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种子名称： introduction to Rprogramming Hopkins
文件类型： 视频
文件数目： 306个文件
文件大小： 2.66 GB
收录时间： 2017-9-19 12:46
已经下载： 3次
资源热度： 184
最近下载： 2024-12-17 08:09

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introduction to Rprogramming Hopkins.torrent

4-Exploratory Data Analysis/5 - 2 - Air Pollution Case Study [40-35].mp472.01MB

5-Reproducible Research/4 - 3 - Case Study- High Throughput Biology [30-51].mp450.56MB

9-Data Product/4 - 6 - yhat (Part 1) (24-39).mp440.06MB

7-Regression Models/3 - 2 - 02_02_b Dummy variables (27-08).mp435.23MB

7-Regression Models/3 - 3 - 02_02_c Interactions (26-29).mp434.41MB

9-Data Product/4 - 3 - Building R Packages Demo (18-00).mp431.99MB

8-machine learning/2 - 6 - Covariate creation (17-31).mp422.77MB

9-Data Product/4 - 7 - yhat (Part 2) (11-38).mp421.23MB

5-Reproducible Research/1 - 7 - Structure of a Data Analysis (part 2) [17-41].mp421.04MB

5-Reproducible Research/4 - 2 - Case Study- Air Pollution [14-12].mp420.91MB

6-Statistical Inference/1 - 1 - 01_01_a Introduction, motivating examples (14-23).mp420.45MB

7-Regression Models/3 - 1 - 02_02_a Multivariable regression examples (14-38).mp419.5MB

7-Regression Models/2 - 9 - 01_07_c Prediction Intervals (14-13).mp418.96MB

5-Reproducible Research/2 - 1 - Coding Standards in R [8-59].mp418.91MB

9-Data Product/4 - 4 - R Classes and Methods (Part 1) (13-50).mp418.12MB

7-Regression Models/4 - 8 - 03_03_b Poisson Regression Example (14-12).mp417.8MB

6-Statistical Inference/2 - 5 - 02_01_b Gaussian (13-50).mp417.68MB

9-Data Product/3 - 8 - RStudio Presenter 2 Authoring details (11-14).mp417.63MB

8-machine learning/2 - 7 - Preprocessing with principal components analysis (14-07).mp417.4MB

6-Statistical Inference/4 - 5 - 03_05_a Multiple testing (13-59).mp417.15MB

9-Data Product/4 - 2 - R Packages (Part 2) (14-59).mp417.1MB

8-machine learning/4 - 1 - Regularized regression (13-20).mp416.76MB

4-Exploratory Data Analysis/5 - 1 - Clustering Case Study [14-51].mp416.76MB

5-Reproducible Research/4 - 1 - Caching Computations [11-16].mp416.5MB

2-R-programming/3 - 5 - Your First R Function [10-29].mp416.48MB

7-Regression Models/2 - 8 - 01_07_b T Tests for Regression Coefficients (12-33).mp416.26MB

8-machine learning/3 - 1 - Predicting with trees (12-51).mp416.18MB

6-Statistical Inference/2 - 3 - 01_05_c Bayes_' Rule Example- Diagnostic Tests (12-52).mp416.06MB

4-Exploratory Data Analysis/2 - 8 - Base Plotting Demonstration [16-56].mp416.04MB

8-machine learning/2 - 8 - Predicting with Regression (12-22).mp415.8MB

7-Regression Models/4 - 9 - 03_03_c Poisson Rate Models (12-53).mp415.8MB

5-Reproducible Research/3 - 4 - Reproducible Research Checklist (part 2) [10-20].mp415.36MB

7-Regression Models/4 - 6 - 03_02_c More on Odds (12-29).mp415.3MB

5-Reproducible Research/1 - 6 - Structure of a Data Analysis (part 1) [12-29].mp414.9MB

2-R-programming/3 - 6 - Coding Standards [8-59].mp414.81MB

6-Statistical Inference/3 - 6 - 02_05_c example and credible intervals (11-04).mp414.56MB

