, “outputs”: [], “source”: [ “links = pd.read_csv(‘links-spreadsheet.csv’)” ] }, { “cell_type”: “code”, “execution_count”: 13, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “109” ] } ], “source”: [ “print(len(links.index))” ] }, { “cell_type”: “code”, “execution_count”: 14, “metadata”: {}, “outputs”: [], “source”: [ “df = links[[‘Section’,‘Links’,‘Main Category’,‘Sub Category’,‘Title’]]”, “df = df.dropna(subset=[‘Main Category’])” ] }, { “cell_type”: “code”, “execution_count”: 15, “metadata”: {}, “outputs”: [], “source”: [ “df.head(3)”, “category_list = list(set(df[‘Main Category’].to_list()))” ] }, { “cell_type”: “code”, “execution_count”: 16, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “[‘Interactive and Web-based Applications’, ‘Programming Techniques and Tools’, ‘Parameter Analysis of Visualization Techniques’, ‘Data Visualization Techniques’, ‘Data Collection and Preprocessing’, ‘Outside of R’, ‘Statistical Analysis and Modelling’]” ] } ], “source”: [ “print(category_list)” ] }, { “cell_type”: “code”, “execution_count”: 17, “metadata”: {}, “outputs”: [], “source”: [ “df1 = df[df[\"Main Category\"] == \"Data Collection and Preprocessing\"]” ] }, { “cell_type”: “code”, “execution_count”: 18, “metadata”: {}, “outputs”: [], “source”: [ “df1.head(10)”, “subcategory_list1 = list(set(df1[‘Sub Category’].to_list()))” ] }, { “cell_type”: “code”, “execution_count”: 19, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “[‘Miscellaneous’]” ] } ], “source”: [ “print(subcategory_list1)” ] }, { “cell_type”: “code”, “execution_count”: 20, “metadata”: {}, “outputs”: [], “source”: [ “category_list = sorted(category_list)”, “category_list.append(category_list.pop(category_list.index(‘Outside of R’)))”, “”, “for cat_nb in range(len(category_list)):”, ” main_category = category_list[cat_nb]“,” df1 = df[df[\"Main Category\"] == main_category]“,”“,” with open(f’project{cat_nb+1}.qmd’, ‘w’) as f: #projecti.qmd“,” link_nb = 1“,” f.write(\"—\\n\")“,” f.write(f\"title: \\\"{main_category}\\\"\\n\")“,” f.write(\"—\\n\")“,”“,” subcategory_list1 = list(set(df1[‘Sub Category’].to_list()))“,”“,” # try:“,” subcategory_list1.append(subcategory_list1.pop(subcategory_list1.index(‘Miscellaneous’)))“,” “,”“,” for sub_nb, subcategory in enumerate(subcategory_list1):“,” df1_sub = df1[df1[\"Sub Category\"] == subcategory]“,” if len(subcategory_list1) > 1:“,” f.write(f\"# Subcategory {sub_nb + 1}: {subcategory}\\n\")“,” for video_nb in range(len(df1_sub[‘Title’].to_list())):“,” # print(len(df1_sub[‘Title’].to_list()))“,” title = df1_sub[‘Title’].to_list()“,” section = df1_sub[‘Section’].to_list()“,” links = df1_sub[‘Links’].to_list()“,” f.write(f\"{link_nb}. {title[video_nb]} ({section[video_nb]})\\n\\n\")“,” link_nb += 1“,” f.write(\"\\n\")“,” ” ] }, { “cell_type”: “code”, “execution_count”: 21, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “8” ] }, “execution_count”: 21, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [ “len(df1_sub[‘Title’].to_list())” ] } ], “metadata”: { “kernelspec”: { “display_name”: “web-scraping”, “language”: “python”, “name”: “python3” }, “language_info”: { “codemirror_mode”: { “name”: “ipython”, “version”: 3 }, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.11.5” }, “orig_nbformat”: 4 }, “nbformat”: 4, “nbformat_minor”: 2 }\n\n\n\n Back to top" } ] \ No newline at end of file diff --git a/docs/welcome.html b/docs/welcome.html index 02d7f9e..28eed3f 100644 --- a/docs/welcome.html +++ b/docs/welcome.html @@ -118,7 +118,7 @@ @@ -136,7 +136,7 @@ + +
  • + + +
  • + + +
  • @@ -179,6 +267,8 @@

    Welcome!

    This webpage contains community contributions for Fall 2023 EDAV class at Columbia University.

    +

    There are 108 videos in total that are divided into 7 categories.

    +

    Hopefully this will help you prepare for your final project!

    diff --git a/index.qmd b/index.qmd index 32ed34a..8062930 100644 --- a/index.qmd +++ b/index.qmd @@ -1,24 +1,17 @@ --- -title: "Welcome" +title: "Welcome!" # about: # id: me # template: broadside # image: img/profile.png # github-repo: jtr13/edav2023 -description: This webpage contains community contributions for Fall 2023 EDAV class at Columbia University. ---- - -:::{#me} - -## Getting Data -[How to web scrape in R](https://www.youtube.com/watch?v=bAlfTcX2Q8Y) - -## Education +--- -## Experience +This webpage contains community contributions for Fall 2023 EDAV class at Columbia University. -::: +There are 108 videos in total that are divided into 7 categories. +Hopefully this will help you prepare for your final project! diff --git a/links-spreadsheet.csv b/links-spreadsheet.csv new file mode 100644 index 0000000..7b4e3be --- /dev/null +++ b/links-spreadsheet.csv @@ -0,0 +1,112 @@ +Section,Links,Title (old),Subcategory (old),Main Category (old),Title,Subcategory (old),Main Category,Sub Category,descriptions,,,,,new unique subcategories,new unique main categories,,old unique subcategories,old unique main categories +Mon/Wed,https://youtu.be/gsCIMVdy-Kw,"Streamlit Data Analysis App: StudyStats, a non-coding platform for beginners +",Data Visualization Tools,Data Visualization Techniques,"Streamlit Data Analysis App: StudyStats, a non-coding platform for beginners +",Miscellaneous,Outside of R,Miscellaneous,,,,,,Miscellaneous,Outside of R,,Data Visualization Tools,Data Visualization Techniques +Mon/Wed,https://youtu.be/GPHgiqD2XfE,Using WordClouds to analyze data,Word Cloud Visualization in R,Data Visualization Techniques,Using WordClouds to analyze data,Text Data Analysis,Data Visualization Techniques,Specialized Data Types Visualization,,,,,,Text Data Analysis,Data Visualization Techniques,,Word Cloud Visualization in R,Programming Techniques and Tools +Mon/Wed,https://www.youtube.com/watch?v=t5iv84vwrxk,Understanding the grepl() function in R,R Programming Basics,Programming Techniques and Tools,Understanding the grepl() function in R,Miscellaneous,Data Collection and Preprocessing,Miscellaneous,,,,,,Shiny App,Data Collection and Preprocessing,,R Programming Basics,Interactive and Web-based Applications +Mon/Wed,https://youtu.be/UG6QibAflRs,How to make an Interactive Dashboard with Shiny App on R,Shiny App Development,Interactive and Web-based Applications,How to make an Interactive Dashboard with Shiny App on R,Shiny App,Interactive and Web-based Applications,Shiny App,,,,,,Faceting,Interactive and Web-based Applications,,Shiny App Development,Statistical Analysis and Modelling +Mon/Wed,https://www.loom.com/share/0ba371f62d884042bdf1288d1fca926e,Implementing Filters in Software Engineering with RStudio,Data Filtering and Cleaning,Programming Techniques and Tools,Implementing Interactive Filters in R,Miscellaneous,Interactive and Web-based Applications,Miscellaneous,,,,,,Geospatial Data Visualization,Programming Techniques and Tools,,Data Filtering and Cleaning,Specific Software and Packages +Mon/Wed,https://www.youtube.com/watch?v=9wtvJz82oSw,Overlaying