STATS-R.AU1
An Introduction to Statistical Learning with Applications in R
Decoding vast and complex data has never been easier. Level up your data game with R programming.
- Practice in 52 Praktische Übungen — nothing to install
- 14 Interaktive Lektionen Und 88 topics mapped to the official exam objectives
Intermediate Selbstgesteuert · 1 Jahr Zugang
52 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
14Interaktive Lektionen
88Topics
52LiveLab
01 / Fähigkeiten, die Sie erwerben
What you will be able to do
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Keine Kreditkarte benötigt
Data and statistics are powering business innovations today. Become an indispensable part of this data-driven industry with our online course ‘Statistical Learning with R’.
Learn how to extract meaningful insights from the most complex and vast datasets. The syllabus covers everything from the fundamental concepts of statistical learning with R to building predictive models and decision-making.
Grain hands-on experience by decoding complicated data problems with our hands-on lab activities using R programming language.
- Understanding of fundamental statistical concepts like regression, classification, clustering, and dimensionality reduction.
- Use model assessment techniques like bias-variance trade-off, cross-validation
- Awareness of hypothesis testing and statistical significance
- Expertise in R programming for data manipulation, analysis, and visualization
- Expertise in data cleaning, preprocessing, and analysis
- Skilled in model building and evaluation by using various statistical methods
- Ability to interpret model results and make data-driven decisions
- Mastery in identifying relevant data and extracting meaningful insights
- Problem-solving mindset for evaluating data analysis results and their implications
- Skilled in presenting data visualizations and model results clearly
Course Highlights
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14 Strukturierte Lektionen Umfassende Abdeckung der zentralen Kursziele
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52 Praktische LiveLabs Interaktive, geführte Szenarien mit sofortiger Auswertung
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1 Jahr voller Zugang Selbstgesteuertes Lernen, jederzeit auf allen Geräten zugänglich
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
14 Interaktive Lektionen · 88 topics01 Preface +
02 Introduction 7 topics · 2 LiveLab +
- An Overview of Statistical Learning
- A Brief History of Statistical Learning
- This Course
- Who Should Read This Course?
- Notation and Simple Matrix Algebra
- Organization of This Course
- Data Sets Used in Labs and Exercises
2 LiveLab in this lesson — see the labs panel →
03 Statistical Learning 4 topics · 3 LiveLab +
- What Is Statistical Learning?
- Assessing Model Accuracy
- Lab: Introduction to R
- Exercises
3 LiveLab in this lesson — see the labs panel →
04 Linear Regression 7 topics · 4 LiveLab +
- Simple Linear Regression
- Multiple Linear Regression
- Other Considerations in the Regression Model
- The Marketing Plan
- Comparison of Linear Regression with K-Nearest Neighbors
- Lab: Linear Regression
- Exercises
4 LiveLab in this lesson — see the labs panel →
05 Classification 8 topics · 9 LiveLab +
- An Overview of Classification
- Why Not Linear Regression?
- Logistic Regression
- Generative Models for Classification
- A Comparison of Classification Methods
- Generalized Linear Models
- Lab: Classification Methods
- Exercises
9 LiveLab in this lesson — see the labs panel →
06 Resampling Methods 4 topics · 4 LiveLab +
- Cross-Validation
- The Bootstrap
- Lab: Cross-Validation and the Bootstrap
- Exercises
4 LiveLab in this lesson — see the labs panel →
07 Linear Model Selection and Regularization 6 topics · 6 LiveLab +
- Subset Selection
- Shrinkage Methods
- Dimension Reduction Methods
- Considerations in High Dimensions
- Lab: Linear Models and Regularization Methods
- Exercises
6 LiveLab in this lesson — see the labs panel →
08 Moving Beyond Linearity 9 topics · 4 LiveLab +
- Polynomial Regression
- Step Functions
- Basis Functions
- Regression Splines
- Smoothing Splines
- Local Regression
- Generalized Additive Models
- Lab: Non-linear Modeling
- Exercises
4 LiveLab in this lesson — see the labs panel →
09 Tree-Based Methods 4 topics · 4 LiveLab +
- The Basics of Decision Trees
- Bagging, Random Forests, Boosting, and Bayesian Additive Regression Trees
- Lab: Decision Trees
- Exercises
4 LiveLab in this lesson — see the labs panel →
10 Support Vector Machines 7 topics · 4 LiveLab +
- Maximal Margin Classifier
- Support Vector Classifiers
- Support Vector Machines
- SVMs with More than Two Classes
- Relationship to Logistic Regression
- Lab: Support Vector Machines
- Exercises
4 LiveLab in this lesson — see the labs panel →
11 Deep Learning 10 topics · 3 LiveLab +
- Single Layer Neural Networks
- Multilayer Neural Networks
- Convolutional Neural Networks
- Document Classification
- Recurrent Neural Networks
- When to Use Deep Learning
- Fitting a Neural Network
- Interpolation and Double Descent
- Lab: Deep Learning
- Exercises