8-machine learning/3 - 5 - Model Based Prediction (11-39).mp414.55MB

8-machine learning/2 - 9 - Predicting with Regression Multiple Covariates (11-12).mp414.5MB

7-Regression Models/1 - 9 - 01_03_c Linear Least Squares Solved (11-33).mp414.41MB

6-Statistical Inference/4 - 4 - 03_04_b Power continued (11-26).mp414.25MB

8-machine learning/2 - 4 - Plotting predictors (10-39).mp414.21MB

6-Statistical Inference/2 - 4 - 02_01_a Bernoulli and Binomial (11-13).mp414.18MB

7-Regression Models/2 - 6 - 01_06_c Residual Variation (11-20).mp414.12MB

5-Reproducible Research/1 - 8 - Organizing Your Analysis [11-05].mp414.12MB

6-Statistical Inference/3 - 11 - 03_02_c Hypothesis testing example and binomial example (10-52).mp413.96MB

7-Regression Models/1 - 11 - 01_04_b Regression to the Mean Example (10-46).mp413.92MB

8-machine learning/1 - 6 - Types of errors (10-35).mp413.87MB

6-Statistical Inference/1 - 9 - 01_04_b Correlation, Variances and IID RVs (11-03).mp413.85MB

8-machine learning/2 - 5 - Basic preprocessing (10-52).mp413.6MB

9-Data Product/2 - 19 - plotly.mp413.36MB

7-Regression Models/3 - 12 - 02_05_b Variance inflation (10-33).mp413.3MB

1-The Data Scientist’s Toolbox/3 - 4 - Experimental Design (15-59).mp413.28MB

9-Data Product/4 - 5 - R Classes and Methods (Part 2) (11-19).mp413.28MB

6-Statistical Inference/1 - 3 - 01_02_b Random variables, densities and pmfs (10-19).mp412.8MB

6-Statistical Inference/3 - 9 - 03_02_a Introduction to hypothesis testing (9-57).mp412.63MB

6-Statistical Inference/3 - 10 - 03_02_b Further discussion of hypothesis testing (9-58).mp412.6MB

9-Data Product/2 - 8 - More advanced shiny discussion, reactivity (9-30).mp412.55MB

5-Reproducible Research/2 - 8 - knitr (part 4) [9-21].mp412.47MB

9-Data Product/2 - 17 - GoogleVis (9-34).mp412.45MB

1-The Data Scientist’s Toolbox/2 - 1 - Command Line Interface (16-04).mp412.37MB

8-machine learning/1 - 3 - Relative importance of steps (9-45).mp412.31MB

3-gettting and cleaning data/2 - 1 - Reading from MySQL (14-44).mp412.23MB

4-Exploratory Data Analysis/3 - 4 - ggplot2 (part 2) [13-53].mp412.19MB

6-Statistical Inference/4 - 9 - 03_06_b Resampling the bootstrap (9-34).mp412.12MB

6-Statistical Inference/3 - 3 - 02_04_c maximum likelihood (9-35).mp412.02MB

7-Regression Models/2 - 12 - 02_01_c More Multivariable Least Squares (8-35).mp411.85MB

6-Statistical Inference/4 - 3 - 03_04_a Power (9-15).mp411.75MB

6-Statistical Inference/2 - 9 - 02_02_c Asymptotic Confidence Intervals (9-12).mp411.75MB

6-Statistical Inference/2 - 6 - 02_01_c Poisson (9-44).mp411.75MB

2-R-programming/2 - 2 - Overview and History of R [16-07].mp411.59MB

6-Statistical Inference/4 - 6 - 03_05_b Multiple testing further discussion (11-23).mp411.54MB

3-gettting and cleaning data/1 - 7 - Reading XML (12-39).mp411.51MB

8-machine learning/3 - 2 - Bagging (9-13).mp411.45MB

2-R-programming/5 - 5 - R Profiler (part 2) [10-26].mp411.23MB

2-R-programming/1 - 5 - Writing Code - Setting Your Working Directory (Mac).mp411.21MB