Facetted Histograms with Theoretical Normal Density Curves,Data Visualization Techniques,Data Visualization Techniques,Overlaying Facetted Histograms with Theoretical Normal Density Curves,Faceting,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,Scatterplots,Statistical Analysis and Modelling,,Data Visualization Techniques,Data Cleaning and Imputation +Mon/Wed,https://youtu.be/UT_-BeZl2o4,Relating Impact with Interactive Maps,Geospatial Data Visualization,Data Visualization Techniques,Relating Impact with Interactive Maps,Geospatial Data Visualization,Outside of R,Geospatial Data Visualization,,,,,,Plotly Interactive Visualizations,Parameter Analysis of Visualization Techniques,,Geospatial Data Visualization,Parameter Analysis of Visualization Techniques +Mon/Wed,https://youtu.be/B5anK7XBXrg,Productivity Tips in R Programming (e.g. VSCode and LaTeX),R Programming Tips,Programming Techniques and Tools,Using R in Jupyter Notebook in VSCode and Latex,Miscellaneous,Programming Techniques and Tools,Miscellaneous,,,,,,Time Series Analysis,,,R Programming Tips, +Mon/Wed,https://youtu.be/dCl9tISXSSQ,Techniques for Creating Scatter Plots with Boundaries in R,Scatter Plots in R,Data Visualization Techniques,Creating Scatter Plots with Boundaries in R,Scatterplots,Data Visualization Techniques,Plot Types and Techniques,,,,,,3D Visualizations,,,Scatter Plots in R, +Mon/Wed,https://www.youtube.com/watch?v=FIr5wMAn2ow,Introduction to Shiny Library for Interactive Web Dashboards,Shiny App Development,Interactive and Web-based Applications,Introducing Shiny Package in R - Interactive Web Dashboard,Shiny App,Interactive and Web-based Applications,Shiny App,,,,,,Heatmap,,,Plotly Data Visualizations, +Mon/Wed,https://youtu.be/87ZuiR7SrfA,Dynamic Data Visualizations with Plotly in R,Plotly Data Visualizations,Data Visualization Techniques,Interactive Data Visualizations with Plotly in R,Plotly Interactive Visualizations,Data Visualization Techniques,Interactive and Dynamic Visualizations,,,,,,Categorical Data Visualization,,,Advanced Data Visualization, +Mon/Wed,https://youtu.be/fyHNWSkcYpk,Normal Distribution Plotting across Facets using gg4x,Advanced Data Visualization,Data Visualization Techniques,Plotting theoretical normal distribution across facets using gg4x,Faceting,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,Data Visualization Aesthetics,,,Plotly Layout Customization, +Mon/Wed,https://www.youtube.com/watch?v=gM8yz6P7V0A,Enhancing Visualizations with Plotly Layout Features in Python,Plotly Layout Customization,Data Visualization Techniques,Enhancing Visualizations with Plotly Layout Features in Python,Plotly Interactive Visualizations,Data Visualization Techniques,Interactive and Dynamic Visualizations,,,,,,Animated Data Visualization,,,Time Series Analysis, +Mon/Wed,https://youtu.be/iCt8uBjFEjo,Time Series Analysis with Forecast Library,Time Series Analysis,Statistical Analysis and Modelling,Time Series Analysis Using Forecast Library,Time Series Analysis,Statistical Analysis and Modelling,Time Series Analysis,,,,,,Basic Data Visualizations,,,Interactive Dashboard, +Mon/Wed,https://youtu.be/szwG8L-Eyc4,Interactive Dashboard Creation in R,Interactive Dashboard,Interactive and Web-based Applications,Interactive Dashboard Creation in R,Miscellaneous,Interactive and Web-based Applications,Miscellaneous,,,,,,Financial Data Analysis,,,Python Folium Library, +Mon/Wed,https://youtu.be/8Z1me5ebCUw,"RShiny: Overview, Reactives, How it helps",Shiny App Development,Interactive and Web-based Applications,"RShiny: Overview, Reactives, How it helps",Shiny App,Interactive and Web-based Applications,Shiny App,,,,,,Hexmap,,,R Project Management, +Mon/Wed,https://www.youtube.com/watch?v=Ob0fhtDnFus,Comprehensive Python Folium Tutorial,Python Folium Library,Specific Software and Packages,A Complete Python Folium Tutorial,Geospatial Data Visualization,Outside of R,Geospatial Data Visualization,,,,,,Sports Data Visualization,,,Text Data Analysis, +Mon/Wed,https://www.youtube.com/watch?v=kBkpIck87Ys,Project Management in R using RProject and Renv,R Project Management,Programming Techniques and Tools,Project Management in R using RProject and Renv,Miscellaneous,Programming Techniques and Tools,Miscellaneous,,,,,,Boxplots and Barplots,,,Data Analysis Techniques, +Mon/Wed,https://youtu.be/xk7L7xJhOyQ,Sentiment Analysis in Tweets about Fast Fashion,Text Data Analysis,Statistical Analysis and Modelling,Sentiment Analysis in Tweets about Fast Fashion,Miscellaneous,Statistical Analysis and Modelling,Miscellaneous,,,,,,Equisse Package,,,3D Data Visualization, +Mon/Wed,https://youtu.be/Nm2oT09uSA0,Understanding Interactive 2D Splines and Their Applications,Data Analysis Techniques,Data Visualization Techniques,Understanding Interactive 2D Splines,Miscellaneous,Interactive and Web-based Applications,Miscellaneous,,,,,,Creative Data Visualization,,,Heatmap Usage, +Mon/Wed,https://youtu.be/tpl4TEVOHCI,Bubble and 3D Plot Techniques in R,3D Data Visualization,Data Visualization Techniques,Bubble and 3D Plot Techniques in R with Plotly,3D Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,Mosaic Plots,,,Categorical Data Visualization, +Mon/Wed,https://www.youtube.com/watch?v=nNI4SeSLgN0,Interactive Heatmap Visualization in Montgomery County with R Shiny,Heatmap Usage,Data Visualization Techniques,Interactive Heatmap Visualization in Montgomery County with R Shiny,Heatmap,Data Visualization Techniques,Plot Types and Techniques,,,,,,Quantile Techniques,,,Data Visualization Accessibility, +Mon/Wed,https://youtu.be/s5ehRahj2pM?feature=shared,Exploring Categorical Datasets in R,Categorical Data Visualization,Data Visualization Techniques,Exploring Categorical Datasets in R,Categorical Data Visualization,Data Visualization Techniques,Statistical and Quantitative Analysis,,,,,,Distribution Comparison Techniques,,,Animated Data Visualization, +Mon/Wed,https://youtu.be/PudGAzRal_4,Unlocking Inclusive Data Visualization: Color-Blind Friendly Charts in R,Data Visualization Accessibility,Data Visualization Techniques,Unlocking Inclusive Data Visualization: Color-Blind Friendly Charts in R,Data Visualization Aesthetics,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,Sankey Diagrams,,,Exploratory Data Analysis, +Mon/Wed,https://www.youtube.com/watch?v=USw2AECFIkw,Creating animations with gganimate in R,Animated Data Visualization,Data Visualization Techniques,Creating Animations using gganimate,Animated Data Visualization,Data Visualization Techniques,Interactive and Dynamic Visualizations,Introduces ways of visualizing time series data through animations.,,,,,Interactive Networks,,,Data Cleaning and Imputation, +Mon/Wed,https://www.youtube.com/watch?v=40MokoiI3Aw,Combining boxplots and ridgeline plots on one plot,Data Visualization Techniques,Data Visualization Techniques,Combining boxplots and ridgeline plots on one plot,Basic