CRC Press

Machine Learning Approaches for Educational Data Analysis Success

Name: Machine Learning Approaches for Educational Data Analysis Success
Brand: CRC Press
SKU: EXT 20260603-28510
Price: 4.99 USD
Availability: InStock

Original price was: $49.99.Current price is: $4.99.

Explore Educational Data Analysis Methods using machine learning to uncover insights, improve learning outcomes, and support decision-making.

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Additional information

Authors	(Anirban Mukherjee, Arpan Deyasi etc.)
Publisher	CRC Press
Published On	27-02-26
Language	English
File Format	PDF
File Size	24.34 MB
Rating	⭐️⭐️⭐️⭐️⭐️ 4.118

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SKU: EXT 20260603-28510 Categories: Artificial Intelligence & Machine Learning, Data Science & Analytics, E-Books & PDF Guides Tags: academic research, AI in Education, Data Science, Education Technology, Educational Analytics, Learning Analytics, Machine Learning, Pedagogical Data Analysis, Predictive Modeling, Student Performance Brand: CRC Press

Description

Machine Learning Approaches for Educational Data Analysis Success

Machine Learning Educational Data Analysis provides a comprehensive introduction to applying machine learning techniques in educational environments to improve student performance, optimize learning experiences, and support data-driven decision-making. This course combines data science principles with educational analytics, enabling learners to transform raw educational data into actionable insights. Moreover, participants gain practical experience using machine learning algorithms to analyze academic trends, predict outcomes, and enhance institutional effectiveness.

As educational institutions generate increasing amounts of data, professionals must develop the ability to extract meaningful information efficiently. Therefore, this course teaches the essential skills required to process, analyze, and interpret educational datasets. Furthermore, learners discover how predictive analytics can identify learning patterns and support student success initiatives.

Course Overview

This course introduces the fundamentals of educational data mining and machine learning applications within academic environments. First, learners explore the foundations of data collection, preprocessing, and feature engineering for educational datasets. Next, they examine supervised and unsupervised learning methods commonly used in learning analytics.

Additionally, the course demonstrates how machine learning models can predict student performance, identify at-risk learners, and improve educational outcomes. As a result, participants develop practical skills that can be applied across schools, universities, online learning platforms, and educational technology organizations. Throughout the training, real-world case studies reinforce theoretical concepts and practical implementation strategies.

What You Will Learn

Understand the fundamentals of machine learning in education
Prepare and clean educational datasets for analysis
Apply classification and regression algorithms
Build predictive models for student performance forecasting
Analyze learner behavior using educational data mining techniques
Implement clustering methods to identify learning patterns
Evaluate machine learning model performance and accuracy
Visualize educational data for effective decision-making
Develop data-driven strategies to improve learning outcomes
Understand ethical considerations in educational analytics

Who Should Enroll?

This course is ideal for data analysts, educators, academic researchers, instructional designers, education administrators, learning analytics specialists, and students interested in educational technology. In addition, professionals seeking to combine machine learning expertise with educational research will find substantial value in this program.

Key Benefits

Gain practical machine learning experience using educational datasets
Learn predictive analytics techniques for academic success
Improve educational decision-making through data insights
Understand modern learning analytics methodologies
Develop valuable skills in educational data science
Enhance career opportunities in EdTech and analytics

Course Modules

Introduction to Educational Data Analysis
Machine Learning Fundamentals
Data Collection and Preprocessing
Feature Engineering for Educational Data
Classification and Regression Techniques
Student Performance Prediction Models
Clustering and Pattern Discovery
Learning Analytics and Data Visualization
Model Evaluation and Optimization
Ethics and Privacy in Educational Analytics

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Career Opportunities

Upon completing this course, learners can pursue roles such as Educational Data Analyst, Learning Analytics Specialist, Data Scientist, Educational Research Analyst, EdTech Consultant, Machine Learning Analyst, and Academic Data Strategist. Consequently, they will possess the knowledge required to leverage machine learning for educational improvement and institutional success.

Ultimately, this course bridges the gap between machine learning technology and educational research. Therefore, it empowers professionals to use advanced analytics techniques to drive meaningful improvements in teaching, learning, and educational performance.

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Additional information

Authors	(Anirban Mukherjee, Arpan Deyasi etc.)
Publisher	CRC Press
Published On	27-02-26
Language	English
File Format	PDF
File Size	24.34 MB
Rating	⭐️⭐️⭐️⭐️⭐️ 4.118