6-Statistical Inference/1 - 7 - 01_03_c Variances (8-51).mp411.15MB

7-Regression Models/3 - 13 - 02_05_c Model comparison and search (8-05).mp411.14MB

8-machine learning/1 - 5 - Prediction study design (9-05).mp411.13MB

5-Reproducible Research/3 - 3 - Reproducible Research Checklist (part 1) [8-22].mp411.1MB

7-Regression Models/2 - 5 - 01_06_b Properties of Residuals (8-48).mp411.03MB

8-machine learning/1 - 2 - What is prediction- (8-39).mp410.98MB

9-Data Product/2 - 3 - Shiny 1 Introduction to Shiny (8-36).mp410.89MB

4-Exploratory Data Analysis/2 - 2 - Principles of Analytic Graphics [12-11].mp410.79MB

6-Statistical Inference/4 - 10 - 03_06_c Permutation tests (8-23).mp410.7MB

6-Statistical Inference/4 - 7 - 03_05_c Multiple testing case studies (9-03) .mp410.68MB

6-Statistical Inference/3 - 2 - 02_04_b Likelihood example, binomial (8-24).mp410.64MB

8-machine learning/4 - 3 - Forecasting.mp410.6MB

8-machine learning/1 - 1 - Prediction motivation (8-26).mp410.49MB

5-Reproducible Research/1 - 5 - Scripting Your Analysis [4-36].mp410.2MB

6-Statistical Inference/4 - 2 - 03_03_b P-values some examples and the attained significance level (7-59).mp410.14MB

8-machine learning/1 - 8 - Cross validation (8-20).mp410.1MB

5-Reproducible Research/1 - 2 - Reproducible Research- Concepts and Ideas (part 1) [7-11].mp410.08MB

9-Data Product/3 - 10 - Very quick introduction to gh-pages.mp410.07MB

5-Reproducible Research/2 - 4 - R Markdown Demonstration [7-24].mp410.04MB

6-Statistical Inference/1 - 10 - 01_04_c Sample Variance (8-17).mp410.03MB

7-Regression Models/4 - 7 - 03_03_a Poisson Regression (8-15).mp49.91MB

6-Statistical Inference/1 - 6 - 01_03_b Continuous Random Variables, Rules for Expected Values (8-18).mp49.91MB

6-Statistical Inference/2 - 11 - 02_03_b T distribution and T intervals (evaluated in Quiz 3) (7-37).mp49.83MB

6-Statistical Inference/2 - 7 - 02_02_a Limits LLN (7-25).mp49.82MB

4-Exploratory Data Analysis/3 - 6 - ggplot2 (part 4) [10-38].mp49.78MB

7-Regression Models/1 - 4 - 01_01_d Regression through the origin (7-37).mp49.7MB

1-The Data Scientist’s Toolbox/1 - 1 - Series Motivation (12-03).mp49.6MB

3-gettting and cleaning data/3 - 2 - Summarizing Data (11-37).mp49.56MB

5-Reproducible Research/2 - 5 - knitr (part 1) [7-05].mp49.47MB

5-Reproducible Research/3 - 10 - Evidence-based Data Analysis (part 5) [7-56].mp49.34MB

8-machine learning/4 - 2 - Combining predictors (7-11).mp49.31MB

9-Data Product/3 - 5 - Slidify more details (7-24).mp49.29MB

2-R-programming/2 - 9 - Reading and Writing Data (part 1) [12-55].mp49.24MB

4-Exploratory Data Analysis/2 - 6 - Base Plotting System (part 1) [11-20].mp49.21MB

2-R-programming/5 - 4 - R Profiler (part 1) [10-39].mp49.17MB

2-R-programming/2 - 3 - Getting Help [13-53].mp49.16MB

7-Regression Models/3 - 11 - 02_05_a Some thoughts on model selection (6-38).mp49.13MB

9-Data Product/4 - 1 - R Packages (Part 1) (7-11).mp49.07MB

8-machine learning/3 - 4 - Boosting (7-08).mp49.07MB

7-Regression Models/4 - 3 - 03_01_c Variances and Quasi Likelihood (7-05).mp49.01MB