Data Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,Violin Plot,,,Faceting, +Mon/Wed,https://youtu.be/aG7t5I2zk9E,EDA & Visualization on Ranking Method,Exploratory Data Analysis,Data Visualization Techniques,EDA & Visualization on Ranking Method,Basic Data Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,Advanced Data Visualizations,,,Plotly Interactive Financial Data Visualization, +Mon/Wed,https://youtu.be/A7GkdSjLL_g,Strategies for Handling Missing Data,Data Cleaning and Imputation,Data Cleaning and Imputation,Strategies for Handling Missing Data,Miscellaneous,Data Collection and Preprocessing,Miscellaneous,,,,,,Multivariate Analysis,,,Hexmap, +Mon/Wed,https://www.youtube.com/watch?v=6k39xguz4js,Comparing Facet Functions: facet_wrap() vs facet_grid(),Faceting,Data Visualization Techniques,Comparing Facet Functions: facet_wrap() vs facet_grid(),Faceting,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,Time Series Data Visualization,,,Sports Data Visualization, +Mon/Wed,https://youtu.be/tbmGq7_TM-4,R Visualizations for Financial Data Analysis,Plotly Interactive Financial Data Visualization,Data Visualization Techniques,R Visualizations for Financial Data Analysis with Plotly,Financial Data Analysis,Data Visualization Techniques,Specialized Data Types Visualization,,,,,,Parallel Coordinates,,,Boxplots and Barplots, +Mon/Wed,https://youtu.be/mslzLw5v738,HexMaps: Understanding the Y-axis,Hexmap,Data Visualization Techniques,HexMaps: Understanding the Y-axis,Hexmap,Data Visualization Techniques,Plot Types and Techniques,,,,,,R Output Formatting,,,Helpful Add-in Packages for Quick Visualization, +Mon/Wed,https://youtu.be/xs0cGbjoseE,NFL Visualizations with Next Gen Stats,Sports Data Visualization,Data Visualization Techniques,NFL Visualizations with Next Gen Stats,Sports Data Visualization,Data Visualization Techniques,Specialized Data Types Visualization,,,,,,,,,Creative Data Visualization, +Mon/Wed,https://youtu.be/3RkNs3cyHlU,Sorting Boxplots and Barplots in R,Boxplots and Barplots,Data Visualization Techniques,Sorting Boxplots and Barplots in R,Boxplots and Barplots,Data Visualization Techniques,Plot Types and Techniques,,,,,,,,,Web Application Development, +Mon/Wed,https://youtu.be/IDy_6UA9jak,Exploring Folium Applications,Python Folium Library,Specific Software and Packages,Map Data Visualization with Folium in Python,Geospatial Data Visualization,Outside of R,Geospatial Data Visualization,,,,,,,,,Mosaic Plots, +Mon/Wed,https://www.youtube.com/watch?v=gIPY_5m-vRk,Easy Data Analysis and Visualization Add-ins in R,Helpful Add-in Packages for Quick Visualization,Data Visualization Techniques,Easy Data Analysis and Visualization Add-ins in R,Equisse Package,Interactive and Web-based Applications,Equisse Package,,,,,,,,,QQ plots, +Mon/Wed,https://youtu.be/ZAyUFSky0N8,Generative Art Techniques in R,Creative Data Visualization,Data Visualization Techniques,Generative Art Techniques in R,Creative Data Visualization,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,,,,Advanced R Programming, +Mon/Wed,https://www.youtube.com/watch?v=8j3ttwrtASE,Embedding Visualizations into Web Apps with R,Web Application Development,Interactive and Web-based Applications,Embedding Visualizations into Web Apps with R,Miscellaneous,Interactive and Web-based Applications,Miscellaneous,,,,,,,,,Data Partitioning Techniques, +Mon/Wed,https://drive.google.com/file/d/15vGCwVp3WpW1bFwdP_prFg1zRozqYERK/view?usp=sharing,Comparison between Geom_Mosaic and Vcd::Mosaic,Mosaic Plots,Data Visualization Techniques,Comparison between Geom_Mosaic and Vcd::Mosaic,Mosaic Plots,Data Visualization Techniques,Plot Types and Techniques,,,,,,,,,Distribution Comparison Techniques, +Mon/Wed,https://www.youtube.com/watch?v=RKcvdK5DNZU&ab_channel=MartinFluder,QQ Plots and Sample Quantiles Analysis in R,QQ plots,Parameter Analysis of Visualization Techniques,QQ Plots and Sample Quantiles Analysis in R,Miscellaneous,Parameter Analysis of Visualization Techniques,Miscellaneous,,,,,,,,,Python Data Visualization Library, +Mon/Wed,https://youtu.be/r7xCvHttgi0,Understanding the Sandwich Package,Advanced R Programming,Programming Techniques and Tools,Causal Inference with Sandwich Package in R,Miscellaneous,Statistical Analysis and Modelling,Miscellaneous,,,,,,,,,Sankey Diagrams in R, +Mon/Wed,https://youtu.be/BmxpApzmpqE,Quantile-Based Data Partitioning Methods in R,Data Partitioning Techniques,Data Visualization Techniques,Quantile-Based Data Partitioning Methods in R with Cut2(),Quantile Techniques,Data Visualization Techniques,Statistical and Quantitative Analysis,,,,,,,,,Dataframe Manipulation in R, +Mon/Wed,https://www.youtube.com/watch?v=LG10EWIpCD4,Effective Data Comparison on Different Scales,Distribution Comparison Techniques,Data Visualization Techniques,Effective Data Comparison on Different Scales,Distribution Comparison Techniques,Data Visualization Techniques,Statistical and Quantitative Analysis,,,,,,,,,NetworkD3 in R, +Mon/Wed,https://youtu.be/gWsKyyakkrk,Interactive Python Library for Data Visualization,Python Data Visualization Library,Specific Software and Packages,Interactive Data Visualizations in Python,Miscellaneous,Outside of R,Miscellaneous,,,,,,,,,Data Scaling Techniques, +Mon/Wed,https://youtu.be/n43RMdALxvc,Creating Sankey Diagrams with networkD3 in R,Sankey Diagrams in R,Data Visualization Techniques,Creating Sankey Diagrams with networkD3 in R,Sankey Diagrams,Data Visualization Techniques,Interactive and Dynamic Visualizations,,,,,,,,,Financial Data Analysis, +Mon/Wed,https://youtu.be/A8IqvqnkIC4,Combining Sub-Categories in Dataframes in R,Dataframe Manipulation in R,Programming Techniques and Tools,Combining Sub-Categories in Dataframes in R with grepl(),Miscellaneous,Data Collection and Preprocessing,Miscellaneous,,,,,,,,,Violin Plot, +Mon/Wed,https://youtu.be/nI1XMoUHn0o,Creating the Droste Effect in R,Creative Data Visualization,Data Visualization Techniques,Creating the Droste Effect in R,Creative Data Visualization,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,,,,Data Visualization Aesthetics, +Mon/Wed,https://www.youtube.com/watch?v=FdzTsrCvdkk,NetworkD3 for Gene and Protein Interaction Networks in R,NetworkD3 in R,Data Visualization Techniques, Gene and Protein Interaction Networks with NetworkD3 in R,Interactive Networks,Data Visualization Techniques,Interactive and Dynamic Visualizations,,,,,,,,,R and Python Integration, +Mon/Wed,https://youtu.be/Oi0P5CT5hS0,"Scaling Techniques: Standardized, Min-Max, and Normalization in R",Data Scaling Techniques,Data Visualization Techniques,Min-max Scaling in R,Miscellaneous,Data Collection and Preprocessing,Miscellaneous,,,,,,,,,Color Palette Design, +Mon/Wed,https://youtu.be/BVLzry9EBGY,Unemployment Heatmaps with R and Python,Heatmap Usage,Data Visualization Techniques,Unemployment Heatmaps with R and Python,Heatmap,Data Visualization Techniques,Plot Types and Techniques,,,,,,,,,Data