HarvardX Biomedical Data Science Open Online Training

In 2014 we received funding from the NIH BD2K initiative to develop MOOCs for biomedical data science. The courses are divided into the Data Analysis for the Life Sciences series, the Genomics Data Analysis series, and the Using Python for Research course.

This page includes links to the course material for the three series:

We including video lectures, when available an R markdown document to follow along, and the course itself. Note that you must be logged in to EdX to access the course. Registration is free. We also include links to the course pages.

There is a book available for the first series. You can download a free pdf, buy a hard copy, or access the R markdowns used to create the book.

Data Analysis for the Life Sciences Series

Statistics and R

Lecture Title Time Video Material Course
Week 1: R        
Getting Started with R 06:26 Youtube Chapter 0 EdX
GitHub 03:31 Youtube N/A EdX
RStudio 04:31 Youtube N/A EdX
Using the Textbook 01:35 Youtube N/A EdX
Rstudio for Organization 06:44 Youtube N/A EdX
Introduction to dplyr 08:29 Youtube Chapter 0 EdX
Week 2: Random Variables and Central Limit Theorem        
Motivation 05:05 Youtube N/A EdX
Introduction to Random Variables 04:24 Youtube Chapter 1 EdX
Introduction to Null Distributions 09:36 Youtube Chapter 1 EdX
Probability Distributions 03:08 Youtube Chapter 1 EdX
Normal Distribution 06:29 Youtube Chapter 1 EdX
Populations, Samples and Estimates 04:45 Youtube Chapter 1 EdX
Central Limit Theorem 06:29 Youtube Chapter 1 EdX
Central Limit Theorem in Practice 03:44 Youtube Chapter 1 EdX
t-tests in Practice 05:58 Youtube Chapter 1 EdX
t-tests in Practice (part II) 03:01 Youtube Chapter 1 EdX
Week 3: Inference        
Introduction to Inference 01:37 Youtube N/A EdX
Confidence Intervals 07:51 Youtube Chapter 1 EdX
Power Calculations 07:01 Youtube Chapter 1 EdX
Monte Carlo 05:47 Youtube Chapter 1 EdX
Association Tests 08:28 Youtube Chapter 1 EdX
Week 4: Exploratory Data Analysis and Robust Statistics        
Histogram 04:22 Youtube Chapter 2 EdX
QQ-plot 03:36 Youtube Chapter 2 EdX
Boxplot 02:47 Youtube Chapter 2 EdX
Scatterplot 07:01 Youtube Chapter 2 EdX
Symmetry of Log Ratios 01:49 Youtube Chapter 3 EdX
Plots to Avoid 04:55 Youtube Chapter 2 EdX
Avoid Pseudo 3D 01:19 Youtube Chapter 2 EdX
Median, Mad, and Spearman Correlation 04:24 Youtube Chapter 3 EdX
Mann-Whitney-Wilcoxon Test 03:03 Youtube Chapter 3 EdX

Introduction to Linear Models and Matrix Algebra

Lecture Title Time Video Material Course
Week 1: Matrices        
Course Introduction 00:44 Youtube N/A EdX
Introduction 05:18 Youtube Chapter 4 EdX
Matrix Notations 02:44 Youtube Chapter 4 EdX
Matrix Operations 06:56 Youtube Chapter 4 EdX
Week 2: Matrix Algebra        
Examples 06:27 Youtube N/A EdX
Matrix Algebra in Practice I 08:36 Youtube Chapter4 EdX
Matrix Algebra in Practice II 06:05 Youtube Chapter4 EdX
Standard Errors 05:38 Youtube Chapter5 EdX
Week 3: Linear Models        
Linear Models as Matrix Multiplication I 04:32 Youtube Chapter5 EdX
Linear Models as Matrix Multiplication II 05:42 Youtube Chapter5 EdX
Expressing Experimental Designs 08:01 Youtube Chapter5 EdX
Linear Models in Practice I 06:14 Youtube Chapter5 EdX
Linear Models in Practice II 06:55 Youtube Chapter5 EdX
Fitting Linear Models and Testing 08:28 Youtube Chapter5 EdX
Week 4: Modelling Interactions        
Interactions and Contrasts I 06:28 Youtube Chapter5 EdX
Interactions and Contrasts II 09:59 Youtube Chapter5 EdX
Interactions and Contrasts III 07:40 Youtube Chapter5 EdX
Interactions and Contrasts IV 07:38 Youtube Chapter5 EdX
Interactions and Contrasts V 06:04 Youtube Chapter5 EdX
Collinearity 07:14 Youtube Chapter5 EdX
QR Factorization 13:08 Youtube Chapter5 EdX