8-machine learning/2 - 3 - Training options (7-15).mp49.01MB

3-gettting and cleaning data/4 - 1 - Editing Text Variables (10-46).mp48.98MB

7-Regression Models/4 - 4 - 03_02_a Binary Data GLMs (7-11).mp48.93MB

5-Reproducible Research/3 - 5 - Reproducible Research Checklist (part 3) [6-54].mp48.92MB

3-gettting and cleaning data/1 - 9 - The data.table Package (11-18).mp48.89MB

2-R-programming/1 - 4 - Writing Code - Setting Your Working Directory (Windows).mp48.87MB

6-Statistical Inference/2 - 8 - 02_02_b CLT (6-55).mp48.79MB

8-machine learning/3 - 3 - Random Forests (6-49).mp48.73MB

4-Exploratory Data Analysis/3 - 5 - ggplot2 (part 3) [9-47].mp48.68MB

8-machine learning/1 - 4 - In and out of sample errors (6-57).mp48.67MB

9-Data Product/3 - 7 - RStudio Presenter 1 Introduction and getting started (4-59).mp48.61MB

3-gettting and cleaning data/3 - 3 - Creating New Variables (10-32).mp48.5MB

9-Data Product/2 - 16 - rCharts mapping and discussion (5-32).mp48.44MB

5-Reproducible Research/2 - 9 - Introduction to Peer Assessment 1.mp48.43MB

7-Regression Models/2 - 2 - 01_05_b Interpreting Regression Coefficients (6-28).mp48.43MB

6-Statistical Inference/3 - 4 - 02_05_a Introduction to Bayesian analysis (6-42).mp48.43MB

2-R-programming/4 - 6 - Debugging Tools (part 1) [9-26].mp48.37MB

4-Exploratory Data Analysis/2 - 5 - Plotting Systems in R [9-34].mp48.33MB

8-machine learning/2 - 1 - Caret package (6-16).mp48.23MB

6-Statistical Inference/3 - 7 - 03_01_a Two group intervals, T intervals with a common variance (6-20).mp48.2MB

5-Reproducible Research/3 - 1 - Communicating Results [6-54].mp48.2MB

6-Statistical Inference/1 - 2 - 01_02_a Basic probability (6-19).mp48.13MB

2-R-programming/2 - 6 - Data Types (part 3) [11-51].mp48.12MB

6-Statistical Inference/3 - 8 - 03_01_b Two group T test examples (6-17).mp48.06MB

6-Statistical Inference/4 - 1 - 03_03_a P-values, introduction (6-01).mp47.98MB

7-Regression Models/4 - 2 - 03_01_b GLM Examples (6-21).mp47.9MB

5-Reproducible Research/2 - 3 - R Markdown [6-35].mp47.85MB

8-machine learning/1 - 9 - What data should you use- (6-01).mp47.77MB

6-Statistical Inference/1 - 5 - 01_03_a Expected Values, Discrete Random Variables (5-51).mp47.71MB

1-The Data Scientist’s Toolbox/3 - 1 - Types of Questions (9-09).mp47.67MB

6-Statistical Inference/4 - 8 - 03_06_a Resampling the jackknife (6-01).mp47.66MB

6-Statistical Inference/1 - 4 - 01_02_c Distribution functions and quantiles (6-06).mp47.64MB

2-R-programming/3 - 7 - Scoping Rules (part 1) [10-32].mp47.6MB

7-Regression Models/2 - 3 - 01_05_c Statistical Regression Models Examples (6-00).mp47.58MB

9-Data Product/2 - 15 - rCharts more examples (5-40).mp47.58MB

3-gettting and cleaning data/1 - 3 - Components of Tidy Data (9-25).mp47.57MB

7-Regression Models/3 - 9 - 02_04_b More on diagnostics (5-18).mp47.56MB

5-Reproducible Research/1 - 3 - Reproducible Research- Concepts and Ideas (part 2) [5-27].mp47.51MB

