13-2 Analyzing Health Text Data - Why Common Words Are Not Useful and How tf-idf Can Help
Karandeep Singh
13-2 Analyzing Health Text Data - Why Common Words Are Not Useful and How tf-idf Can Help
25:23
13-1 Analyzing Health Text Data - Reading in Text Data and Calculating Term Frequencies
Karandeep Singh
13-1 Analyzing Health Text Data - Reading in Text Data and Calculating Term Frequencies
27:22
11-5 Supervised Learning Algorithms - Combining Classifiers
Karandeep Singh
11-5 Supervised Learning Algorithms - Combining Classifiers
21:18
11-4 Supervised Learning Algorithms - Review of Supervised Learning Algorithms
Karandeep Singh
11-4 Supervised Learning Algorithms - Review of Supervised Learning Algorithms
8:34
11-3 Supervised Learning Algorithms - Naive Bayes
Karandeep Singh
11-3 Supervised Learning Algorithms - Naive Bayes
28:35
11-2 Supervised Learning Algorithms - Perceptrons and Neural Networks
Karandeep Singh
11-2 Supervised Learning Algorithms - Perceptrons and Neural Networks
14:34
11-1 Supervised Learning Algorithms - Support Vector Machines
Karandeep Singh
11-1 Supervised Learning Algorithms - Support Vector Machines
22:17
10-4 Machine Learning and Missing Data - Diagnosing Missing Values
Karandeep Singh
10-4 Machine Learning and Missing Data - Diagnosing Missing Values
18:32
10-3 Machine Learning and Missing Data - Why Are the Data Missing?
Karandeep Singh
10-3 Machine Learning and Missing Data - Why Are the Data Missing?
7:48
10-2 Machine Learning and Missing Data - The Missing Data Problem In a Nutshell
Karandeep Singh
10-2 Machine Learning and Missing Data - The Missing Data Problem In a Nutshell
5:01
10-1 Machine Learning and Missing Data - Review of Key ML Concepts and Performance Measures
Karandeep Singh
10-1 Machine Learning and Missing Data - Review of Key ML Concepts and Performance Measures
14:56
9-5 Supervised Learning Algorithms - Lazy Learners
Karandeep Singh
9-5 Supervised Learning Algorithms - Lazy Learners
10:33
9-4 Supervised Learning Algorithms - Decision Trees
Karandeep Singh
9-4 Supervised Learning Algorithms - Decision Trees
34:58
9-3 Supervised Learning Algorithms - Evaluation Measures
Karandeep Singh
9-3 Supervised Learning Algorithms - Evaluation Measures
16:28
9-2 Supervised Learning Algorithms - The Majority Baseline
Karandeep Singh
9-2 Supervised Learning Algorithms - The Majority Baseline
7:53
9-1 Supervised Learning Algorithms - What is Supervised Learning?
Karandeep Singh
9-1 Supervised Learning Algorithms - What is Supervised Learning?
20:23
7-6 Interactive Data Analysis - Live Coding - Converting an Rmd Document to an Interactive Document
Karandeep Singh
7-6 Interactive Data Analysis - Live Coding - Converting an Rmd Document to an Interactive Document
20:26
7-5 Interactive Data Analysis - Converting R Markdown Documents into Interactive Shiny Documents
Karandeep Singh
7-5 Interactive Data Analysis - Converting R Markdown Documents into Interactive Shiny Documents
35:44
7-4 Interactive Data Analysis - Live Coding - Converting an R Notebook into an R Markdown Document
Karandeep Singh
7-4 Interactive Data Analysis - Live Coding - Converting an R Notebook into an R Markdown Document
11:04
7-3 Interactive Data Analysis - Converting R Notebooks into R Markdown Documents
Karandeep Singh
7-3 Interactive Data Analysis - Converting R Notebooks into R Markdown Documents
25:50
8-6 Introduction to ML - Step-by-Step Process for Training and Evaluating Models Using Tidymodels
Karandeep Singh
8-6 Introduction to ML - Step-by-Step Process for Training and Evaluating Models Using Tidymodels
29:19
8-5 Introduction to ML - Common Problems with Fitting and Applying Models
Karandeep Singh
8-5 Introduction to ML - Common Problems with Fitting and Applying Models
24:48
8-4 Introduction to ML - Supervised Learning is a Curve-Fitting Exercise
Karandeep Singh
8-4 Introduction to ML - Supervised Learning is a Curve-Fitting Exercise
23:17
8-3 Introduction to ML - Is a Predictive Model Needed and Should You Develop One?