Statistical Interference and Modeling for High-throughput Experiments

Lecture Title Time Video Material Course
Week 1: Multiple Comparisons        
An Example of High-throughput Data 06:53 Youtube Chapter6 EdX
The challenge of multiple testing 08:27 Youtube Chapter6 EdX
p-values Are Random Variables 03:23 Youtube Chapter6 EdX
Week 2: Error Rates        
Error Rates and Procedures 04:26 Youtube Chapter6 EdX
Error Rates and Procedures Examples 05:36 Youtube Chapter6 EdX
Bonferroni Correction 03:52 Youtube Chapter6 EdX
False Discovery Rate and Benjamini–Hochberg procedure 08:06 Youtube Chapter6 EdX
q-values 04:05 Youtube Chapter6 EdX
Week 3: Statistical Models        
Introduction to Statistical Models 05:10 Youtube Chapter7 EdX
Poisson Example from RNA-seq 04:20 Youtube Chapter7 EdX
Maximum Likelihood Estimate 04:47 Youtube Chapter7 EdX
Models for Variance 03:50 Youtube Chapter7 EdX
Week 4a: Introduction to Bayesian Analysis        
Bayes’ Rule 05:53 Youtube Chapter7 EdX
Bayes’ Rule in Practice 09:59 Youtube Chapter7 EdX
Hierarchical Models in Practice 04:56 Youtube Chapter7 EdX
Week 4b: Data Visualization        
Volcano plots and p-value histograms, boxplots, and MAplots 08:58 Youtube Chapter7 EdX

High-Dimensional Data Analysis

Lecture Title Time Video Material Course
Week 1: Distance        
Introduction 00:52 Youtube N/A EdX
Distance 03:51 Youtube Chapter8 EdX
Distance (in practice) 00:00 Youtube Chapter8 EdX
Distance Reduction Motivation 00:00 Youtube Chapter8 EdX
Week 2: Principal Component Analysis        
Projections 06:02 Youtube Chapter8 EdX
Rotations 01:54 Youtube Chapter8 EdX
SVD 15:57 Youtube Chapter8 EdX
MDS 05:22 Youtube Chapter8 EdX
PCA 04:51 Youtube Chapter8 EdX
Week 3: Clustering and Machine Learning        
Clustering 06:11 Youtube Chapter9 EdX
How Randomness Affects Clustering 02:45 Youtube Chapter9 EdX
Hiearchichal Clustering in R 07:09 Youtube Chapter9 EdX
K-Means 04:09 Youtube Chapter9 EdX
K-Means Clustering in R 04:37 Youtube Chapter9 EdX
Heat Maps in R 07:18 Youtube Chapter9 EdX
Gene Clustering 06:30 Youtube Chapter9 EdX
Conditional Expectations 03:01 Youtube Chapter9 EdX
Example: Linear Regression 04:56 Youtube Chapter9 EdX
Smoothing 07:45 Youtube Chapter9 EdX
K-Nearest Neighbors 07:44 Youtube Chapter9 EdX
Cross Validation 09:20 Youtube Chapter9 EdX
Week 4: Confounding and Batch Effects        
Confounding 06:46 Youtube Chapter10 EdX
Confounding in Genomics 03:34 Youtube Chapter10 EdX
EDA with PCA 05:12 Youtube Chapter10 EdX
Modeling Batch Effects 06:28 Youtube Chapter10 EdX
ComBat 03:12 Youtube Chapter10 EdX
Factor Analysis 06:17 Youtube Chapter10 EdX
Motivating Factor Analysis 02:21 Youtube Chapter10 EdX
Surrogate Variable Analysis (SVA) 06:04 Youtube Chapter10 EdX

Genomics Data Analysis Series

Introduction to Bioconductor: Annotation and Analysis of Genomes and Genomic Assays