7-Regression Models/3 - 4 - 02_03_a Multivariable simulation exercises (5-42).mp47.5MB

7-Regression Models/1 - 3 - 01_01_c Least squares continued (5-38).mp47.49MB

7-Regression Models/2 - 1 - 01_05_a Statistical Linear Regression Models (5-58).mp47.41MB

9-Data Product/3 - 1 - Presenting Data Analysis Writing a Data Report (3-18).mp47.35MB

7-Regression Models/1 - 7 - 01_03_a Linear Least Squares (6-01).mp47.29MB

4-Exploratory Data Analysis/3 - 7 - ggplot2 (part 5) [8-11].mp47.26MB

5-Reproducible Research/2 - 2 - Markdown [5-15].mp47.25MB

3-gettting and cleaning data/3 - 4 - Reshaping Data (9-13).mp47.21MB

6-Statistical Inference/2 - 2 - 01_05_b Bayes_' Rule (5-54).mp47.21MB

9-Data Product/3 - 2 - Slidify intro (5-32).mp47.19MB

7-Regression Models/1 - 2 - 01_01_b Basic least squares (5-41).mp47.15MB

4-Exploratory Data Analysis/4 - 7 - Dimension Reduction (part 2) [9-26].mp47.15MB

8-machine learning/2 - 2 - Data slicing (5-40).mp47MB

2-R-programming/3 - 10 - Dates and Times [10-29].mp46.99MB

4-Exploratory Data Analysis/2 - 3 - Exploratory Graphs (part 1) [9-28].mp46.89MB

9-Data Product/2 - 9 - More advanced shiny, the reactive function (5-50).mp46.82MB

7-Regression Models/1 - 6 - 01_02_b Normalization and Correlation (5-22).mp46.75MB

1-The Data Scientist’s Toolbox/1 - 3 - Getting Help (8-52).mp46.71MB

6-Statistical Inference/3 - 5 - 02_05_b posteriors (5-21).mp46.65MB

2-R-programming/2 - 5 - Data Types (part 2) [9-45].mp46.6MB

2-R-programming/2 - 8 - Subsetting (part 2) [10-18].mp46.58MB

2-R-programming/2 - 10 - Reading and Writing Data (part 2) [9-30].mp46.57MB

2-R-programming/2 - 4 - Data Types (part 1) [9-26].mp46.57MB

9-Data Product/2 - 7 - Shiny 5 Discussion (4-48).mp46.51MB

6-Statistical Inference/3 - 1 - 02_04_a Introduction to likelihoods (5-05).mp46.49MB

3-gettting and cleaning data/4 - 3 - Regular Expressions II (8-00).mp46.46MB

2-R-programming/3 - 3 - Functions (part 1) [9-17].mp46.46MB

3-gettting and cleaning data/2 - 4 - Reading From APIs (7-57).mp46.37MB

2-R-programming/5 - 6 - Scoping Rules (part 3) [9-21].mp46.35MB

9-Data Product/2 - 11 - More advanced shiny, odds and ends (4-55).mp46.32MB

2-R-programming/4 - 4 - split [9-09].mp46.16MB

4-Exploratory Data Analysis/2 - 10 - Graphics Devices in R (part 2) [7-31].mp46.14MB

9-Data Product/2 - 12 - Manipulate (4-49).mp46.12MB

5-Reproducible Research/2 - 7 - knitr (part 3) [4-46].mp46.12MB

2-R-programming/4 - 1 - lapply [9-23].mp46.1MB

8-machine learning/1 - 7 - Receiver Operating Characteristic (5-03).mp46.07MB

7-Regression Models/3 - 8 - 02_04_a Residuals (4-48).mp45.98MB

9-Data Product/2 - 4 - Shiny 2 basic html and getting input (4-56).mp45.98MB

3-gettting and cleaning data/1 - 2 - Raw and Processed Data (7-07).mp45.95MB

4-Exploratory Data Analysis/3 - 3 - ggplot2 (part 1) [6-26].mp45.91MB

3-gettting and cleaning data/1 - 4 - Downloading Files (7-09).mp45.9MB

4-Exploratory Data Analysis/4 - 6 - Dimension Reduction (part 1) [7-55].mp45.89MB