3 LiveLab in this lesson — see the labs panel →
12 Survival Analysis and Censored Data 9 topics · 3 LiveLab +
- Survival and Censoring Times
- A Closer Look at Censoring
- The Kaplan-Meier Survival Curve
- The Log-Rank Test
- Regression Models With a Survival Response
- Shrinkage for the Cox Model
- Additional Topics
- Lab: Survival Analysis
- Exercises
3 LiveLab in this lesson — see the labs panel →
13 Unsupervised Learning 6 topics · 3 LiveLab +
- The Challenge of Unsupervised Learning
- Principal Components Analysis
- Missing Values and Matrix Completion
- Clustering Methods
- Lab: Unsupervised Learning
- Exercises
3 LiveLab in this lesson — see the labs panel →
14 Multiple Testing 7 topics · 4 LiveLab +
- A Quick Review of Hypothesis Testing
- The Challenge of Multiple Testing
- The Family-Wise Error Rate
- The False Discovery Rate
- A Re-Sampling Approach to p-Values and False Discovery Rates
- Lab: Multiple Testing
- Exercises
4 LiveLab in this lesson — see the labs panel →
Praktische Übungen Our edge
52 LiveLabs- Analyzing Stock Market Trends Using the Smarket Dataset from ISLR
- Analyzing Wage Data Using the ISLR Package
- Implementing the Bayes Classifier
- Implementing the Bias-Variance Trade-Off
- Indexing Data
- Implementing Simple Linear Regression
- Performing Multiple Linear Regression
- Implementing Qualitative Predictors Using the Credit Dataset from ISLR
- Implementing Non-linear Transformations of Predictors
- Implementing Multinomial Logistic Regression
- Implementing Multiple Logistic Regression
- Implementing Naive Bayes Classification
- Implementing Quadratic Discriminant Analysis
- Generating and Visualizing Multivariate Gaussian Distribution
- Implementing Linear Discriminant Analysis
- Implementing the Generalized Linear Model
- Implementing Poisson Regression
- Implementing K-Nearest Neighbors on the Caravan Dataset from ISLR
- Implementing the Validation Set Approach with the Auto Dataset from ISLR
- Implementing Leave-One-Out Cross-Validation
- Implementing K-Fold Cross-Validation
- Understanding Bootstrapping Techniques on the Portfolio Dataset from ISLR
- Implementing Subset Selection Methods Using the Hitters Dataset from ISLR
- Implementing Forward and Backward Stepwise Selection
- Implementing Lasso Regression
- Implementing Ridge Regression
- Implementing Partial Least Squares
- Improving Predictions with Principal Components Regression
- Implementing Polynomial Regression
- Implementing Step Functions
- Implementing Splines
- Improving Generalized Additive Models
- Implementing Bagging and Random Forests
- Fitting Regression Trees
- Improving Model Performance Using Boosting
- Building and Analyzing Classification Trees Using the Carseats Dataset from ISLR
- Implementing the Maximal Margin Classifier
- Introducing ROC Curves
- Implementing Support Vector Classifier
- Implementing SVM with Multiple Classes
- Implementing RNN for Time Series Prediction
- Creating an Image Classifier Using CNNs
- Implementing RNN for Time Series Prediction
- Implementing the Kaplan-Meier Survival Curve
- Applying the Log-Rank Test
- Incorporating Shrinkage Techniques into the Cox Model
- Implementing a Dendrogram
- Implementing K-Means Clustering
- Analyzing the NCI60 Data using the ISLR Package
- Implementing Family-Wise Error Rate
- Implementing Holm's Step-Down Procedure
- Implementing the Benjamini-Hochberg Procedure
- Implementing the False Discovery Rate
Labore laufen direkt im Browser — nichts zu installieren.
03 / FAQS
Fragen, bevor Sie beginnen
What is Statistical Learning?+
Statistical Learning is the study of using statistical methods for analyzing data to extract valuable insights and making data-driven decisions. The key aspects include predictive modeling, pattern recognition, decision making, and data mining.
Who should do this course?+
All those wanting to learn how to utilize data for driving business growth, should enroll for this course. It will be of great benefit to the following people:
- Data Scientists
- Machine Learning Engineers
- Statisticians
- Analysts
- Students and Researchers
Is prior knowledge of R programming needed to take this course?+
No, there’s no need for prior programming knowledge. You’ll be learning it with this course.
Does this course cover any advanced topics?+
Yes, it covers several advanced topics like the following:
- Understanding of survival analysis, time series analysis, and unsupervised learning
- Deep learning techniques and their applications
- Handling complex data structures and performing high-dimensional analysis
What are the practical applications of statistical learning?+
It can be effectively used for a wide range of applications including:
- Predictive analysis
- Risk assessment
- Portfolio optimization
- Fraud detection
- Customer & market segmentation
- Climate modeling & species distribution modeling
- Environment impact assessment
- Image and speech recognition
- Anomaly detection
- Time series analysis
Can statistical learning techniques be applied without advanced mathematics?+
No, advanced maths is not required for using statistical learning techniques.
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