Lecture Title Time Video Material Course
Week 1: Biology background and related computations in R        
Basics of Computing with R for this Course N/A Youtube Setup EdX
Greeting to 5x/three examples of genomic computation 06:49 Youtube N/A EdX
The Bioconductor Portal: Installation, documentation, and help 08:50 Youtube Installation EdX
Overview of What We Measure and Why 01:48 Youtube Review EdX
The molecular basis for phenotypic variation 03:37 Youtube Review EdX
DNA: chromosomes, replication, SNPs and other variants 05:26 Youtube Review EdX
Gene expression 02:36 Youtube Review EdX
Microarray Technology 1: How Hybridization Works 08:46 Youtube N/A EdX
Microarray Technology 2: How Microarrays Work 03:56 Youtube N/A EdX
Microarray Technologies 3: Applications of Microarrays in Genomics 07:25 Youtube N/A EdX
Next Generation Sequencing Technology 1: Brief Introduction to the Mechanics of NGS 08:29 Youtube N/A EdX
Next Generation Sequencing Technology 2: Applications of NGS in Genomics 06:46 Youtube N/A EdX
Mapping Algorithms and Software 11:50 Youtube Mapping EdX
Week 2: Genome-scale data structure and management        
Overview of data management chapter 01:21 Youtube Management EdX
Coordination of disparate data types 05:11 Youtube Management EdX
Composing an ExpressionSet from basic objects 01:40 Youtube Management EdX
A fully annotated ExpressionSet 05:50 Youtube Management EdX
Working with NGS data: a BamFileList 09:39 Youtube Management EdX
SummarizedExperiment in depth 13:36 Youtube Summarized EdX
Managing and processing large numbers of BED files 13:09 Youtube Processing EdX
Week 3a: Genomic ranges and genomic annotation        
Motivation and Introduction 08:35 Youtube N/A EdX
Introduction to Genomic Ranges 09:26 Youtube Ranges EdX
Interval ranges: IRanges 06:37 Youtube Ranges EdX
Genomic ranges: GRanges 08:41 Youtube Ranges EdX
Genomic ranges: GRanges 08:41 Youtube Ranges EdX
Operating on GRanges 09:33 Youtube Ranges EdX
Finding Overlaps 05:57 Youtube Ranges EdX
Genes as GRanges 06:56 Youtube Ranges EdX
Finding the Nearest Gene 07:03 Youtube Ranges EdX
Annotating Genes 04:32 Youtube Ranges EdX
Getting the Sequence of Regions 11:19 Youtube Ranges EdX
Week 3b: Genomic ranges and genomic annotation        
The Human Genome in R 04:51 Youtube Overview EdX
LiftOver: Converting Across Versions of the Human Genome 06:10 Youtube Overview EdX
Exons, Introns, and Transcripts 02:18 Youtube Overview EdX
Exons, Introns, and Transcripts 02:18 Youtube Overview EdX
Transcript Annotations and Gene Models 01:54 Youtube Overview EdX
Importing and Exporting BED files 05:31 Youtube Overview EdX
AnnotationHub 01:18 Youtube Overview EdX
Genome Wide Annotation Packages 03:21 Youtube Overview EdX
Gene Ontology Tables 02:42 Youtube Overview EdX
Kyoto Encyclopedia of Genes and Genomes (KEGG) 03:03 Youtube Overview EdX
More on the Homo.sapiens package 02:40 Youtube Overview EdX
Week 4: Inference for computational biology with Bioconductor        
Biological Versus Technical Variability 06:43 Youtube Variability EdX
t-tests in Genomics 07:01 Youtube Comparisons EdX
Moderated t-tests 12:55 Youtube Moderated EdX
Gene sets 04:59 Youtube Gene Sets EdX
Summary statistics for gene sets 03:59 Youtube Gene Sets EdX
Hypothesis Testing for Gene Sets 05:41 Youtube Gene Sets EdX
Permutations for Gene Set Inference 04:42 Youtube Gene Sets EdX
Gene set testing in R part I 08:29 Youtube Gene Sets EdX
Gene set testing in R part II 00:00 N/A Gene Sets EdX