Karandeep Singh
8-3 Introduction to ML - Is a Predictive Model Needed and Should You Develop One?
15:11
8-2 Introduction to ML - Reinforcement Learning and ML vs Stats Terminology
Karandeep Singh
8-2 Introduction to ML - Reinforcement Learning and ML vs Stats Terminology
9:04
8-1 Introduction to ML - Unsupervised and Supervised Learning
Karandeep Singh
8-1 Introduction to ML - Unsupervised and Supervised Learning
12:44
7-2 Interactive Data Analysis - R Tips of the Day - The magic of !!parse_expr()
Karandeep Singh
7-2 Interactive Data Analysis - R Tips of the Day - The magic of !!parse_expr()
9:17
7-1 Interactive Data Analysis - Overview of Content
Karandeep Singh
7-1 Interactive Data Analysis - Overview of Content
3:44
6-8 Hypothesis Testing - Key Lessons
Karandeep Singh
6-8 Hypothesis Testing - Key Lessons
5:43
6-6 Hypothesis Testing - Which Test, Which Plot
Karandeep Singh
6-6 Hypothesis Testing - Which Test, Which Plot
30:42
6-7 Hypothesis Testing - Multiple Hypothesis Testing
Karandeep Singh
6-7 Hypothesis Testing - Multiple Hypothesis Testing
4:44
6-5 Hypothesis Testing - What Common Statistical Tests Should I Know
Karandeep Singh
6-5 Hypothesis Testing - What Common Statistical Tests Should I Know
6:26
6-3 Hypothesis Testing - What is Hypothesis Testing
Karandeep Singh
6-3 Hypothesis Testing - What is Hypothesis Testing
10:12
6-4 Hypothesis Testing - Why a Null Hypothesis and Interpreting a P-Value
Karandeep Singh
6-4 Hypothesis Testing - Why a Null Hypothesis and Interpreting a P-Value
9:53
6-2 Hypothesis Testing - R Tips of the Day - Replacing and Assigning Missing Values
Karandeep Singh
6-2 Hypothesis Testing - R Tips of the Day - Replacing and Assigning Missing Values
21:44
6-1 Hypothesis Testing - Overview of Content
Karandeep Singh
6-1 Hypothesis Testing - Overview of Content
1:40
5-11 Telling Stories with Plots - Exploring Potential Confounders with Plots
Karandeep Singh
5-11 Telling Stories with Plots - Exploring Potential Confounders with Plots
23:33
5-10 Telling Stories with Plots - Exploring the Relationship Between Weight and Blood Pressure
Karandeep Singh
5-10 Telling Stories with Plots - Exploring the Relationship Between Weight and Blood Pressure
8:53
5-9 Telling Stories with Plots - Components of a ggplot - Coordinates, Scales, and Themes
Karandeep Singh
5-9 Telling Stories with Plots - Components of a ggplot - Coordinates, Scales, and Themes
13:59
5-8 Telling Stories with Plots - Components of a ggplot - Positions, Labels, and Facets
Karandeep Singh
5-8 Telling Stories with Plots - Components of a ggplot - Positions, Labels, and Facets
12:19
5-7 Telling Stories with Plots - Components of a ggplot - Geometric Objects and Mappings
Karandeep Singh
5-7 Telling Stories with Plots - Components of a ggplot - Geometric Objects and Mappings
12:35
5-6 Telling Stories with Plots - Graphics with Grammar
Karandeep Singh
5-6 Telling Stories with Plots - Graphics with Grammar
17:15
5-5 Telling Stories with Plots - Tell the Right Story
Karandeep Singh
5-5 Telling Stories with Plots - Tell the Right Story
6:02
5-4 Telling Stories with Plots - Principles of Visualization
Karandeep Singh
5-4 Telling Stories with Plots - Principles of Visualization
14:20
5-3 Telling Stories with Plots - Mini-Lab - Anscombe’s Quartet
Karandeep Singh
5-3 Telling Stories with Plots - Mini-Lab - Anscombe’s Quartet
3:36
5-2 Telling Stories with Plots - R Tips of the Day - Saving Your Workspace and the Plus Sign
Karandeep Singh
5-2 Telling Stories with Plots - R Tips of the Day - Saving Your Workspace and the Plus Sign
7:04