Visualization in Python, +Mon/Wed,https://youtu.be/Q06kLBNwGc8,Personal Finance Case Study: Monthly Expense Analysis in R,Financial Data Analysis,Data Visualization Techniques,Personal Finance Case Study: Monthly Expense Analysis in R,Financial Data Analysis,Data Visualization Techniques,Specialized Data Types Visualization,,,,,,,,,Circular Data Visualization, +Mon/Wed,https://youtu.be/1DR7mgeZuKE,Explaining tm and wordcloud2 Packages in R,Word Cloud Visualization in R,Data Visualization Techniques,Explaining tm and wordcloud2 Packages in R,Text Data Analysis,Data Visualization Techniques,Specialized Data Types Visualization,,,,,,,,,Data Scraping Techniques, +Mon/Wed,https://www.youtube.com/watch?v=aUJAHXfVrnw,Animated Demographic Trend Maps with Census Data in R,Animated Data Visualization,Data Visualization Techniques,Animated Demographic Trend Maps with Census Data in R,Animated Data Visualization,Data Visualization Techniques,Interactive and Dynamic Visualizations,,,,,,,,,Choropleth Map Design, +Mon/Wed,https://www.youtube.com/watch?v=jmLVh4yy58Y,Differentiating Population Pyramid and Violin Plot,Violin Plot,Data Visualization Techniques,Comparison Between Population Pyramid and Violin Plot,Violin Plot,Data Visualization Techniques,Plot Types and Techniques,,,,,,,,,R Data Visualization Tools, +Mon/Wed,https://youtu.be/9hB1M4hqCAA,Visualizing Demographic Trends with Animated Maps in Python,Animated Data Visualization,Data Visualization Techniques,Visualizing Demographic Trends with Animated Maps,Miscellaneous,Outside of R,Miscellaneous,,,,,,,,,GGplot2 Geospatial Visualization, +Mon/Wed,https://youtu.be/JV1eIjZ2EPA,Evaluating Aesthetics in GGplot2,Data Visualization Aesthetics,Data Visualization Techniques,3 Stages of Aesthetic Evaluation in GGplot2,Data Visualization Aesthetics,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,,,,Time Zone Handling in R, +Mon/Wed,https://www.youtube.com/watch?v=pFi11WIDi0E,Running R and Python in Jupyter with rpy2,R and Python Integration,Programming Techniques and Tools,How to run R and Python in the same Jupyter Notebook using rpy2,Miscellaneous,Programming Techniques and Tools,Miscellaneous,,,,,,,,,Expense Data Visualization, +Mon/Wed,https://www.youtube.com/watch?v=U6-6sXRUwoI,Creating Color Palettes from Images,Color Palette Design,Data Visualization Techniques,How To Create a Color Palette from an Image,Data Visualization Aesthetics,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,,,,Outlier Detection in Time Series, +Mon/Wed,https://www.youtube.com/watch?v=oU7mhpVZJT8,Comprehensive Guide to Data Visualization and Regression in Python,Data Visualization in Python,Data Visualization Techniques,Exploratory Analysis and Data Visualization,Miscellaneous,Outside of R,Miscellaneous,,,,,,,,,Shiny App for Outlier Detection, +Mon/Wed,https://youtu.be/bQ6XQiVKSGU,Circular Visualization of MBTI Data,Circular Data Visualization,Data Visualization Techniques,MBTI Data Analysis with Circular Visualization,Advanced Data Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,,,,,,,,,Emoji Data Visualization, +Tues/Thurs,https://youtu.be/bAlfTcX2Q8Y,Web Scraping Techniques in R,Data Scraping Techniques,Programming Techniques and Tools,Web Scraping in R,Miscellaneous,Data Collection and Preprocessing,Miscellaneous,,,,,,,,,Jointplot Creation in R, +Tues/Thurs,https://youtu.be/jLz0sf-UWug,Drawing Normal Curves with Facet_wrap() in R,Data Visualization Techniques,Data Visualization Techniques,How to draw normal curves while using 'facet_wrap()' in R,Faceting,Data Visualization Techniques,Basic and Advanced Techniques,Introduces the pitfalls and best practices of drawing normal curves with facets,,,,,,,,Lattice Package in R, +Tues/Thurs,https://youtu.be/-83_kmNW-D8,Choropleth Map Color Interpolation in Data Wrapper,Choropleth Map Design,Data Visualization Techniques,Color Interpolation for Choropleth Maps in Data Wrapper,Miscellaneous,Outside of R,Miscellaneous,,,,,,,,,Linear Regression App in R, +Tues/Thurs,https://youtu.be/ui8R4zbRgo8,Global Demographic Mapping with GGplot2 and Plotly,Geospatial Data Visualization,Data Visualization Techniques,Interactive Visualization of Geospatial Data with GGplot2 and Plotly,Geospatial Data Visualization,Data Visualization Techniques,Specialized Data Types Visualization,,,,,,,,,Caret Package in R, +Tues/Thurs,https://youtu.be/CpGb063CeWY,Data Visualization Tools for R Beginners,R Data Visualization Tools,Specific Software and Packages,Data Visualization R Add-ins: Equisse and GGThemeAssist ,Equisse Package,Interactive and Web-based Applications,Equisse Package,,,,,,,,,Color Ramp Design in R, +Tues/Thurs,https://youtu.be/NqDyeAsKUm8,Geom_map Usage in GGplot2,GGplot2 Geospatial Visualization,Data Visualization Techniques,How to use Geom_map in GGplot2,Geospatial Data Visualization,Data Visualization Techniques,Specialized Data Types Visualization,,,,,,,,,Plotly Map Creation in R, +Tues/Thurs,https://youtu.be/nLTD8tKimW4,Handling Time Zones in R Data Visualization,Time Zone Handling in R,Data Visualization Techniques,Working with Time Zone in R with Lubridate,Miscellaneous,Data Collection and Preprocessing,Miscellaneous,,,,,,,,,Categorical Data Changes Visualization, +Tues/Thurs,https://youtu.be/8lsHGKUn8A0,Visualizing Expenses with R,Expense Data Visualization,Data Visualization Techniques,Using R to visualize expense,Financial Data Analysis,Data Visualization Techniques,Specialized Data Types Visualization,,,,,,,,,2D and 3D Data Visualization, +Tues/Thurs,https://youtu.be/eCsZs-pqy3M,Structure and Features of Shiny Web Applications,Shiny App Development,Interactive and Web-based Applications,Introduction to Features of Shiny App,Shiny App,Interactive and Web-based Applications,Shiny App,,,,,,,,,Functional Programming, +Tues/Thurs,https://youtu.be/qxEn68VXSNo,Understanding ARIMA and SARIMA in Parameter Selection,Time Series Analysis,Statistical Analysis and Modelling,"Introduction to ARIMA, SARIMA and Parameters Selection",Time Series Analysis,Statistical Analysis and Modelling,Time Series Analysis,,,,,,,,,Data Manipulation and Speed Optimization, +Tues/Thurs,https://youtu.be/ZYdfLpoBVjU,Outlier Detection in Time Series with R,Outlier Detection in Time Series,Statistical Analysis and Modelling,"Detecting Outliers in Time Series: 'anomaly', 'tsoutliers', and 'checkresiduals'",Time Series Analysis,Statistical Analysis and Modelling,Time Series Analysis,,,,,,,,,Customizing Themes in 'ggplot2', +Tues/Thurs,https://youtu.be/cmJrHpFYh1U,Shiny Web App for Outlier Detection and Visualization,Shiny App for Outlier Detection,Interactive and Web-based Applications,Outlier Detection App with Shiny App,Shiny App,Interactive and Web-based Applications,Shiny App,,,,,,,,,Advanced Plot Types in 'ggplot2', +Tues/Thurs,https://youtu.be/A-il70GvkkM,Emoji-Based Data Visualization in R,Emoji Data Visualization,Data Visualization Techniques,Data Visualization with