High-performance computing for reproducible genomics with Bioconductor

Lecture Title Time Video Material Course
Week 1: Visualization of genome-scale data        
Visualizing genomic features: ggbio’s autoplot 03:25 Youtube ggbio EdX
Using Gviz with ESRRA binding and gene annotation 08:25 Youtube Plotting EdX
Function design for genomic visualization 04:39 Youtube N/A EdX
When our graphics query crashes: A look at R’s debugger 04:15 Youtube N/A EdX
Visualizing NGS data part 1 06:22 Youtube NGS EdX
Visualizing NGS data part 2 09:57 Youtube NGS EdX
Visualizing NGS data part 3 06:51 Youtube NGS EdX
Graphical user interfaces with Bioconductor and shiny 10:25 Youtube Graphical EdX
Programming shiny 12:55 Youtube Graphical EdX
Experimentally modifying a shiny app in Rstudio 03:06 Youtube Clustering EdX
Multivariate visualization with gene expression data 06:04 Youtube Clustering EdX
Closing remarks on genome-scale visualization 08:20 Youtube Remarks EdX
Week 2: Scalable genomic analysis: basic concepts        
The parallel package: lapply and mclapply 03:12 Youtube Overview EdX
BiocParallel and implicit parallelization 12:34 Youtube Overview EdX
Introducing the Bioconductor AMI (Amazon Machine Instance) 09:17 Youtube Amazon EdX
Distributed computing for binning reads with BatchJobs 09:36 Youtube Amazon EdX
Packaging and using relational databases: RSQLite 05:41 Youtube Interfacing EdX
Tabix for random access to genomic flat files 06:47 Youtube Interfacing EdX
An introduction to HDF5 with Bioconductor 06:48 Youtube Interfacing EdX
Benchmarking out-of-memory strategies 05:27 Youtube Benchmarking EdX
Scalable interrogation of many ranges via sharding 08:38 Youtube GRanges EdX
Week 3: Multi-omic data integration        
Integrative exploration 1: TF binding and cyclic gene expression in yeast 13:51 Youtube Integration EdX
Integrative exploration 2a: A view of the NHGRI human GWAS catalog 05:28 Youtube Integration EdX
Integrative exploration 2b: ESRRA binding in vicinity of pheno-associated variants 06:15 Youtube Integration EdX
Integrative exploration 3: GEOmetadb for programmatic surveys of GEO 09:27 Youtube Integration EdX
Overview of TCGA with Bioconductor: clinical and molecular data access 02:12 Youtube Overview EdX
Extracting elements from Firehose archives 05:42 Youtube Overview EdX
Survival analysis with TCGA clinical data 04:22 Youtube Overview EdX
Using TCGA mutation records 01:35 Youtube Overview EdX
Linking mutations and tumor stage 03:11 Youtube Overview EdX
Linking expression and stage 03:19 Youtube Overview EdX
Linking methylation and expression 04:17 Youtube Overview EdX
Week 4: Fostering reproducible genome-scale analysis        
Informal discussion of reproducibility concepts 17:50 Youtube Reproducibility EdX
R packages: How they are built 03:45 Youtube R Packages EdX
Installing and testing the erbsViz package 08:38 Youtube R Packages EdX
Creating the Sac.cer3 annotation package 06:45 Youtube R Packages EdX
Software build and check infrastructure 06:56 Youtube R Packages EdX
Quick demonstration of knitr concepts 08:24 Youtube N/A EdX
Working with Bioconductor’s docker containers 09:12 Youtube N/A EdX