5-Reproducible Research/1 - 1 - Introduction.mp45.86MB

9-Data Product/2 - 18 - shinyApps.io.mp45.85MB

9-Data Product/2 - 14 - rCharts introduction (4-45).mp45.83MB

2-R-programming/5 - 1 - The str Function [6-08].mp45.78MB

7-Regression Models/3 - 7 - 02_03_d Simulation examples finished (4-22).mp45.77MB

9-Data Product/3 - 9 - RStudio Presenter 3 Discussion and comparison with Slidify (4-13).mp45.75MB

2-R-programming/3 - 8 - Scoping Rules (part 2) [8-34].mp45.7MB

3-gettting and cleaning data/3 - 5 - Merging Data (6-19).mp45.69MB

3-gettting and cleaning data/2 - 3 - Reading from The Web (6-47).mp45.59MB

5-Reproducible Research/3 - 9 - Evidence-based Data Analysis (part 4) [4-47].mp45.54MB

7-Regression Models/1 - 1 - 01_01_a Introduction to regression (4-10).mp45.54MB

9-Data Product/3 - 4 - Slidify customization (4-09).mp45.5MB

2-R-programming/3 - 2 - Control Structures (part 2) [8-11].mp45.5MB

3-gettting and cleaning data/2 - 2 - Reading from HDF5 (6-45).mp45.47MB

4-Exploratory Data Analysis/4 - 10 - Working with Color in R Plots (part 2) [7-41].mp45.46MB

9-Data Product/2 - 10 - More advanced shiny, conditional execution of reactive statements (4-16).mp45.42MB

4-Exploratory Data Analysis/2 - 7 - Base Plotting System (part 2) [6-56].mp45.41MB

5-Reproducible Research/2 - 6 - knitr (part 2) [4-11].mp45.4MB

8-machine learning/4 - 4 - Unsupervised Prediction (4-24).mp45.4MB

2-R-programming/4 - 8 - Debugging Tools (part 3) [11-51].mp45.37MB

4-Exploratory Data Analysis/4 - 3 - Hierarchical Clustering (part 3) [7-34].mp45.36MB

2-R-programming/5 - 2 - Simulation (part 1) [7-47].mp45.34MB

6-Statistical Inference/2 - 10 - 02_03_a Chi Squared Distribution (4-05).mp45.31MB

6-Statistical Inference/1 - 8 - 01_04_a Basic Independence (4-13).mp45.28MB

5-Reproducible Research/3 - 2 - RPubs [3-21].mp45.22MB

7-Regression Models/1 - 8 - 01_03_b Linear Least Squares Special Cases (4-22).mp45.21MB

2-R-programming/1 - 1 - Installing R on Windows.mp45.12MB

1-The Data Scientist’s Toolbox/1 - 16 - Installing R on Windows (3-20) {Roger Peng}.mp45.12MB

9-Data Product/2 - 5 - Shiny 3 Creating a very basic prediction function (4-12).mp45.07MB

4-Exploratory Data Analysis/4 - 8 - Dimension Reduction (part 3) [6-42].mp45.06MB

7-Regression Models/1 - 10 - 01_04_a Regression to the Mean (3-46).mp45.04MB

4-Exploratory Data Analysis/4 - 1 - Hierarchical Clustering (part 1) [7-21].mp45.03MB

5-Reproducible Research/1 - 4 - Reproducible Research- Concepts and Ideas (part 3) [3-26].mp44.99MB

3-gettting and cleaning data/1 - 1 - Obtaining Data Motivation (5-38).mp44.98MB

6-Statistical Inference/2 - 1 - 01_05_a Conditional probability (4-01).mp44.97MB

2-R-programming/4 - 2 - apply [7-21].mp44.96MB

4-Exploratory Data Analysis/3 - 2 - Lattice Plotting System (part 2) [6-12].mp44.96MB