Emojis,Miscellaneous,Data Visualization Techniques,Miscellaneous,,,,,,,,,TrelliscopeJS, +Tues/Thurs,https://www.youtube.com/watch?v=bLQfqMvSh3g,Jointplot Creation with ggside in R,Jointplot Creation in R,Data Visualization Techniques,Jointplot with GGside for Bivariate Analysis ,Multivariate Analysis,Data Visualization Techniques,Statistical and Quantitative Analysis,,,,,,,,,Advanced Plotly Visualization in Python, +Tues/Thurs,https://youtu.be/wsrGQPPPmN4,Lattice Package Introduction in R,Lattice Package in R,Specific Software and Packages,Lattice Package for Univariate and Multivariate Analysis,Multivariate Analysis,Data Visualization Techniques,Statistical and Quantitative Analysis,,,,,,,,,Earthquake Mapping in Turkey, +Tues/Thurs,https://youtu.be/eMwM3gXQ5Xg,Interactive Linear Regression Model App in R,Linear Regression App in R,Statistical Analysis and Modelling,Interactive Linear Regression Model App with Shiny App,Shiny App,Interactive and Web-based Applications,Shiny App,,,,,,,,,Spatial Data Analysis, +Tues/Thurs,https://www.youtube.com/watch?v=WDSP9KGcFAA,Caret Package Implementation in R,Caret Package in R,Programming Techniques and Tools,Caret Package for Machine Learning,Miscellaneous,Statistical Analysis and Modelling,Miscellaneous,"Carot is a useful ML package for preprocessing, feature selection, training and testing, hyper parameter tuning and etc.",,,,,,,,Data Analysis and Clustering, +Tues/Thurs,https://youtu.be/8Ktp7bOyGNg,Designing Effective Data Visualization Color Ramps in R,Color Ramp Design in R,Programming Techniques and Tools,Effective color ramps,Data Visualization Aesthetics,Data Visualization Techniques,Basic and Advanced Techniques,Color ramps are the set of gradient of colors for continuous numeric or ordered data. The video introduces best practices of color ramp choices.,,,,,,,,Excel Data Visualization, +Tues/Thurs,https://www.youtube.com/watch?v=tikmXPmjig8,Plotly Map Creation in R for NYC Car Accident Density,Plotly Map Creation in R,Data Visualization Techniques,Accident Density Mapping in NYC with Plotly Interactive Plots,Geospatial Data Visualization,Data Visualization Techniques,Specialized Data Types Visualization,The tutorial of drawing a colored map with Plotly in R based on the car accident density of each census block in New York City.,,,,,,,,Data Table Creation in R, +Tues/Thurs,https://www.youtube.com/watch?v=rp4ahQtkXrM&ab_channel=YelamanSain,Visualizing Categorical Data Changes Over Time with Arrow Plots,Categorical Data Changes Visualization,Data Visualization Techniques,Arrow Plots and Animations for Exploratory Data Analysis and Visualization,Animated Data Visualization,Data Visualization Techniques,Interactive and Dynamic Visualizations,Arrow plots and animations are great ways visualize changes in categorical data over time and as an alternative to alluvial plots.,,,,,,,,Geospatial Data Analysis, +Tues/Thurs,https://www.youtube.com/watch?v=7UHCfWN96d0,Creating 2D and 3D Visualizations in R,2D and 3D Data Visualization,Data Visualization Techniques,Creating 2D and 3D Visualizations with Rayshader,3D Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,Rayshader is a useful package to create 3D rendings of 2D plots and build basic terrain plots.,,,,,,,,Heatmap Usage in R, +Tues/Thurs,https://www.youtube.com/watch?v=AAMZFA-eQM8&ab_channel=emm2293,Functional Programming in R,Functional Programming,Programming Techniques and Tools,Functional Programming in R,Miscellaneous,Programming Techniques and Tools,Miscellaneous,Demonstrates the use of functional programming (eg. map()) in R with PURR package.,,,,,,,,Graph Data Analysis, +Tues/Thurs,https://youtu.be/E4M39IxvvtI,Speeding Up Data Manipulation and Visualization in R,Data Manipulation and Speed Optimization,Programming Techniques and Tools,Quick Tips on Speeding up Data Manipulation and Visualization in R,Miscellaneous,Programming Techniques and Tools,Miscellaneous,"Introduces ways of speeding up data manipulation and visualization, like apply() vs lapply() and etc.",,,,,,,,Dygraphs Package in R, +Tues/Thurs,https://www.youtube.com/watch?v=IM6cCHPoP38&ab_channel=AustinSchaefer,Customizing Themes in R with 'ggthemr',Customizing Themes in 'ggplot2',Programming Techniques and Tools,Customizing Themes in R with GGthemr,Data Visualization Aesthetics,Data Visualization Techniques,Basic and Advanced Techniques,Introduces ways of selecting pre-built themes and creating themes.,,,,,,,,Color Palette Design for Maps, +Tues/Thurs,https://youtu.be/EaNv8NVBc_Q,Waterfall Graph Plotting with GGplot2 in R,Advanced Plot Types in 'ggplot2',Data Visualization Techniques,Plotting Waterfall Chart with R ggplot2,Advanced Data Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,Waterfall plots are useful in visualizing cumulative changes in values like time series data or components of revenue.,,,,,,,,Hexagonal Grids for EDA, +Tues/Thurs,https://youtu.be/7qcQgLN8TnM,TrelliscopeJS Tutorial in R,TrelliscopeJS,Data Visualization Techniques,TrelliscopeJS for Interactive Data Visualization,Advanced Data Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,TrelliscopeJS helps interactively explore and analyze relationships in complex multivariate datasets.,,,,,,,,Parallel Coordinate Plots in R, +Tues/Thurs,https://youtu.be/mIg_5rwmGfo,Introduction to Heatmap Usage in R,Heatmap Usage,Data Visualization Techniques,Common Uses of Heatmap,Heatmap,Data Visualization Techniques,Plot Types and Techniques,Introduces usage of website heatmap and correlation plot.,,,,,,,,Scatterplots with Highcharter in R, +Tues/Thurs,https://youtu.be/moCIjBGxPzs,Advanced Interactive Visualization Techniques with Plotly in Python,Advanced Plotly Visualization in Python,Data Visualization Techniques,Interactive visualization with Plotly in Python,Miscellaneous,Outside of R,Miscellaneous,Plotly is useful for interactive visualizations. We can also customize its interactivity.,,,,,,,,Body Fat Percentage Estimation, +Tues/Thurs,https://www.youtube.com/watch?v=-1G16o8fKMU&feature=youtu.be,Interactive Earthquake Mapping in Turkey with Python,Earthquake Mapping in Turkey,Interactive and Web-based Applications,Interactive visualization of Earthquakes in Turkey in Python,Geospatial Data Visualization,Outside of R,Geospatial Data Visualization,Uses the Python package Folium for creating interactive geospatial visualizaitons.,,,,,,,,Quick Data Visualization in R, +Tues/Thurs,https://youtu.be/cs6Mb3Kv7rA,Embedding Graphs in Geographic Maps with Leaflet in R,Geospatial Data Visualization,Data Visualization Techniques,Add graphs Inside Markers on a Geographic Map with LeafLet,Geospatial Data Visualization,Data Visualization Techniques,Specialized Data Types Visualization,Leaflet can embed graphs in markers in a map. ,,,,,,,,Formatting and Visualization in R, +Tues/Thurs,https://youtu.be/ykFh3_D8Ors,Reducing Spatial Dataset Redundancies in R,Spatial Data Analysis,Programming Techniques