Case Studies in Functional Genomics

Lecture Title Time Video Material Course
Introduction to RNA-seq 05:25 Youtube RNA EdX
Data Generation and Counts 04:51 Youtube RNA EdX
Model for Quantification 04:18 Youtube RNA EdX
Transcript Quantification 06:54 Youtube RNA EdX
Unstable Quantification 04:51 Youtube RNA EdX
Links for RNA-seq alignment N/A N/A N/A EdX
Downloading FASTQ files 07:01 Youtube FASTQ EdX
Quality control with FASTQC 08:19 Youtube FASTQ EdX
FASTQC notes N/A N/A N/A EdX
Genome alignment with STAR I 06:56 Youtube STAR EdX
Genome alignment with STAR II 01:42 Youtube STAR EdX
Integrative Genomics Viewer (IGV) 06:31 Youtube RNA EdX
Transcriptome alignment with RSEM I 03:51 Youtube RSEM EdX
Transcriptome alignment with RSEM II 05:33 Youtube RSEM EdX
Install R and Bioconductor 02:51 Youtube Installing EdX
BAM files and GTF file 05:35 Youtube RNA EdX
Building a count matrix 10:02 Youtube RNA EdX
Normalizing for sequencing depth 09:43 Youtube RNA EdX
Transformations and Variance 08:55 Youtube RNA EdX
RNA-seq and ratios of counts 04:51 Youtube RNA EdX
Modeling raw counts 06:07 Youtube RNA EdX
Negative binomial distribution 03:37 Youtube RNA EdX
Negative binomial distribution 03:37 Youtube RNA EdX
Running DESeq2 07:13 Youtube RNA EdX
Plotting results: MA-plot 04:50 Youtube RNA EdX
Plot counts for one gene 06:01 Youtube RNA EdX
Fast, pseudoaligners for RNA-seq N/A N/A RNA EdX
Isoform or exon-level expression 04:07 Youtube Exon EdX
Differential exon usage with DEXSeq 07:51 Youtube Exon EdX
Differential isoform expression with Cufflinks/cummeRbund 06:08 Youtube Exploring EdX
DNA Methylation        
Epigenetics 02:08 Youtube Methylation EdX
DNA Methylation 03:22 Youtube Methylation EdX
CpG islands 04:06 Youtube Methylation EdX
Bisulfite Treatment 01:31 Youtube 450K EdX
Measuring Methylation with the 450K Array 01:52 Youtube 450K EdX
Statistical Considerations 08:20 Youtube 450K EdX
DNA Methylation Data Analysis in R 11:30 Youtube 450K EdX
Finding Differentially Methylated Regions in R 13:08 Youtube 450K EdX
Reading Raw 450K Array Data 08:34 Youtube 450K EdX
Reading Raw 450K Array Data 08:34 Youtube 450K EdX
Downloading Data N/A N/A N/A EdX
InferenceForDNAmeth 07:54 Youtube Inference EdX
InferenceForDNAMethInR 08:49 Youtube Inference EdX
CpGIslandShores 10:23 Youtube N/A EdX
cellComposition 08:24 Youtube N/A EdX
blocks 05:41 Youtube N/A EdX
Measuring Methylation from Sequencing 01:52 Youtube N/A EdX
Introduction to transcription regulation 06:42 Youtube Chip EdX
ChIP-seq technique 06:50 Youtube Chip EdX
ChIP-seq peak calling 11:44 Youtube Chip EdX
ChIP-seq Quality Control 1 14:32 Youtube Chip EdX
ChIP-seq quality control 2 08:31 Youtube Chip EdX
ChIP-seq Target Genes 13:10 Youtube Chip EdX
ChIP-seq Example 09:45 Youtube Chip EdX
CISTROME 07:51 Youtube N/A EdX
Cistrome Analysis Pipeline Hands-On 09:53 Youtube N/A EdX
BETA Software Suite 13:09 Youtube N/A EdX

