Skip to main content
  • Achieve Your Finance Career Goals with Expert-Guided Data Science Training

    From Data Science to Portfolio Management, Master Every Step with Digital Hub Insights

Practical Applications of AI and Machine Learning in Financial Services

Digital Hub Insights (DHI) provides essential AI and machine learning training tailored for finance and data science professionals, addressing the critical gap between theoretical knowledge and practical application.
Gain hands-on expertise, guided by industry veterans with over 60 years of experience, and drive your ability to navigate and succeed in today's evolving financial landscape.

The Digital Hub Approach

In response to financial professional's requirements for AI and data science skills, the Digital HUB Insights has created a unique approach for well rounded courses paired with the certification specifically for the financial professionals. Tested with major financial institutions, the Digital HUB Insights skills development program consists of three components: hands-on curriculum, community of users, and continuing education.

Curriculum

The Digital HUB Insights curriculum is structured to deliver skills and practical training for financial professionals consisting of theory, codes, models, performance evaluation, methodology and projects. Upon successful completion, a Digital Badge is awarded. Curriculum modules follow closely the real-life workflows of financial professionals in their job functions.

Community

The Digital HUB Insights skills development program includes a Community platform to support users and partner firms in collaborating and in providing tools and value add services.  The community network plays a key role in keeping the professionals engaged throughout their careers and foster Continuing Education and re-skilling beyond the initial training.

Newsletters and Publications

The Digital HUB Insights provides original, curated publications and newsletters to support continuing education for the financial professionals.  We integrate the state of the art technologies and it impacts and use cases in finance.  

CONTENT STRUCTURE AND DELIVERY

STRUCTURE
DELIVERY

CURRICULUM

Introduction to the Course
• What we do
• Why us
• Instructors
• How we do it
• Curriculum
• Technologies and use cases
• Learning objectives
• Who should take it
Asset Class Risk Return Analysis Use Case 1: Clustering
• Importing libraries
• Retrieve fund pricing
• Data transformation
• Feature engineering
• Feature matrix
• Merging
• Hierarchical Clustering
• Dendrogram
• Quiz
• Portfolio implications
• Methodology
• Strategic takeaways
Asset Class Risk Return Analysis Use Case 2: Shapley Values
• Fund to cluster assignments
• Feature matrix
• Light Gradient Boosting Method (LGBM)
• Shapley / SHAP values
• Feature importance
• Visualization / Plots
• Quiz
• Portfolio implications
• Methodology
• Strategic takeaways
Investment Selection Due Diligence Use Case 3: Factor Analysis with LASSO Regression
• Factor modeling
• LSO regression
• LASSO regression
• Retrieve factors pricing
• Data Transformation
• Feature engineering
• Variance (VIF) VS benchmark
• AIC and BIC metrics
• Visualization / Plots
• Quiz
• Portfolio implications
• Methodology
• Strategic takeaways
Tactical Asset Allocation with Deep Learning Use Case 4: Predicting SPY Direction
• Feature selection
• Feature engineering
• Target selection
• Deep Learning
• Setup Model
• Run Model
• Visualization / Plots
• Quiz
• Portfolio implications
• Methodology
• Strategic takeaway
Primer Courses
• Python primer
• Introduction to the main functions for Python
• Common financial functions in Python
• How to setup Google Colab 
• Mounting Google drive for Google Colab
• Financial data sources
• Data retrieval with OpenBB 
Go To Courses

WHAT YOU LEARN


Practical machine learning and deep learning for finance

Deep learning models for tactical asset allocation

Theory, code, and methodology

Feature engineering and data transformation for finance

Regression and deep learning in portfolio construction

Natural Language Processing for funds retrieval

Python programming for data science for use in finance

Python libraries and functions for financial metrics

Selected open source tools

Evaluating model performance

YOUR LEARNING RESOURCES


Code repository on Github

One-on-One office hours

Learning Management Platform 

Certification upon completion

Newsletters and Original Articles 

Community of experts