and Tools,Reducing Spatial Data Redundancies,Geospatial Data Visualization,Outside of R,Geospatial Data Visualization,Uses clustering to reduce overlapping geospatial data points.,,,,,,,,Graph Resizing in R Markdown, +Tues/Thurs,https://youtu.be/J8RPf7fpeio,Analyzing Keyword Popularity and Stock Prices,Financial Data Analysis,Statistical Analysis and Modelling,Correlation between Google keyword searches and stock prices via regression,Miscellaneous,Statistical Analysis and Modelling,Miscellaneous,Uses gtrendsR and Quantmod libraries to explore correlation between Google trends and the closing price of a stock.,,,,,,,,, +Tues/Thurs,https://youtu.be/PhbY_ovIQIw,Correlation Matrices and Hierarchical Clustering in R,Data Analysis and Clustering,Data Visualization Techniques,Correlation Matrices and Hierarchical Clustering in R,Basic Data Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,Correlation matrix is useful for analyzing relationships between lots of variables. Hiearchical clustering combined with correlation matrix help us find clear groups of correlated variables.,,,,,,,,Data Cleaning and Visualization in R, +Tues/Thurs,https://www.youtube.com/watch?v=mc1ON6ATsxU,Efficient Excel Chart Management with VBA Macros,Excel Data Visualization,Data Visualization Techniques,Efficient Excel Chart Management with VBA Macros,Miscellaneous,Outside of R,Miscellaneous,VBA can be used to code efficient functions to perform operations on multiple excel charts in a loop.,,,,,,,,, +Tues/Thurs,https://youtu.be/SL9gkcZjGx4,Basic Table Creation in R,Data Table Creation in R,Specific Software and Packages,Basic Table Creation in R,Basic Data Visualizations,Data Visualization Techniques,Basic and Advanced Techniques,Introduces table creaion and table theme customization.,,,,,,,,, +Tues/Thurs,https://youtu.be/xjwiaVM0G9A,Understanding Geographic Maps and Projections in R,Geospatial Data Analysis,Data Visualization Techniques,Maps and Projections in R,Geospatial Data Visualization,Data Visualization Techniques,Specialized Data Types Visualization,Projections can convert plain 2-D maps to spherical accurate visual representations of the globe.,,,,,,,,, +Tues/Thurs,https://youtu.be/06sfnL8TpOI,Heatmap Plotting Tutorial in R,Heatmap Usage in R,Data Visualization Techniques,Heatmaps in R,Heatmap,Data Visualization Techniques,Plot Types and Techniques,"Introduces practices of heatmap plotting, like scaling, setting colors and etc.",,,,,,,,, +Tues/Thurs,https://youtu.be/cgjVXbSMjho,Exploratory Analysis with Neo4J and Python,Graph Data Analysis,Programming Techniques and Tools,Visualization of Graph Data with Neo4J in Python,Miscellaneous,Outside of R,Miscellaneous,,,,,,,,,, +Tues/Thurs,https://youtu.be/71zl39yzwXM,Dygraphs Package Usage in R,Dygraphs Package in R,Specific Software and Packages,Interactive Time Series Data Visualization with Dygraphs Package,Time Series Data Visualization,Data Visualization Techniques,Specialized Data Types Visualization,Dygraph packages allows for interactive visualization of time series data and displaying confidence intervals for forecasting.,,,,,,,,, +Tues/Thurs,https://www.youtube.com/watch?v=KrwS10RStbw,Designing Bivariate Color Palettes for Choropleth Maps in R,Color Palette Design for Maps,Specific Software and Packages,Designing Bivariate Color Palettes for Choropleth Maps in R,Data Visualization Aesthetics,Data Visualization Techniques,Basic and Advanced Techniques,Introduces the process of creating a distrete bivariate color palette for Choropleth Maps in GGplot2l,,,,,,,,, +Tues/Thurs,https://www.youtube.com/watch?v=oUgAx_Fosvg,Exploring Hexagonal Grids in R for EDA,Hexagonal Grids for EDA,Statistical Analysis and Modelling,Deep Dive in Hexagonal Grids,Miscellaneous,Parameter Analysis of Visualization Techniques,Miscellaneous,Analyzes the mathematical foundation behind hexagonal grids and their benefits in visualization.,,,,,,,,, +Tues/Thurs,https://youtu.be/-hutMwiLO7A,Choropleth Map Creation in R,Choropleth Map Design,Data Visualization Techniques,Choropleth Maps in R,Geospatial Data Visualization,Data Visualization Techniques,Specialized Data Types Visualization,Introduces Creation of Choropleth Maps in R combined with sf package.,,,,,,,,, +Tues/Thurs,https://youtu.be/HTh5e7v54uM,Parallel Coordinate Plot Analysis in R,Parallel Coordinate Plots in R,Data Visualization Techniques,Deep Dive of Parallel Coordinate Plots in R,Parallel Coordinates,Data Visualization Techniques,Basic and Advanced Techniques,"Introduces best-practices for parallel coordinate plots, like adjusting line fills, using interactive plotting and etc.",,,,,,,,, +Tues/Thurs,https://youtu.be/SNbqdJmqtAw,Highcharter Package for Scatterplots in R Studio,Scatterplots with Highcharter in R,Data Visualization Techniques,Interactive Scatterplots with Highcharter Package in R,Scatterplots,Data Visualization Techniques,Plot Types and Techniques,"Highcharter package can conveniently create scatter plots with interactive tooltips, automatic coloring and etc.",,,,,,,,, +Tues/Thurs,https://youtu.be/UxGtp_KIkL0,Developing a Model to Estimate Body Fat Percentage,Body Fat Percentage Estimation,Statistical Analysis and Modelling,Modelling of Body Fat Percentage,Miscellaneous,Statistical Analysis and Modelling,Miscellaneous,Conducts modelling and several plotting of distributions of body fat percentage,,,,,,,,, +Tues/Thurs,https://www.youtube.com/watch?v=Lod_XBetQ5g,Quick Visualization with GGplot and GGRapture in R,Quick Data Visualization in R,Data Visualization Techniques,Quick Interactive Data Visualization with GGplot and GGRapture,Miscellaneous,Interactive and Web-based Applications,Miscellaneous,Convenient Add-in for R to perform multiple visualizations,,,,,,,,, +Tues/Thurs,https://www.youtube.com/watch?v=xj4HCy5Y3Yg,Advanced Formatting and Visualization in R,Formatting and Visualization in R,Programming Techniques and Tools,Formatting of R Output File and Changing Order of Graph Layers,R Output Formatting,Data Visualization Techniques,Miscellaneous,Introduces methods of changing font color and size and reordering of layers in a graph.,,,,,,,,, +Tues/Thurs,https://youtu.be/PWqMcgbISFI,Resizing Graphs in R Markdown,Graph Resizing in R Markdown,Data Visualization Techniques,Graph Resizing in R Markdown,R Output Formatting,Data Visualization Techniques,Miscellaneous,,,,,,,,,, +Tues/Thurs,https://youtu.be/gFOugJLtkF8,,,,,,,,,,,,,,,,, +Tues/Thurs,https://www.youtube.com/watch?v=cmzpZyz0Os4,Data Cleaning and Visualization Techniques in R,Data Cleaning and Visualization in R,Data Visualization Techniques,Tricks of data cleaning and visualization in R,Miscellaneous,Data Collection and Preprocessing,Miscellaneous,Introduces pitfalls and tricks in data preprocessing and visualizations.,,,,,,,,, diff --git a/project1.qmd b/project1.qmd index 46840aa..a90240a 100644 --- a/project1.qmd +++ b/project1.qmd @@ -1,6 +1,18 @@ --- -title: "Project 