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.
-For the final project project, you’re welcome to first check out “1. Data Collection and Preprocessing” and “2. Data Visualization Techniques”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in it.
-Other categories cover more advanced skills, which may not necessarily be within the scope of the final project, but are still extremely interesting, useful, and insightful!
-“3. Interactive and Web-based Applications”: using Shiny App in R to create interactive online dashboards
+For the final project, you’re welcome to first check out “1. Data Collection and Preprocessing” and “2. Data Visualization Techniques”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in each of them.
+Other categories cover more advanced skills, which may be outside the scope of the final project but are still extremely interesting, useful, and insightful!
+“3. Interactive and Web-based Applications”: Using Shiny App in R to create interactive online dashboards
“4. Outside of R”: Data visualization techniques outside of R, most of which are in Python
“5. Parameter Analysis of Visualization Techniques”: Dives into the mathematical foundation behind visualization techniques
-“6. Programming Techniques and Tools”: diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex
-“7. Statistical Analysis and Modeling”: machine learning, modeling, and inference in R
+“6. Programming Techniques and Tools”: Diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex
+“7. Statistical Analysis and Modeling”: Machine learning, modeling, and inference in R
diff --git a/docs/search.json b/docs/search.json index 08343cf..c703931 100644 --- a/docs/search.json +++ b/docs/search.json @@ -60,13 +60,13 @@ "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.\nFor the final project project, you’re welcome to first check out “1. Data Collection and Preprocessing” and “2. Data Visualization Techniques”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in it.\nOther categories cover more advanced skills, which may not necessarily be within the scope of the final project, but are still extremely interesting, useful, and insightful!\n“3. Interactive and Web-based Applications”: using Shiny App in R to create interactive online dashboards\n“4. Outside of R”: Data visualization techniques outside of R, most of which are in Python\n“5. Parameter Analysis of Visualization Techniques”: Dives into the mathematical foundation behind visualization techniques\n“6. Programming Techniques and Tools”: diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex\n“7. Statistical Analysis and Modeling”: machine learning, modeling, and inference in R\n\n\n\n Back to top" + "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.\nFor the final project, you’re welcome to first check out “1. Data Collection and Preprocessing” and “2. Data Visualization Techniques”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in each of them.\nOther categories cover more advanced skills, which may be outside the scope of the final project but are still extremely interesting, useful, and insightful!\n“3. Interactive and Web-based Applications”: Using Shiny App in R to create interactive online dashboards\n“4. Outside of R”: Data visualization techniques outside of R, most of which are in Python\n“5. Parameter Analysis of Visualization Techniques”: Dives into the mathematical foundation behind visualization techniques\n“6. Programming Techniques and Tools”: Diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex\n“7. Statistical Analysis and Modeling”: Machine learning, modeling, and inference in R\n\n\n\n Back to top" }, { "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.\nFor the final project project, you’re welcome to first check out “1. Data Collection and Preprocessing” and “2. Data Visualization Techniques”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in it.\nOther categories cover more advanced skills, which may not necessarily be within the scope of the final project, but are still extremely interesting, useful, and insightful!\n“3. Interactive and Web-based Applications”: using Shiny App in R to create interactive online dashboards\n“4. Outside of R”: Data visualization techniques outside of R, most of which are in Python\n“5. Parameter Analysis of Visualization Techniques”: Dives into the mathematical foundation behind visualization techniques\n“6. Programming Techniques and Tools”: diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex\n“7. Statistical Analysis and Modeling”: machine learning, modeling, and inference in R\n\n\n\n Back to top" + "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.\nFor the final project, you’re welcome to first check out “1. Data Collection and Preprocessing” and “2. Data Visualization Techniques”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in each of them.\nOther categories cover more advanced skills, which may be outside the scope of the final project but are still extremely interesting, useful, and insightful!\n“3. Interactive and Web-based Applications”: Using Shiny App in R to create interactive online dashboards\n“4. Outside of R”: Data visualization techniques outside of R, most of which are in Python\n“5. Parameter Analysis of Visualization Techniques”: Dives into the mathematical foundation behind visualization techniques\n“6. Programming Techniques and Tools”: Diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex\n“7. Statistical Analysis and Modeling”: Machine learning, modeling, and inference in R\n\n\n\n Back to top" } ] \ No newline at end of file diff --git a/docs/welcome.html b/docs/welcome.html index e360760..937c79e 100644 --- a/docs/welcome.html +++ b/docs/welcome.html @@ -183,13 +183,13 @@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.
-For the final project project, you’re welcome to first check out “1. Data Collection and Preprocessing” and “2. Data Visualization Techniques”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in it.
-Other categories cover more advanced skills, which may not necessarily be within the scope of the final project, but are still extremely interesting, useful, and insightful!
-“3. Interactive and Web-based Applications”: using Shiny App in R to create interactive online dashboards
+For the final project, you’re welcome to first check out “1. Data Collection and Preprocessing” and “2. Data Visualization Techniques”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in each of them.
+Other categories cover more advanced skills, which may be outside the scope of the final project but are still extremely interesting, useful, and insightful!
