R is a programming language for statistical computing. This skill will teach you the main components for coding in the R language. Developed with the contribution of the OEduverse Erasmus Plus Project. www.oeduverse.eu
After completing this learning path, learners will be able to understand and apply the main components of coding in the R language for statistical computing.
This learning path provides a comprehensive introduction to machine learning concepts and their practical application using the R programming language. It covers fundamental topics such as supervised and unsupervised learning, various regression and classification techniques (linear, logistic, decision trees, SVM, Naive Bayes), clustering methods (K-means, hierarchical), ensemble learning (boosting, bagging, random forest), and dimensionality reduction (PCA, LDA). Each concept is reinforced with examples and implementations in R, making it ideal for learners who want to gain hands-on experience in machine learning with R.
Understand the fundamental concepts of supervised and unsupervised machine learning.
Apply various regression and classification techniques, including linear regression, logistic regression, decision trees, Support Vector Machines (SVM), and Naive Bayes, using the R programming language.
Implement and interpret clustering algorithms such as K-means and hierarchical clustering in R.
Utilize ensemble learning methods, including boosting, bagging, and random forests, to enhance model accuracy and robustness.
Perform dimensionality reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) with R.
Gain practical, hands-on experience in building and evaluating machine learning models using R.
This learning path provides a comprehensive introduction to data visualization using R, focusing on the powerful package. You will learn the grammar of graphics, create various plot types such as bar plots, histograms, scatter plots, and box plots, and master techniques for combining multiple plots using facet wrap.
Upon completion of this learning path, learners will be able to:
Understand the fundamentals of data visualization using R and the package.
Create and customize various plot types such as bar plots, histograms, scatter plots, and box plots.
Apply the grammar of graphics to produce meaningful and aesthetically pleasing visualizations.
Combine multiple plots using faceting to effectively display complex datasets.
This learning path provides a comprehensive introduction to text mining concepts and their practical application using R. It covers essential techniques such as text preprocessing (lower case conversion, punctuation and stopword removal, tokenization), stemming, lemmatization, feature extraction (Bag of Words, TF-IDF), and advanced models like Word2Vec, Doc2Vec, Sentiment Analysis, Latent Semantic Analysis, and Latent Dirichlet Allocation, all with a focus on implementation in R.
Understand fundamental text mining concepts.
Apply text preprocessing techniques in R (lower case conversion, punctuation and stopword removal, tokenization).
Implement stemming and lemmatization in R.
Utilize feature extraction methods like Bag of Words and TF-IDF in R.
Work with advanced text mining models such as Word2Vec and Doc2Vec in R.
Perform sentiment analysis using R.
Apply Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) in R for topic modeling.
This learning path provides a comprehensive introduction to statistics, focusing on both theoretical concepts and practical application using R. It covers central tendency measures, variance, standard deviation, various probability distributions (Bernoulli, Binomial, Poisson, Normal), hypothesis testing, regression, and correlation. Developed with the contribution of the OEduverse Erasmus Plus Project. www.oeduverse.eu
Understand and Apply Measures of Central Tendency: Learners will be able to explain the concepts of mean, median, and mode and apply them to describe data sets.
Calculate and Interpret Measures of Dispersion: Learners will be able to calculate variance and standard deviation and interpret their significance for data analysis.
Identify and Apply Various Probability Distributions: Learners will be able to describe the characteristics of Bernoulli, Binomial, Poisson, and Normal distributions and apply them to appropriate scenarios.
Conduct Hypothesis Tests: Learners will be able to formulate, conduct, and interpret the results of basic hypothesis tests.
Fundamentals of Regression and Correlation: Learners will be able to explain linear regression and correlation and demonstrate their application in analyzing relationships between variables.
Practical Application of Statistical Methods with R: Learners will be able to implement and interpret the learned statistical concepts and methods using the R programming language.
This learning path provides a comprehensive introduction to Business Analytics using R. It covers fundamental machine learning concepts such as supervised vs. unsupervised learning, regression, and classification. You will delve into various algorithms including Linear Regression, Logistic Regression, Decision Trees, Ensemble Learning (Boosting and Bagging), Random Forests, K-Means, Density-Based Clustering, and Hierarchical Clustering. The path also includes essential statistical concepts like Cross-Validation, Bootstrapping, Hypothesis Testing, p-value, and Confidence Intervals, all with practical applications in R.
Apply fundamental machine learning concepts: Differentiate between supervised and unsupervised learning, and distinguish between regression and classification problems. Implement various machine learning algorithms in R: Utilize R to apply Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Means, Density-Based Clustering, and Hierarchical Clustering to real-world datasets. Understand and apply ensemble learning techniques: Explain and implement Boosting and Bagging methods for improved model performance. Master essential statistical concepts for model evaluation: Apply Cross-Validation and Bootstrapping techniques, and interpret Hypothesis Testing, p-values, and Confidence Intervals in the context of business analytics. Analyze and interpret data using R for business insights: Develop the ability to use R for data analysis, model building, and deriving actionable insights relevant to business scenarios.