1" -author: 'author name' +title: "Data Collection and Preprocessing" --- +[1. Understanding the grepl() function in R (Mon/Wed)](https://www.youtube.com/watch?v=t5iv84vwrxk) + +[2. Strategies for Handling Missing Data (Mon/Wed)](https://youtu.be/A7GkdSjLL_g) + +[3. Combining Sub-Categories in Dataframes in R with grepl() (Mon/Wed)](https://youtu.be/A8IqvqnkIC4) + +[4. Min-max Scaling in R (Mon/Wed)](https://youtu.be/Oi0P5CT5hS0) + +[5. Web Scraping in R (Tues/Thurs)](https://youtu.be/bAlfTcX2Q8Y) + +[6. Working with Time Zone in R with Lubridate (Tues/Thurs)](https://youtu.be/nLTD8tKimW4) + +[7. Tricks of data cleaning and visualization in R (Tues/Thurs)](https://www.youtube.com/watch?v=cmzpZyz0Os4) + -## Project 1 \ No newline at end of file diff --git a/project1_sample.qmd b/project1_sample.qmd new file mode 100644 index 0000000..bf35dc2 --- /dev/null +++ b/project1_sample.qmd @@ -0,0 +1,8 @@ +--- +title: "Project 1" +author: 'author name' +--- + +## Welcome to EDAV 2023 community contribution video repository + +In here you'll find the hard work of the MSDS 2023 students. \ No newline at end of file diff --git a/project2.qmd b/project2.qmd index 059960d..bd521b3 100644 --- a/project2.qmd +++ b/project2.qmd @@ -1,6 +1,135 @@ --- -title: "Project 2" -author: 'author name' +title: "Data Visualization Techniques" --- +# Subcategory 1: Plot Types and Techniques +[1. Creating Scatter Plots with Boundaries in R (Mon/Wed)](https://youtu.be/dCl9tISXSSQ) + +[2. Interactive Heatmap Visualization in Montgomery County with R Shiny (Mon/Wed)](https://www.youtube.com/watch?v=nNI4SeSLgN0) + +[3. HexMaps: Understanding the Y-axis (Mon/Wed)](https://youtu.be/mslzLw5v738) + +[4. Sorting Boxplots and Barplots in R (Mon/Wed)](https://youtu.be/3RkNs3cyHlU) + +[5. Comparison between Geom_Mosaic and Vcd::Mosaic (Mon/Wed)](https://drive.google.com/file/d/15vGCwVp3WpW1bFwdP_prFg1zRozqYERK/view?usp=sharing) + +[6. Unemployment Heatmaps with R and Python (Mon/Wed)](https://youtu.be/BVLzry9EBGY) + +[7. Comparison Between Population Pyramid and Violin Plot (Mon/Wed)](https://www.youtube.com/watch?v=jmLVh4yy58Y) + +[8. Common Uses of Heatmap (Tues/Thurs)](https://youtu.be/mIg_5rwmGfo) + +[9. Heatmaps in R (Tues/Thurs)](https://youtu.be/06sfnL8TpOI) + +[10. Interactive Scatterplots with Highcharter Package in R (Tues/Thurs)](https://youtu.be/SNbqdJmqtAw) + + +# Subcategory 2: Interactive and Dynamic Visualizations +[11. Interactive Data Visualizations with Plotly in R (Mon/Wed)](https://youtu.be/87ZuiR7SrfA) + +[12. Enhancing Visualizations with Plotly Layout Features in Python (Mon/Wed)](https://www.youtube.com/watch?v=gM8yz6P7V0A) + +[13. Creating Animations using gganimate (Mon/Wed)](https://www.youtube.com/watch?v=USw2AECFIkw) + +[14. Creating Sankey Diagrams with networkD3 in R (Mon/Wed)](https://youtu.be/n43RMdALxvc) + +[15. Gene and Protein Interaction Networks with NetworkD3 in R (Mon/Wed)](https://www.youtube.com/watch?v=FdzTsrCvdkk) + +[16. Animated Demographic Trend Maps with Census Data in R (Mon/Wed)](https://www.youtube.com/watch?v=aUJAHXfVrnw) + +[17. Arrow Plots and Animations for Exploratory Data Analysis and Visualization (Tues/Thurs)](https://www.youtube.com/watch?v=rp4ahQtkXrM&ab_channel=YelamanSain) + + +# Subcategory 3: Basic and Advanced Techniques +[18. Overlaying Facetted Histograms with Theoretical Normal Density Curves (Mon/Wed)](https://www.youtube.com/watch?v=9wtvJz82oSw) + +[19. Plotting theoretical normal distribution across facets using gg4x (Mon/Wed)](https://youtu.be/fyHNWSkcYpk) + +[20. Bubble and 3D Plot Techniques in R with Plotly (Mon/Wed)](https://youtu.be/tpl4TEVOHCI) + +[21. Unlocking Inclusive Data Visualization: Color-Blind Friendly Charts in R (Mon/Wed)](https://youtu.be/PudGAzRal_4) + +[22. Combining boxplots and ridgeline plots on one plot (Mon/Wed)](https://www.youtube.com/watch?v=40MokoiI3Aw) + +[23. EDA & Visualization on Ranking Method (Mon/Wed)](https://youtu.be/aG7t5I2zk9E) + +[24. Comparing Facet Functions: facet_wrap() vs facet_grid() (Mon/Wed)](https://www.youtube.com/watch?v=6k39xguz4js) + +[25. Generative Art Techniques in R (Mon/Wed)](https://youtu.be/ZAyUFSky0N8) + +[26. Creating the Droste Effect in R (Mon/Wed)](https://youtu.be/nI1XMoUHn0o) + +[27. 3 Stages of Aesthetic Evaluation in GGplot2 (Mon/Wed)](https://youtu.be/JV1eIjZ2EPA) + +[28. How To Create a Color Palette from an Image (Mon/Wed)](https://www.youtube.com/watch?v=U6-6sXRUwoI) + +[29. MBTI Data Analysis with Circular Visualization (Mon/Wed)](https://youtu.be/bQ6XQiVKSGU) + +[30. How to draw normal curves while using 'facet_wrap()' in R (Tues/Thurs)](https://youtu.be/jLz0sf-UWug) + +[31. Effective color ramps (Tues/Thurs)](https://youtu.be/8Ktp7bOyGNg) + +[32. Creating 2D and 3D Visualizations with Rayshader (Tues/Thurs)](https://www.youtube.com/watch?v=7UHCfWN96d0) + +[33. Customizing Themes in R with GGthemr (Tues/Thurs)](https://www.youtube.com/watch?v=IM6cCHPoP38&ab_channel=AustinSchaefer) + +[34. Plotting Waterfall Chart with R ggplot2 (Tues/Thurs)](https://youtu.be/EaNv8NVBc_Q) + +[35. TrelliscopeJS for Interactive Data Visualization (Tues/Thurs)](https://youtu.be/7qcQgLN8TnM) + +[36. Correlation Matrices and Hierarchical Clustering in R (Tues/Thurs)](https://youtu.be/PhbY_ovIQIw) + +[37. Basic Table Creation in R (Tues/Thurs)](https://youtu.be/SL9gkcZjGx4) + +[38. Designing Bivariate Color Palettes for Choropleth Maps in R (Tues/Thurs)](https://www.youtube.com/watch?v=KrwS10RStbw) + +[39. Deep Dive of Parallel Coordinate Plots in R (Tues/Thurs)](https://youtu.be/HTh5e7v54uM) + + +# Subcategory 4: Specialized Data Types Visualization +[40. Using WordClouds to analyze data (Mon/Wed)](https://youtu.be/GPHgiqD2XfE) + +[41. R Visualizations for Financial Data Analysis with Plotly (Mon/Wed)](https://youtu.be/tbmGq7_TM-4) + +[42. NFL Visualizations with Next Gen Stats (Mon/Wed)](https://youtu.be/xs0cGbjoseE) + +[43. Personal Finance Case Study: Monthly Expense Analysis in R (Mon/Wed)](https://youtu.be/Q06kLBNwGc8) + +[44. Explaining tm and wordcloud2 Packages in R (Mon/Wed)](https://youtu.be/1DR7mgeZuKE) + +[45. Interactive Visualization of Geospatial Data with GGplot2 and Plotly (Tues/Thurs)](https://youtu.be/ui8R4zbRgo8) + +[46. How to use Geom_map in GGplot2 (Tues/Thurs)](https://youtu.be/NqDyeAsKUm8) + +[47. Using R to visualize expense (Tues/Thurs)](https://youtu.be/8lsHGKUn8A0) + +[48. Accident Density Mapping in NYC with Plotly Interactive Plots (Tues/Thurs)](https://www.youtube.com/watch?v=tikmXPmjig8) + +[49. Add graphs Inside Markers on a Geographic Map with LeafLet (Tues/Thurs)](https://youtu.be/cs6Mb3Kv7rA) + +[50. Maps and Projections in R (Tues/Thurs)](https://youtu.be/xjwiaVM0G9A) + +[51. Interactive Time Series Data Visualization with Dygraphs Package (Tues/Thurs)](https://youtu.be/71zl39yzwXM) + +[52. Choropleth Maps in R (Tues/Thurs)](https://youtu.be/-hutMwiLO7A) + + +# Subcategory 5: Statistical and Quantitative