5-1 Telling Stories with Plots - Overview of Content
Karandeep Singh
5-1 Telling Stories with Plots - Overview of Content
1:23
4-5 Formulating a Question - Study Designs and Bias in Observational Studies
Karandeep Singh
4-5 Formulating a Question - Study Designs and Bias in Observational Studies
4:12
4-4 Formulating a Question - Bradford Hill’s Criteria of Causation
Karandeep Singh
4-4 Formulating a Question - Bradford Hill’s Criteria of Causation
12:07
4-3 Formulating a Question - Dealing with Confounders
Karandeep Singh
4-3 Formulating a Question - Dealing with Confounders
8:19
4-2 Formulating a Question - What Makes a Health-Related Question Answerable
Karandeep Singh
4-2 Formulating a Question - What Makes a Health-Related Question Answerable
14:46
4-1 Formulating a Question - What Makes a Health-Related Question Important
Karandeep Singh
4-1 Formulating a Question - What Makes a Health-Related Question Important
9:44
3-9 Tidy Data - Reviewing the Old and New Verbs of Data Science
Karandeep Singh
3-9 Tidy Data - Reviewing the Old and New Verbs of Data Science
2:54
3-8 Tidy Data - A Challenging Case of Tidying Office Hours Data
Karandeep Singh
3-8 Tidy Data - A Challenging Case of Tidying Office Hours Data
12:51
3-7 Tidy Data - Separating and Uniting Columns
Karandeep Singh
3-7 Tidy Data - Separating and Uniting Columns
6:19
3-6 Tidy Data - Reshaping Data with spread() and gather()
Karandeep Singh
3-6 Tidy Data - Reshaping Data with spread() and gather()
20:37
3-5 Tidy Data - Joining Data Frames
Karandeep Singh
3-5 Tidy Data - Joining Data Frames
14:40
3-4 Tidy Data - Combining mutate() with if_else() and case_when()
Karandeep Singh
3-4 Tidy Data - Combining mutate() with if_else() and case_when()
5:47
3-3 Tidy Data - R Tips of the Day - Dealing with Dates
Karandeep Singh
3-3 Tidy Data - R Tips of the Day - Dealing with Dates
5:59
3-2 Tidy Data - Refresher of data frame verbs
Karandeep Singh
3-2 Tidy Data - Refresher of data frame verbs
2:07
3-1 Tidy Data - Overview of Content
Karandeep Singh
3-1 Tidy Data - Overview of Content
1:30
2-7 Data Frames - Grouping and Combining Verbs
Karandeep Singh
2-7 Data Frames - Grouping and Combining Verbs
15:24
2-6 Data Frames - The Verbs of Data Science
Karandeep Singh
2-6 Data Frames - The Verbs of Data Science
25:15
2-5 Data Frames - Introducing Pipes
Karandeep Singh
2-5 Data Frames - Introducing Pipes
10:07
2-4 Data Frames - Reading Data into R
Karandeep Singh
2-4 Data Frames - Reading Data into R
4:42
2-3 Data Frames - Data Structures and Types in R
Karandeep Singh
2-3 Data Frames - Data Structures and Types in R
8:19
2-2 Data Frames - Refresher of Introductory Content and R Tips of the Day
Karandeep Singh
2-2 Data Frames - Refresher of Introductory Content and R Tips of the Day
8:36
2-1 Data Frames - Overview of Content
Karandeep Singh
2-1 Data Frames - Overview of Content
0:49
1-5 Introduction to LHS 610 - Anatomy of an R Notebook
Karandeep Singh
1-5 Introduction to LHS 610 - Anatomy of an R Notebook
2:23
1-4 Introduction to LHS 610 - Basics of R and RStudio
Karandeep Singh
1-4 Introduction to LHS 610 - Basics of R and RStudio
5:23
1-3 Introduction to LHS 610 - Primary versus secondary use of health data
Karandeep Singh
1-3 Introduction to LHS 610 - Primary versus secondary use of health data
18:16
1-2 Introduction to LHS 610 - Is LHS 610 the right course for you?
Karandeep Singh
1-2 Introduction to LHS 610 - Is LHS 610 the right course for you?
20:53
1-1 Introduction to LHS 610 - What is exploratory data analysis?
Karandeep Singh
1-1 Introduction to LHS 610 - What is exploratory data analysis?
28:19