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.
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Lecture Title | Time | Video | Material | Course |
|---|---|---|---|---|
| RNA-Seq | ||||
| 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 |
| ChIP-Seq | ||||
| 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 |
| 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 |