3-gettting and cleaning data/3 - 1 - Subsetting and Sorting (6-51).mp44.94MB

4-Exploratory Data Analysis/3 - 1 - Lattice Plotting System (part 1) [6-22].mp44.92MB

2-R-programming/4 - 7 - Debugging Tools (part 2) [6-25].mp44.92MB

5-Reproducible Research/3 - 8 - Evidence-based Data Analysis (part 3) [4-25].mp44.86MB

2-R-programming/3 - 4 - Functions (part 2) [7-13].mp44.86MB

1-The Data Scientist’s Toolbox/2 - 4 - Creating a Github Repository (5-51).mp44.84MB

7-Regression Models/3 - 5 - 02_03_b More simulation exercises (3-53).mp44.84MB

4-Exploratory Data Analysis/2 - 9 - Graphics Devices in R (part 1) [5-34].mp44.84MB

2-R-programming/3 - 1 - Control Structures (part 1) [7-10].mp44.82MB

4-Exploratory Data Analysis/4 - 11 - Working with Color in R Plots (part 3) [6-39].mp44.8MB

1-The Data Scientist’s Toolbox/2 - 7 - Installing R Packages (5-37).mp44.8MB

2-R-programming/5 - 3 - Simulation (part 2) [7-02].mp44.8MB

3-gettting and cleaning data/4 - 4 - Working with Dates (6-02).mp44.7MB

1-The Data Scientist’s Toolbox/3 - 2 - What is Data- (5-15).mp44.68MB

2-R-programming/2 - 7 - Subsetting (part 1) [7-01].mp44.62MB

3-gettting and cleaning data/1 - 8 - Reading JSON (5-03).mp44.59MB

5-Reproducible Research/3 - 6 - Evidence-based Data Analysis (part 1) [3-51].mp44.54MB

1-The Data Scientist’s Toolbox/2 - 5 - Basic Git Commands (5-52).mp44.43MB

1-The Data Scientist’s Toolbox/1 - 2 - The Data Scientist-'s Toolbox (5-09).mp44.41MB

3-gettting and cleaning data/1 - 5 - Reading Local Files (4-55).mp44.39MB

7-Regression Models/1 - 5 - 01_02_a Basic Notation and Background (3-26).mp44.31MB

3-gettting and cleaning data/4 - 2 - Regular Expressions I (5-16).mp44.15MB

5-Reproducible Research/3 - 7 - Evidence-based Data Analysis (part 2) [3-34].mp44.1MB

2-R-programming/2 - 1 - Introduction.mp44.09MB

1-The Data Scientist’s Toolbox/2 - 2 - Introduction to Git (4-49).mp44.02MB

1-The Data Scientist’s Toolbox/1 - 15 - Install R on a Mac (2-02) {Roger Peng}.mp43.98MB

2-R-programming/1 - 2 - Installing R on a Mac.mp43.98MB

4-Exploratory Data Analysis/2 - 1 - Introduction.mp43.98MB

4-Exploratory Data Analysis/4 - 2 - Hierarchical Clustering (part 2) [5-24].mp43.97MB

