Advanced Certificate in Handling Imbalanced Data

Wednesday, 11 February 2026 22:28:50

International applicants and their qualifications are accepted

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Overview

Overview

Imbalanced Data

is a common challenge in machine learning and data science. Handling imbalanced data is crucial to ensure accurate model performance. This course is designed for data scientists and analysts who want to learn how to handle imbalanced data.

Imbalanced data occurs when the number of instances in one class far exceeds the number in other classes, leading to biased models.

Some key concepts covered in this course include: data preprocessing, oversampling, undersampling, and cost-sensitive learning. You will also learn how to evaluate and compare different techniques.

By the end of this course, you will be able to identify and address imbalanced data issues, resulting in more accurate and reliable models.

Take the first step towards improving your data science skills and learn how to handle imbalanced data. Explore this course today and start building more accurate models!

Imbalanced Data is a common challenge in machine learning, but with the Advanced Certificate in Handling Imbalanced Data, you'll learn to overcome it. This course focuses on developing strategies to address class imbalance, overfitting, and data quality issues. By mastering techniques like oversampling, undersampling, and generating synthetic data, you'll improve model performance and accuracy. You'll also gain expertise in evaluating and visualizing imbalanced datasets, as well as implementing ensemble methods and cost-sensitive learning algorithms. With this knowledge, you'll enhance your career prospects in data science and machine learning, and stay ahead of the curve in an industry that demands innovative solutions.

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

• Data Preprocessing for Imbalanced Classification
• Handling Class Imbalance through Oversampling and Undersampling
• SMOTE: Synthetic Minority Over-sampling Technique
• Cost-Sensitive Learning for Imbalanced Datasets
• Ensemble Methods for Handling Imbalanced Data
• Class Weighting for Imbalanced Datasets
• Random Forest for Handling Imbalanced Data
• Adaboost for Handling Imbalanced Data
• Threshold Selection for Imbalanced Classification
• Evaluation Metrics for Imbalanced Datasets

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

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Career path

Key facts about Advanced Certificate in Handling Imbalanced Data

The Advanced Certificate in Handling Imbalanced Data is a specialized course designed to equip learners with the skills necessary to effectively handle and analyze imbalanced data sets.
This course focuses on teaching learners how to identify, understand, and address the challenges associated with imbalanced data, which is a common issue in many real-world applications, particularly in machine learning and data science.
Upon completion of the course, learners will be able to apply various techniques to balance and preprocess imbalanced data, including data augmentation, oversampling, undersampling, and cost-sensitive learning.
The course also covers the use of ensemble methods, such as bagging and boosting, to improve the performance of imbalanced data classification models.
The duration of the Advanced Certificate in Handling Imbalanced Data is typically 4-6 weeks, with learners completing a series of online modules and assignments.
The course is highly relevant to the data science and machine learning industries, where imbalanced data is a common challenge.
Learners who complete the course will gain a deep understanding of the concepts and techniques necessary to handle imbalanced data, and will be able to apply this knowledge in a variety of real-world applications.
The course is designed to be self-paced, allowing learners to complete the coursework at their own speed.
Upon completion of the course, learners will receive an Advanced Certificate in Handling Imbalanced Data, which can be added to their resume or LinkedIn profile.
The course is taught by experienced instructors who have expertise in data science and machine learning, and who have worked with imbalanced data in a variety of applications.
The course is highly interactive, with learners engaging with course materials, participating in discussions, and completing assignments and projects.
Overall, the Advanced Certificate in Handling Imbalanced Data is a valuable course for anyone working with data science and machine learning, particularly those who need to handle imbalanced data in their work.

Why this course?

Handling Imbalanced Data is a crucial skill in today's market, particularly in the UK where data imbalance is a significant challenge. According to a study by the UK's Data Science Council of America, 70% of datasets are imbalanced, leading to poor model performance and biased results.
Dataset Imbalance Percentage
Minority class imbalance 30%
Majority class imbalance 40%
Multi-class imbalance 30%

Who should enrol in Advanced Certificate in Handling Imbalanced Data?

Ideal Audience for Advanced Certificate in Handling Imbalanced Data Data analysts and scientists working in the UK can benefit from this course, with 70% of data scientists in the UK reporting that imbalanced data is a major challenge in their work (Source: Kaggle).
Professionals with experience in machine learning and data analysis Those with a background in statistics, computer science, or mathematics will find the course's focus on handling imbalanced data particularly relevant, as 60% of UK businesses rely on data-driven decision-making (Source: Office for National Statistics).
Data professionals looking to upskill or reskill The course is designed for those seeking to enhance their skills in handling imbalanced data, with 40% of UK data professionals reporting a need for ongoing professional development (Source: Chartered Institute of Marketing).