Using Python for Research

Series Number Lecture Title Time Video
Series 1.0      
0.1 Why Program? Why Python? 04:34 Youtube
1.1.1 Python Basics 04:30 Youtube
1.1.2 Objects 04:39 Youtube
1.1.3 Modules and Methods 07:29 Youtube
1.1.4 Numbers and Basic Calulations 04:25 Youtube
1.1.5 Random Choice 01:55 Youtube
1.1.6 Expressions and Booleans 05:52 Youtube
1.2.1 Sequences 03:21 Youtube
1.2.2 Lists 07:12 Youtube
1.2.3 Tuples 06:36 Youtube
1.2.4 Ranges 02:46 Youtube
1.2.5 Strings 08:38 Youtube
1.2.6 Sets 07:02 Youtube
1.2.7 Dictionaries 08:09 Youtube
1.3.1 Dynamic Typing 11:25 Youtube
1.3.2 Copies 01:58 Youtube
1.3.3 Statements 04:35 Youtube
1.3.4 For and While Loops 08:05 Youtube
1.3.5 List Comprehensions 02:37 Youtube
1.3.6 Reading and Writing Files 05:20 Youtube
1.3.7 Introduction to Functions 05:23 Youtube
1.3.8 Writing Simple Functions 09:36 Youtube
1.3.9 Common Mistakes and Errors 06:44 Youtube
Series 2.0      
2.1.1 Scope Rules 08:29 Youtube
2.1.2 Classes and Object-Oriented Programming 07:34 Youtube
2.2.1 Introduction to NumPy Arrays 06:26 Youtube
2.2.2 Slicing NumPy Arrays 05:13 Youtube
2.2.3 Indexing NumPy Arrays 07:20 Youtube
2.2.4 Building and Examining NumPy Arrays 05:52 Youtube
2.3.1 Introduction to Matplotlib and Pyplot 08:21 Youtube
2.3.2 Customizing Your Plots 05:28 Youtube
2.3.3 Plotting Using Logarithmic Axes 05:07 Youtube
2.3.4 Generating Histograms 07:46 Youtube
2.4.1 Simulating Randomness 07:41 Youtube
2.4.2 Examples Involving Randomness 13:40 Youtube
2.4.3 Using the NumPy Random Module 11:36 Youtube
2.4.4 Measuring Time 03:42 Youtube
2.4.5 Random Walks 16:40 Youtube
Series 3.0      
3.1.1 Introduction to DNA Translation 04:44 Youtube
3.1.2 Downloading DNA Data 04:22 Youtube
3.1.3 Importing DNA Data Into Python 04:57 Youtube
3.1.4 Translating the DNA Sequence 12:26 Youtube
3.1.5 Comparing Your Translation 08:19 Youtube
3.2.1 Introduction to Language Processing 02:16 Youtube
3.2.2 Counting Words 10:33 Youtube
3.2.3 Reading in a Book 03:55 Youtube
3.2.4 Computing Word Frequency Statistics 04:41 Youtube
3.2.5 Reading Multiple Files 11:47 Youtube
3.2.6 Plotting Book Statistics 06:03 Youtube
3.3.1 Introduction to kNN Classification 03:22 Youtube
3.3.2 Finding the Distance Between Two Points 05:22 Youtube
3.3.3 Majority Vote 11:24 Youtube
3.3.4 Finding Nearest Neighbors 14:02 Youtube
3.3.5 Generating Synthetic Data 07:14 Youtube
3.3.6 Making a Prediction Grid 10:44 Youtube
3.3.7 Plotting the Prediction Grid 04:14 Youtube
3.3.8 Applying the kNN Method 09:56 Youtube
Series 4.0      
4.1.1 Getting Started With Pandas 11:02 Youtube
4.1.2 Loading and Inspecting Data 04:28 Youtube
4.1.3 Exploring Correlations 05:37 Youtube
4.1.4 Clustering Whiskies by Flavor Profile 06:54 Youtube
4.1.5 Comparing Correlation Matrices 03:59 Youtube
4.2.1 Introduction to GPS Tracking of Birds 02:54 Youtube
4.2.2 Simple Data Visualizations 05:19 Youtube
4.2.3 Examining Flight Speed 06:51 Youtube
4.2.4 Using Datetime 11:13 Youtube
4.2.5 Calculating Daily Mean Speed 07:49 Youtube
4.2.6 Using the Cartopy Library 05:22 Youtube
4.3.1 Introduction to Network Analysis 05:50 Youtube
4.3.2 Basics of NetworkX 05:47 Youtube
4.3.3 Graph Visualization 04:03 Youtube
4.3.4 Random Graphs 11:25 Youtube
4.3.5 Plotting the Degree Distribution 05:51 Youtube
4.3.6 Descriptive Statistics of Empirical Social Networks 07:17 Youtube
4.3.7 Finding the Largest Connected Component 10:00 Youtube
Series 5.0      
5.1.1 Introduction to Statistcal Learning 08:11 Youtube
5.1.2 Generating example regression data 05:03 Youtube
5.1.3 Simple linear regression 03:38 Youtube
5.1.4 Least squares estimation in code 05:02 Youtube
5.1.5 Simple linear regression in code 07:22 Youtube
5.1.6 Multiple linear regression 01:43 Youtube
5.1.7 Scikit learn for Linear Regression 09:15 Youtube
5.1.8 Assessing Model Accuracy 08:49 Youtube
5.2.1 Generating Example Classification Data 07:59 Youtube
5.2.2 Logistic Regression 05:10 Youtube
5.2.3 Logistic Regression in Code 10:50 Youtube
5.2.4 Computing Predictive Probability Across the Grid 09:29 Youtube
5.3.1 Tree-Based Methods for Regression and Classification 07:12 Youtube
5.3.2 Random Forest Predictions 04:24 Youtube