3-gettting and cleaning data/2 - 5 - Reading From Other Sources (4-44).mp43.93MB

1-The Data Scientist’s Toolbox/1 - 4 - Finding Answers (4-35).mp43.82MB

1-The Data Scientist’s Toolbox/3 - 3 - What About Big Data- (4-15).mp43.82MB

4-Exploratory Data Analysis/2 - 4 - Exploratory Graphs (part 2) [5-13].mp43.8MB

4-Exploratory Data Analysis/4 - 4 - K-Means Clustering (part 1) [5-46].mp43.74MB

7-Regression Models/2 - 10 - 02_01_a Multivariate Regression (2-47).mp43.68MB

7-Regression Models/3 - 6 - 02_03_c More simulation examples 2 (2-52).mp43.56MB

7-Regression Models/2 - 4 - 01_06_a Residuals (2-51).mp43.52MB

3-gettting and cleaning data/1 - 6 - Reading Excel Files (3-55).mp43.46MB

9-Data Product/2 - 2 - Motivating Shiny (1-49).mp43.4MB

9-Data Product/3 - 3 - Slidify working it out (2-01).mp43.37MB

9-Data Product/3 - 6 - Slidify reminder about knitting R (1-52).mp43.33MB

7-Regression Models/4 - 1 - 03_01_a Generalized Linear Models (2-32).mp43.26MB

2-R-programming/4 - 5 - mapply [4-46].mp43.21MB

1-The Data Scientist’s Toolbox/2 - 3 - Introduction to Github (3-53).mp43.18MB

9-Data Product/2 - 6 - Shiny 4 Working with images (2-39).mp43.17MB

2-R-programming/2 - 11 - Introduction to swirl.mp43.17MB

3-gettting and cleaning data/4 - 5 - Data Resources (3-33).mp43.03MB

4-Exploratory Data Analysis/4 - 9 - Working with Color in R Plots (part 1) [4-08].mp42.97MB

4-Exploratory Data Analysis/4 - 5 - K-Means Clustering (part 2) [4-26].mp42.94MB

4-Exploratory Data Analysis/4 - 12 - Working with Color in R Plots (part 4) [3-35].mp42.73MB

1-The Data Scientist’s Toolbox/1 - 13 - Installing Rstudio (1-36) {Roger Peng}.mp42.61MB

2-R-programming/1 - 3 - Installing R Studio (Mac).mp42.61MB

2-R-programming/3 - 9 - Vectorized Operations [3-46].mp42.49MB

1-The Data Scientist’s Toolbox/1 - 14 - Installing Outside Software on Mac (OS X Mavericks) [1-19].mp42.18MB

2-R-programming/1 - 6 - Installing Outside Software (Mac OS X Mavericks).mp42.18MB

1-The Data Scientist’s Toolbox/2 - 8 - Installing Rtools (2-29).mp42.18MB

2-R-programming/4 - 3 - tapply [3-17].mp42.17MB

9-Data Product/2 - 13 - Intro to rCharts and GoogleVis (1-01).mp41.94MB

1-The Data Scientist’s Toolbox/2 - 6 - Basic Markdown (2-22).mp41.76MB

7-Regression Models/2 - 7 - 01_07_a Inference in Regression (1-28).mp41.76MB

1-The Data Scientist’s Toolbox/1 - 5 - R Programming Overview (2-12).mp41.73MB

1-The Data Scientist’s Toolbox/1 - 10 - Regression Models Overview (1-46).mp41.5MB

9-Data Product/2 - 1 - Introduction to Data Products (1-05).mp41.37MB

7-Regression Models/2 - 13 - 02_01_d Multivariable Linear Models Interpretation (9-46).mp41.34MB

1-The Data Scientist’s Toolbox/1 - 11 - Practical Machine Learning Overview (1-31).mp41.27MB

1-The Data Scientist’s Toolbox/1 - 6 - Getting Data Overview (1-34).mp41.22MB

1-The Data Scientist’s Toolbox/1 - 12 - Building Data Products Overview (1-19).mp41.16MB

1-The Data Scientist’s Toolbox/1 - 7 - Exploratory Data Analysis Overview (1-21).mp41.07MB

5-Reproducible Research/3 - 11 - Introduction to Peer Assessment 2.mp41.06MB

1-The Data Scientist’s Toolbox/1 - 8 - Reproducible Research Overview (1-27).mp41.06MB

1-The Data Scientist’s Toolbox/1 - 9 - Statistical Inference Overview (1-06).mp4891.93KB

7-Regression Models/4 - 5 - 03_02_b GLMs and Odds (14-03).mp4356.36KB

7-Regression Models/3 - 10 - 02_04_c Residuals and diagnostics examples (6-32).mp4169.32KB

7-Regression Models/2 - 11 - 02_01_b Multivariable Least Squares (12-59).mp4118.33KB