+“3. Interactive and Web-based Applications”: Using Shiny App in R to create interactive online dashboards
“4. Outside of R”: Data visualization techniques outside of R, most of which are in Python
“5. Parameter Analysis of Visualization Techniques”: Dives into the mathematical foundation behind visualization techniques
-“6. Programming Techniques and Tools”: diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex
-“7. Statistical Analysis and Modeling”: machine learning, modeling, and inference in R
+“6. Programming Techniques and Tools”: Diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex
+“7. Statistical Analysis and Modeling”: Machine learning, modeling, and inference in R
diff --git a/index.qmd b/index.qmd index bbae917..5b4a559 100644 --- a/index.qmd +++ b/index.qmd @@ -12,17 +12,17 @@ This webpage contains community contributions for Fall 2023 EDAV class at Columb There are 108 videos in total that are divided into 7 categories. -For the final project project, you’re welcome to first check out “[1. Data Collection and Preprocessing](https://jtr13.github.io/edav2023/project1.html)” and “[2. Data Visualization Techniques](https://jtr13.github.io/edav2023/project2.html)”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in it. +For the final project, you’re welcome to first check out “[1. Data Collection and Preprocessing](https://jtr13.github.io/edav2023/project1.html)” and “[2. Data Visualization Techniques](https://jtr13.github.io/edav2023/project2.html)”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in each of them. -Other categories cover more advanced skills, which may not necessarily be within the scope of the final project, but are still extremely interesting, useful, and insightful! +Other categories cover more advanced skills, which may be outside the scope of the final project but are still extremely interesting, useful, and insightful! -“[3. Interactive and Web-based Applications](https://jtr13.github.io/edav2023/project3.html)”: using Shiny App in R to create interactive online dashboards +“[3. Interactive and Web-based Applications](https://jtr13.github.io/edav2023/project3.html)”: Using Shiny App in R to create interactive online dashboards “[4. Outside of R](https://jtr13.github.io/edav2023/project7.html)”: Data visualization techniques outside of R, most of which are in Python “[5. Parameter Analysis of Visualization Techniques](https://jtr13.github.io/edav2023/project4.html)”: Dives into the mathematical foundation behind visualization techniques -“[6. Programming Techniques and Tools](https://jtr13.github.io/edav2023/project5.html)”: diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex +“[6. Programming Techniques and Tools](https://jtr13.github.io/edav2023/project5.html)”: Diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex -“[7. Statistical Analysis and Modeling](https://jtr13.github.io/edav2023/project6.html)”: machine learning, modeling, and inference in R +“[7. Statistical Analysis and Modeling](https://jtr13.github.io/edav2023/project6.html)”: Machine learning, modeling, and inference in R diff --git a/welcome.qmd b/welcome.qmd index abd5d07..428f9e6 100644 --- a/welcome.qmd +++ b/welcome.qmd @@ -6,16 +6,16 @@ This webpage contains community contributions for Fall 2023 EDAV class at Columb There are 108 videos in total that are divided into 7 categories. -For the final project project, you’re welcome to first check out “[1. Data Collection and Preprocessing](https://jtr13.github.io/edav2023/project1.html)” and “[2. Data Visualization Techniques](https://jtr13.github.io/edav2023/project2.html)”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in it. +For the final project, you’re welcome to first check out “[1. Data Collection and Preprocessing](https://jtr13.github.io/edav2023/project1.html)” and “[2. Data Visualization Techniques](https://jtr13.github.io/edav2023/project2.html)”, as these are all videos about the basic graphs, skills, and packages in R that are within the scope of the final project. There are also several subcategories in each of them. -Other categories cover more advanced skills, which may not necessarily be within the scope of the final project, but are still extremely interesting, useful, and insightful! +Other categories cover more advanced skills, which may be outside the scope of the final project but are still extremely interesting, useful, and insightful! -“[3. Interactive and Web-based Applications](https://jtr13.github.io/edav2023/project3.html)”: using Shiny App in R to create interactive online dashboards +“[3. Interactive and Web-based Applications](https://jtr13.github.io/edav2023/project3.html)”: Using Shiny App in R to create interactive online dashboards “[4. Outside of R](https://jtr13.github.io/edav2023/project7.html)”: Data visualization techniques outside of R, most of which are in Python “[5. Parameter Analysis of Visualization Techniques](https://jtr13.github.io/edav2023/project4.html)”: Dives into the mathematical foundation behind visualization techniques -“[6. Programming Techniques and Tools](https://jtr13.github.io/edav2023/project5.html)”: diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex +“[6. Programming Techniques and Tools](https://jtr13.github.io/edav2023/project5.html)”: Diverse programming techniques ranging from speeding up codes in R to integrating R with VSCode and Latex -“[7. Statistical Analysis and Modeling](https://jtr13.github.io/edav2023/project6.html)”: machine learning, modeling, and inference in R \ No newline at end of file +“[7. Statistical Analysis and Modeling](https://jtr13.github.io/edav2023/project6.html)”: Machine learning, modeling, and inference in R