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Details for:
Linkedin - Complete Guide to R Wrangling Visualizing and Modeling Data
linkedin complete guide r wrangling visualizing modeling data
Type:
Other
Files:
71
Size:
1.3 GB
Uploaded On:
April 20, 2024, 8:31 a.m.
Added By:
abdenna
Seeders:
4
Leechers:
2
Info Hash:
0F83E8666CA3F7A58681BB5D331967F7D21B9014
Get This Torrent
Course details Trying to locate meaning and direction in big data is difficult. R can help you find your way. R is a statistical programming language to analyze and visualize the relationships between large amounts of data. This course with data analytics expert Barton Poulson provides a thorough introduction to R, with detailed instruction for installing and navigating R and RStudio and hands-on examples, from exploratory graphics to neural networks. Barton shows how to get R and popular R packages up and running and start importing, cleaning, and converting data for analysis. He also shows how to create visualizations such as bar charts, histograms, and scatterplots and transform categorical, qualitative, and outlier data to best meet your research questions and the requirements of your algorithms
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1. Introduction/1. Make your data make sense.mp4
4.3 MB
1. Introduction/2. Using the exercise files.mp4
1.7 MB
10. Analyzing Data/1. Comparing proportions.mp4
24.3 MB
10. Analyzing Data/2. Comparing one mean to a population- One-sample t-test.mp4
16.1 MB
10. Analyzing Data/3. Comparing paired means- Paired samples t-test.mp4
23.2 MB
10. Analyzing Data/4. Comparing two means- Independent samples t-test.mp4
21.0 MB
10. Analyzing Data/5. Comparing multiple means- One-factor analysis of variance.mp4
29.9 MB
10. Analyzing Data/6. Comparing means with multiple categorical predictors- Factorial analysis of variance.mp4
21.8 MB
11. Predicting Outcomes/1. Predicting outcomes with linear regression.mp4
27.7 MB
11. Predicting Outcomes/2. Predicting outcomes with lasso regression.mp4
24.7 MB
11. Predicting Outcomes/3. Predicting outcomes with quantile regression.mp4
18.3 MB
11. Predicting Outcomes/4. Predicting outcomes with logistic regression.mp4
31.3 MB
11. Predicting Outcomes/5. Predicting outcomes with Poisson or log-linear regression.mp4
9.3 MB
11. Predicting Outcomes/6. Assessing predictions with blocked-entry models.mp4
30.6 MB
12. Clustering and Classifying Cases/1. Grouping cases with hierarchical clustering.mp4
29.2 MB
12. Clustering and Classifying Cases/2. Grouping cases with k-means clustering.mp4
22.9 MB
12. Clustering and Classifying Cases/3. Classifying cases with k-nearest neighbors.mp4
30.6 MB
12. Clustering and Classifying Cases/4. Classifying cases with decision tree analysis.mp4
24.4 MB
12. Clustering and Classifying Cases/5. Creating ensemble models with random forest classification.mp4
24.0 MB
13. Conclusion/1. Next steps.mp4
5.7 MB
2. What Is R/1. R in context.mp4
13.5 MB
2. What Is R/2. Data science with R- A case study.mp4
30.6 MB
3. Getting Started/1. Installing R.mp4
5.1 MB
3. Getting Started/10. Piping commands with %%.mp4
7.9 MB
3. Getting Started/2. Environments for R.mp4
11.2 MB
3. Getting Started/3. Installing RStudio.mp4
3.3 MB
3. Getting Started/4. Navigating the RStudio environment.mp4
17.4 MB
3. Getting Started/5. Entering data.mp4
17.0 MB
3. Getting Started/6. Data types and structures.mp4
28.7 MB
3. Getting Started/7. Comments and headers.mp4
13.1 MB
3. Getting Started/8. Packages for R.mp4
17.0 MB
3. Getting Started/9. The tidyverse.mp4
9.3 MB
4. Importing Data/1. Rs built-in datasets.mp4
16.3 MB
4. Importing Data/2. Exploring sample datasets with pacman.mp4
21.3 MB
4. Importing Data/3. Importing data from a spreadsheet.mp4
18.0 MB
4. Importing Data/4. Importing XML data.mp4
19.3 MB
4. Importing Data/5. Importing JSON data.mp4
21.3 MB
4. Importing Data/6. Saving data in native R formats.mp4
19.8 MB
5. Visualizing Data with ggplot2/1. Introduction to ggplot2.mp4
14.8 MB
5. Visualizing Data with ggplot2/2. Using colors in R.mp4
16.0 MB
5. Visualizing Data with ggplot2/3. Using color palettes.mp4
26.5 MB
5. Visualizing Data with ggplot2/4. Creating bar charts.mp4
24.2 MB
5. Visualizing Data with ggplot2/5. Creating histograms.mp4
13.1 MB
5. Visualizing Data with ggplot2/6. Creating box plots.mp4
12.4 MB
5. Visualizing Data with ggplot2/7. Creating scatterplots.mp4
14.1 MB
5. Visualizing Data with ggplot2/8. Creating multiple graphs.mp4
10.0 MB
5. Visualizing Data with ggplot2/9. Creating cluster charts.mp4
25.2 MB
6. Wrangling Data/1. Creating tidy data.mp4
34.3 MB
6. Wrangling Data/10. Filtering cases and subgroups.mp4
22.0 MB
6. Wrangling Data/2. Using tibbles.mp4
13.3 MB
6. Wrangling Data/3. Using data.table.mp4
15.8 MB
6. Wrangling Data/4. Converting data from wide to tall and from tall to wide.mp4
11.4 MB
6. Wrangling Data/5. Converting data from tables to rows.mp4
14.9 MB
6. Wrangling Data/6. Working with dates and times.mp4
18.8 MB
6. Wrangling Data/7. Working with list data.mp4
14.9 MB
6. Wrangling Data/8. Working with XML data.mp4
17.2 MB
6. Wrangling Data/9. Working with categorical variables.mp4
15.5 MB
7. Recoding Data/1. Recoding categorical data.mp4
23.9 MB
7. Recoding Data/2. Recoding quantitative data.mp4
22.1 MB
7. Recoding Data/3. Transforming outliers.mp4
21.5 MB
7. Recoding Data/4. Creating scale scores by counting.mp4
14.4 MB
7. Recoding Data/5. Creating scale scores by averaging.mp4
8.5 MB
8. An R for Data Science Case Study/1. Data science with R- A case study.mp4
57.4 MB
9. Exploring Data/1. Computing frequencies.mp4
14.4 MB
9. Exploring Data/2. Computing descriptive statistics.mp4
25.3 MB
9. Exploring Data/3. Computing correlations.mp4
16.6 MB
9. Exploring Data/4. Creating contingency tables.mp4
16.8 MB
9. Exploring Data/5. Conducting a principal component analysis.mp4
37.8 MB
9. Exploring Data/6. Conducting an item analysis.mp4
52.1 MB
9. Exploring Data/7. Conducting a confirmatory factor analysis.mp4
18.1 MB
Ex_Files_Complete_Guide_to_R.zip
6.2 MB
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