Data Science Fundamentals in R Training
Data Science Fundamentals in R Training
Order this unique E-Learning Data Science Fundamentals Training in R online, 1 year 24/7 access to rich interactive videos, progress through reporting and testing.
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Data Science Fundamentals Using R E-Learning
Order this unique E-Learning Data Science Fundamentals in R course online!
✔️ 1 year 24/7 access to rich interactive videos, voice commands and progress monitoring via chapter-by-chapter reports and tests.
✔️ Learn the fundamentals of data science and get hands-on experience with R, the programming language of choice for data scientists.
Why choose this course?
R is one of the most widely used and powerful tools for data science and statistical analysis worldwide. This course will provide you with the knowledge and skills needed to work with R for data analysis, solving common data science problems and managing data efficiently.
What you will learn:
- Data preparation and manipulation: Discover how to clean, transform and prepare data for analysis.
- Debugging and debugging: Learn how to fix common programming errors in R so that your code is effective and robust.
- Defensive programming: Understand how to write your R code defensively to avoid errors and improve readability.
- Domain-specific language integration: Learn how to integrate R with other programming languages and tools for more advanced data analysis.
- Data analysis and visualisation: Discover how to effectively analyse and visualise data using R packages such as ggplot2 and dplyr.
This course is ideal for anyone who wants to start their career in data science and is looking for a solid foundation in using R for data analysis.
Who should participate?
This training is perfect for:
- Novice data scientists who want to learn how to use R for data analysis.
- Data analysts who want to extend their skills to data science and statistical analysis.
- Statisticians and researchers who want to use R for data processing, analysis and visualisation.
- Programmers and software developers who are interested in data science and want to learn how to work with R.
Course content
Introduction
Course: 11 Minutes
- Course Introduction
- What is Data Science?
- Examples of Data Science
- Sources of Data for Learning
Important R Basics
Course: 28 Minutes
- Data Frames
- The Structure Function str
- Summary Statistics
- Import JSON Data
- Foreach Looping
Data Management
Course: 32 Minutes
- Reshaping Data
- Merging Data
- Transposing Data
- Aggregating Data
- Basic Imputation
- Linear Fitted Imputation
- Categorize Continuous Variables
Data Analysis
Course: 45 Minutes
- Modeling for Data Science
- Linear Modeling
- Analysis of Variance
- The R coef Function
- The R fitted Function
- The R residuals Function
- The Variance-Covariance Matrix
- Confidence Intervals
- Fitting Generalized Linear Models
- Plotting Linear Models
- T-Test
- The TukeyHSD Test
- The predict Function
Time Series
Course: 8 Minutes
- Time Series
The R forecast package
Practice: Data Science Fundamentals in R
Course: 15 Minutes
- Exercise: Using R for Data Science
Introduction
Course: 5 Minutes
- Course Introduction
- Supervised and Unsupervised Learning
Clustering
Course: 34 Minutes
- Multidimensional Scaling
- Hierarchical Clustering
- Hierarchical Clustering with corclust
- K-Means Clustering
- Selecting K for kmeans Clustering
- Clustering Large Applications (Clara)
- Fuzzy C-Means Clustering
Classification and Regression
Course: 1 Hour, 11 Minutes
- Classification Trees with rpart in R
- Regression Tree with rpart
- Classification Trees with the tree Package
- Regression Trees with the tree Package
- K-Nearest Neighbor Classification
- The randomForest Package
- Combining Random Forests
- Random Forests for Proximity Classification
- Partitioning Around Medoids (PAM)
- Naive Bayes Classifier
- Linear Discriminant Analysis (LDA)
- Quadratic Discriminant Analysis (QDA)
- Mixture Discriminant Analysis (MDA)
- Support Vector Machines (SVMs)
- Loess Regression
- Partial Least Squares Regression (PLS)
- Smoothing Splines
Boosting and Bagging
Course: 11 Minutes
- AdaBoost in R
- Bagging
Advanced Visualizations
Course: 15 Minutes
- Scatterplot Matrix in R
- Overlay Density Plots
- Scatterplot 3D Visualization in R
Practice: Machine Learning Examples in R
Course: 15 Minutes
- Exercise: Statistical analysis in R
Get started with Data Science in R!
✔️ Learn at your own pace with interactive videos and hands-on exercises.
✔️ Test your knowledge after each chapter with tests to track your progress.
✔️ Strengthen your data science skills and take the first step in your career as a data scientist.
Order your course now and start learning data science in R today!
| Language | English |
|---|---|
| Qualifications of the Instructor | Certified |
| Course Format and Length | Teaching videos with subtitles, interactive elements and assignments and tests |
| Lesson duration | 5:04 Hours |
| Progress monitoring | Yes |
| Access to Material | 365 days |
| Technical Requirements | Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge. |
| Support or Assistance | Helpdesk and online knowledge base 24/7 |
| Certification | Certificate of participation in PDF format |
| Price and costs | Course price at no extra cost |
| Cancellation policy and money-back guarantee | We assess this on a case-by-case basis |
| Award Winning E-learning | Yes |
| Tip! | Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform. |
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