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.
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
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
Training Provider Pricing
Pax:
Features
Subsidies
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