Analysis +[53. Exploring Categorical Datasets in R (Mon/Wed)](https://youtu.be/s5ehRahj2pM?feature=shared) + +[54. Quantile-Based Data Partitioning Methods in R with Cut2() (Mon/Wed)](https://youtu.be/BmxpApzmpqE) + +[55. Effective Data Comparison on Different Scales (Mon/Wed)](https://www.youtube.com/watch?v=LG10EWIpCD4) + +[56. Jointplot with GGside for Bivariate Analysis (Tues/Thurs)](https://www.youtube.com/watch?v=bLQfqMvSh3g) + +[57. Lattice Package for Univariate and Multivariate Analysis (Tues/Thurs)](https://youtu.be/wsrGQPPPmN4) + + +# Subcategory 6: Miscellaneous +[58. Data Visualization with Emojis (Tues/Thurs)](https://youtu.be/A-il70GvkkM) + +[59. Formatting of R Output File and Changing Order of Graph Layers (Tues/Thurs)](https://www.youtube.com/watch?v=xj4HCy5Y3Yg) + +[60. Graph Resizing in R Markdown (Tues/Thurs)](https://youtu.be/PWqMcgbISFI) + -## Project 2 \ No newline at end of file diff --git a/project3.qmd b/project3.qmd new file mode 100644 index 0000000..2af80a9 --- /dev/null +++ b/project3.qmd @@ -0,0 +1,35 @@ +--- +title: "Interactive and Web-based Applications" +--- +# Subcategory 1: Equisse Package +[1. Easy Data Analysis and Visualization Add-ins in R (Mon/Wed)](https://www.youtube.com/watch?v=gIPY_5m-vRk) + +[2. Data Visualization R Add-ins: Equisse and GGThemeAssist (Tues/Thurs)](https://youtu.be/CpGb063CeWY) + + +# Subcategory 2: Shiny App +[3. How to make an Interactive Dashboard with Shiny App on R (Mon/Wed)](https://youtu.be/UG6QibAflRs) + +[4. Introducing Shiny Package in R - Interactive Web Dashboard (Mon/Wed)](https://www.youtube.com/watch?v=FIr5wMAn2ow) + +[5. RShiny: Overview, Reactives, How it helps (Mon/Wed)](https://youtu.be/8Z1me5ebCUw) + +[6. Introduction to Features of Shiny App (Tues/Thurs)](https://youtu.be/eCsZs-pqy3M) + +[7. Outlier Detection App with Shiny App (Tues/Thurs)](https://youtu.be/cmJrHpFYh1U) + +[8. Interactive Linear Regression Model App with Shiny App (Tues/Thurs)](https://youtu.be/eMwM3gXQ5Xg) + + +# Subcategory 3: Miscellaneous +[9. Implementing Interactive Filters in R (Mon/Wed)](https://www.loom.com/share/0ba371f62d884042bdf1288d1fca926e) + +[10. Interactive Dashboard Creation in R (Mon/Wed)](https://youtu.be/szwG8L-Eyc4) + +[11. Understanding Interactive 2D Splines (Mon/Wed)](https://youtu.be/Nm2oT09uSA0) + +[12. Embedding Visualizations into Web Apps with R (Mon/Wed)](https://www.youtube.com/watch?v=8j3ttwrtASE) + +[13. Quick Interactive Data Visualization with GGplot and GGRapture (Tues/Thurs)](https://www.youtube.com/watch?v=Lod_XBetQ5g) + + diff --git a/project4.qmd b/project4.qmd new file mode 100644 index 0000000..233e1c0 --- /dev/null +++ b/project4.qmd @@ -0,0 +1,8 @@ +--- +title: "Parameter Analysis of Visualization Techniques" +--- +[1. QQ Plots and Sample Quantiles Analysis in R (Mon/Wed)](https://www.youtube.com/watch?v=RKcvdK5DNZU&ab_channel=MartinFluder) + +[2. Deep Dive in Hexagonal Grids (Tues/Thurs)](https://www.youtube.com/watch?v=oUgAx_Fosvg) + + diff --git a/project5.qmd b/project5.qmd new file mode 100644 index 0000000..d530aa9 --- /dev/null +++ b/project5.qmd @@ -0,0 +1,14 @@ +--- +title: "Programming Techniques and Tools" +--- +[1. Using R in Jupyter Notebook in VSCode and Latex (Mon/Wed)](https://youtu.be/B5anK7XBXrg) + +[2. Project Management in R using RProject and Renv (Mon/Wed)](https://www.youtube.com/watch?v=kBkpIck87Ys) + +[3. How to run R and Python in the same Jupyter Notebook using rpy2 (Mon/Wed)](https://www.youtube.com/watch?v=pFi11WIDi0E) + +[4. Functional Programming in R (Tues/Thurs)](https://www.youtube.com/watch?v=AAMZFA-eQM8&ab_channel=emm2293) + +[5. Quick Tips on Speeding up Data Manipulation and Visualization in R (Tues/Thurs)](https://youtu.be/E4M39IxvvtI) + + diff --git a/project6.qmd b/project6.qmd new file mode 100644 index 0000000..bf9fef1 --- /dev/null +++ b/project6.qmd @@ -0,0 +1,23 @@ +--- +title: "Statistical Analysis and Modelling" +--- +# Subcategory 1: Time Series Analysis +[1. Time Series Analysis Using Forecast Library (Mon/Wed)](https://youtu.be/iCt8uBjFEjo) + +[2. Introduction to ARIMA, SARIMA and Parameters Selection (Tues/Thurs)](https://youtu.be/qxEn68VXSNo) + +[3. Detecting Outliers in Time Series: 'anomaly', 'tsoutliers', and 'checkresiduals' (Tues/Thurs)](https://youtu.be/ZYdfLpoBVjU) + + +# Subcategory 2: Miscellaneous +[4. Sentiment Analysis in Tweets about Fast Fashion (Mon/Wed)](https://youtu.be/xk7L7xJhOyQ) + +[5. Causal Inference with Sandwich Package in R (Mon/Wed)](https://youtu.be/r7xCvHttgi0) + +[6. Caret Package for Machine Learning (Tues/Thurs)](https://www.youtube.com/watch?v=WDSP9KGcFAA) + +[7. Correlation between Google keyword searches and stock prices via regression (Tues/Thurs)](https://youtu.be/J8RPf7fpeio) + +[8. Modelling of Body Fat Percentage (Tues/Thurs)](https://youtu.be/UxGtp_KIkL0) + + diff --git a/project7.qmd b/project7.qmd new file mode 100644 index 0000000..366e3d8 --- /dev/null +++ b/project7.qmd @@ -0,0 +1,34 @@ +--- +title: "Outside of R" +--- +# Subcategory 1: Geospatial Data Visualization +[1. Relating Impact with Interactive Maps (Mon/Wed)](https://youtu.be/UT_-BeZl2o4) + +[2. A Complete Python Folium Tutorial (Mon/Wed)](https://www.youtube.com/watch?v=Ob0fhtDnFus) + +[3. Map Data Visualization with Folium in Python (Mon/Wed)](https://youtu.be/IDy_6UA9jak) + +[4. Interactive visualization of Earthquakes in Turkey in Python (Tues/Thurs)](https://www.youtube.com/watch?v=-1G16o8fKMU&feature=youtu.be) + +[5. Reducing Spatial Data Redundancies (Tues/Thurs)](https://youtu.be/ykFh3_D8Ors) + + +# Subcategory 2: Miscellaneous +[6. Streamlit Data Analysis App: StudyStats, a non-coding platform for beginners + (Mon/Wed)](https://youtu.be/gsCIMVdy-Kw) + +[7. Interactive Data Visualizations in Python (Mon/Wed)](https://youtu.be/gWsKyyakkrk) + +[8. Visualizing Demographic Trends with Animated Maps (Mon/Wed)](https://youtu.be/9hB1M4hqCAA) + +[9. Exploratory Analysis and Data Visualization (Mon/Wed)](https://www.youtube.com/watch?v=oU7mhpVZJT8) + +[10. Color Interpolation for Choropleth Maps in Data Wrapper (Tues/Thurs)](https://youtu.be/-83_kmNW-D8) + +[11. Interactive visualization with Plotly in Python (Tues/Thurs)](https://youtu.be/moCIjBGxPzs) + +[12. Efficient Excel Chart Management with VBA Macros (Tues/Thurs)](https://www.youtube.com/watch?v=mc1ON6ATsxU) + +[13. Visualization of Graph Data with Neo4J in Python (Tues/Thurs)](https://youtu.be/cgjVXbSMjho) + + diff --git a/welcome.qmd b/welcome.qmd index 5098edf..78483d2 100644 --- a/welcome.qmd +++ b/welcome.qmd @@ -2,4 +2,9 @@ title: "Welcome!" --- -This webpage contains community contributions for Fall 2023 EDAV class at Columbia University. \ No newline at end of file +This webpage contains community contributions for Fall 2023 EDAV class at Columbia University. + +There are 108 videos in total that are divided into 7 categories. + +Hopefully this will help you prepare for your final project! +