Data Analytics in Finance
Master the art of Data Analytics in Finance with our comprehensive course. Dive deep into supervised and unsupervised learning, neural networks, machine learning classification techniques, big data applications in fintech industries such as fraud detection and credit ratings. Transform your career by unlocking powerful insights from financial data.
- Available in:
- Malaysia
- Upcoming intakes:
- Jul 12, 2025
- Jul 14, 2025
- Jul 14, 2025
- Jul 16, 2025
- Jul 16, 2025
- Jul 17, 2025
- Jul 18, 2025
- Jul 20, 2025
- Jul 21, 2025
- Jul 22, 2025
- Jul 23, 2025
- Jul 24, 2025
- Jul 24, 2025
- Jul 26, 2025
- Jul 28, 2025
- Jul 30, 2025
- Aug 1, 2025
- Aug 3, 2025
- Aug 4, 2025
- Aug 5, 2025
- Aug 6, 2025
- Aug 7, 2025
- Aug 7, 2025
- Aug 9, 2025
- Aug 11, 2025

Corporate Pricing
Pax:
Training Provider Pricing
Pax:
Features
Subsidies

What you'll learn
- Develop expertise in neural networks and extreme gradient boosting.
- Acquire skills in predictive analytics and sentiment analysis within fintech sectors.
- Understand and apply various supervised learning regression techniques.
- Learn logistic regression and support vector machines for classification problems.
- Explore unsupervised learning techniques including clustering and factor analysis.
- Master non-parametric regression methods for financial data analysis.
- Gain practical knowledge of deep learning applications in finance.
- Implement reinforcement learning strategies for real-world financial scenarios.
Why should you attend?
Data Analytics in Finance is a transformative course designed to equip learners with cutting-edge techniques and tools essential for navigating the complex world of financial data. The course delves into Supervised Learning Regressions, exploring both parametric methods like Lasso, Ridge, and Elastic Net, as well as non-parametric approaches including Loess and K-Nearest Neighbor. Learners will gain proficiency in advanced algorithms such as Neural Networks and Extreme Gradient Boosting. The curriculum also encompasses Supervised Learning Classification, covering Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, and Hidden Markov Models. Unsupervised learning sessions introduce Clustering and Factor Analysis, while Deep and Reinforcement Learning modules focus on Multi-Layer Perceptrons, Long Short-Term Memory networks, Convolutional Neural Networks, and Reinforcement Learning strategies. Lastly, the course addresses the practical application of Big Data in Fintech companies with an emphasis on Predictive Analytics, Sentiment Analysis, Financial Fraud detection, Credit Ratings assessments, Estimation Techniques, Robustness and Optimization Techniques for Modern Data Analysis. It concludes with insights into Sentiment Analysis and High Frequency Trading applications.
Course Syllabus
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Minimum Qualification
Target Audience
Methodologies
Why should you attend?
Data Analytics in Finance is a transformative course designed to equip learners with cutting-edge techniques and tools essential for navigating the complex world of financial data. The course delves into Supervised Learning Regressions, exploring both parametric methods like Lasso, Ridge, and Elastic Net, as well as non-parametric approaches including Loess and K-Nearest Neighbor. Learners will gain proficiency in advanced algorithms such as Neural Networks and Extreme Gradient Boosting. The curriculum also encompasses Supervised Learning Classification, covering Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, and Hidden Markov Models. Unsupervised learning sessions introduce Clustering and Factor Analysis, while Deep and Reinforcement Learning modules focus on Multi-Layer Perceptrons, Long Short-Term Memory networks, Convolutional Neural Networks, and Reinforcement Learning strategies. Lastly, the course addresses the practical application of Big Data in Fintech companies with an emphasis on Predictive Analytics, Sentiment Analysis, Financial Fraud detection, Credit Ratings assessments, Estimation Techniques, Robustness and Optimization Techniques for Modern Data Analysis. It concludes with insights into Sentiment Analysis and High Frequency Trading applications.
What you'll learn
- Develop expertise in neural networks and extreme gradient boosting.
- Acquire skills in predictive analytics and sentiment analysis within fintech sectors.
- Understand and apply various supervised learning regression techniques.
- Learn logistic regression and support vector machines for classification problems.
- Explore unsupervised learning techniques including clustering and factor analysis.
- Master non-parametric regression methods for financial data analysis.
- Gain practical knowledge of deep learning applications in finance.
- Implement reinforcement learning strategies for real-world financial scenarios.
Course Syllabus
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Corporate Pricing
Pax:
Training Provider Pricing
Pax:
Features
Subsidies

Minimum Qualification
Target Audience
Methodologies
Our Offers

Become a Trainer
Teach what you love. Abundent Academy gives you the tools you need to run your own trainings! We provide you with the platform, the students, the materials, and the support you need to succeed!
- Higher trainer payouts
- Ready-made course materials
- Student management system
- AI digital marketing assistant

Academy for Business
Get unlimited access to all of Abundent Academy's carefully curated courses for your team, all organized according to job category and role! Perfect for companies looking to upskill their workforce and stay ahead in the tech industry.
- Carefully curated courses
- Role-based learning paths
- Team progress tracking
- Gap Identification and Analysis