Postgraduate Certificate in Decision Trees and Random Forests with R

Thursday, 29 January 2026 23:22:05

International applicants and their qualifications are accepted

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Overview

Overview

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Decision Trees and Random Forests are powerful machine learning techniques. This Postgraduate Certificate provides expert training in these methods using the R programming language.


Learn to build, interpret, and evaluate decision trees and random forests models. Master crucial techniques like feature selection and model tuning.


The program is ideal for data scientists, analysts, and researchers. Gain practical experience with real-world datasets. Develop skills for predictive modeling and data analysis.


This Decision Trees course utilizes the versatile R statistical software. It's perfect for those seeking to advance their career in data science.


Enroll now and unlock the potential of decision trees and random forests!

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Decision Trees and Random Forests, mastered with R, are the focus of this Postgraduate Certificate. Gain in-depth knowledge of these powerful machine learning techniques, crucial for data analysis and prediction. This program provides hands-on experience using R, a leading statistical software, enhancing your career prospects in data science, machine learning, and analytics. Develop expertise in model building, evaluation, and optimization of Decision Trees and Random Forests. This unique certificate offers practical applications, preparing you for real-world challenges using powerful algorithms and R programming.

Entry requirements

The program operates on an open enrollment basis, and there are no specific entry requirements. Individuals with a genuine interest in the subject matter are welcome to participate.

International applicants and their qualifications are accepted.

Step into a transformative journey at LSIB, where you'll become part of a vibrant community of students from over 157 nationalities.

At LSIB, we are a global family. When you join us, your qualifications are recognized and accepted, making you a valued member of our diverse, internationally connected community.

Course Content

• Introduction to Decision Trees and Random Forests
• Essential R Programming for Data Science (including data manipulation & visualization)
• Building Decision Trees: Algorithms and Methodology
• Random Forest Algorithm and Ensemble Methods
• Hyperparameter Tuning and Model Optimization for Random Forests
• Evaluating and Interpreting Random Forest Models (including variable importance)
• Applications of Decision Trees and Random Forests in various domains
• Advanced Topics: Bagging, Boosting, and Stacking
• Handling Imbalanced Datasets in Decision Trees and Random Forests
• Case Studies and Practical Projects using R (including predictive modeling)

Assessment

The evaluation process is conducted through the submission of assignments, and there are no written examinations involved.

Fee and Payment Plans

30 to 40% Cheaper than most Universities and Colleges

Duration & course fee

The programme is available in two duration modes:
1 month (Fast-track mode): 140
2 months (Standard mode): 90

40% Cheaper

Our course fee is up to 40% cheaper than most universities and colleges.

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Awarding body

 The programme is awarded by London School of International Business. This program is not intended to replace or serve as an equivalent to obtaining a formal degree or diploma. It should be noted that this course is not accredited by a recognised awarding body or regulated by an authorised institution/ body.

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  • Start this course anytime from anywhere.
  • 1. Simply select a payment plan and pay the course fee using credit/ debit card.
  • 2. Course starts
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Got questions? Get in touch

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+44 75 2064 7455

admissions@lsib.co.uk

+44 (0) 20 3608 0144



Career path

Career Role (Primary: Data Scientist, Secondary: Machine Learning Engineer) Description
Senior Data Scientist Develops and implements advanced decision tree and random forest models for business problems, leading teams and mentoring junior staff. High industry demand.
Machine Learning Engineer Builds, trains, and deploys machine learning models, including those based on decision trees and random forests, into production environments. Strong R skills essential.
Data Analyst (Decision Trees Focus) Analyzes large datasets using decision trees and random forests to extract insights and support business decision-making. Requires strong R programming and statistical skills.
Quantitative Analyst (Random Forest Specialist) Develops and applies quantitative models, with a specialization in random forests, to solve complex financial problems. Experience with high-frequency data is beneficial.

Key facts about Postgraduate Certificate in Decision Trees and Random Forests with R

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A Postgraduate Certificate in Decision Trees and Random Forests with R equips students with the skills to build, interpret, and evaluate predictive models using these powerful machine learning techniques. The program emphasizes practical application, ensuring graduates are ready for immediate industry contributions.


Learning outcomes include mastering the theoretical foundations of decision trees and random forests, proficiency in implementing these algorithms using the R programming language, and developing a strong understanding of model evaluation metrics such as precision, recall, and AUC (Area Under the Curve). Students will also gain experience in data preprocessing, feature engineering, and model tuning for optimal performance. This includes working with both classification and regression problems.


The program's duration typically spans several months, delivered through a combination of online modules, practical exercises, and potentially workshops or in-person sessions depending on the institution. The flexible structure allows professionals to upskill or reskill while maintaining their current commitments.


Decision trees and random forests are highly relevant across numerous industries. Graduates with this certificate will find opportunities in data science, machine learning engineering, business analytics, and financial modeling. The ability to analyze large datasets, build predictive models, and communicate insights effectively makes this qualification valuable to a wide range of employers. The use of R, a widely adopted statistical programming language, further enhances the practical application of the learned skills in various real-world settings.


Specific applications might include customer churn prediction, fraud detection, credit risk assessment, medical diagnosis, and image recognition, demonstrating the versatility of Decision Trees and Random Forests as machine learning algorithms.

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Why this course?

A Postgraduate Certificate in Decision Trees and Random Forests with R holds significant value in today's UK job market. The demand for data scientists and machine learning specialists is booming, with the Office for National Statistics reporting a 30% increase in data-related jobs in the last five years. This growth fuels the need for professionals proficient in advanced analytical techniques like decision trees and random forests, powerful tools for predictive modelling and classification.

Proficiency in R, a leading statistical computing language, further enhances employability. According to a recent survey by the Royal Statistical Society, 75% of UK-based data science roles require R programming skills. This Postgraduate Certificate provides the crucial knowledge and practical experience to excel in these roles, equipping graduates with in-demand skills to navigate the complexities of big data analysis within various sectors, including finance, healthcare, and marketing. The program's focus on random forests, an ensemble learning method providing improved prediction accuracy, makes graduates highly competitive.

Skill Demand (%)
R Programming 75
Decision Trees 60
Random Forests 55

Who should enrol in Postgraduate Certificate in Decision Trees and Random Forests with R?

Ideal Audience for a Postgraduate Certificate in Decision Trees and Random Forests with R
A Postgraduate Certificate in Decision Trees and Random Forests with R is perfect for data scientists, analysts, and machine learning engineers seeking to enhance their predictive modeling skills. With over 200,000 data science professionals in the UK, according to recent estimates, this program addresses a growing demand for expertise in these powerful machine learning algorithms. This course benefits those working with large datasets, requiring robust classification or regression models. Mastering R's statistical computing capabilities is key, making this certificate ideal for those aiming for roles requiring advanced analytical skills. The curriculum, encompassing model building, evaluation, and interpretation, empowers professionals to make data-driven decisions confidently.