Deep Dive - Module Content

The learning modules equip participants with fundamental technical knowledge and offer the opportunity to specialize

Excerpt Obligatory Content:

  • Data Sourcing & Infrastructure
  • Coding Essentials and Statistical Foundations
  • ESG & Sustainability - Data and Regulation

Excerpt Specialized Pathways:

  • Machine Learning in Data Science
  • Discovering AI (incl. low-code solutions for data science)

DAS Responsible Data Science

APPROACH
At Steinbeis SIT, our forward-thinking triple transformation approach is at the heart of what we do. We seamlessly combine green, digital, and social/cultural elements, aiming to solve complex issues with holistic solutions.

INTEGRATIVE

Sustainable Finance & Data Science

USE-CASE-BASED

Hands-on, tailored approach for each company’s unique needs.

ESG DATA-CENTERED

Focusing on ESG data sets used specifically within relevant use cases.

Program Curriculum

Obligatory Modules

Module 1 - The Power of Data for Organizational Transformation  
Duration - 1 week
  • Introduction to Green and Digital Transition
  • Building a Data-Driven Organization
  • Translate business problems into data hypotheses
  • Explore and describe datasets with visualization
  • Excel Fundamentals & Estimate statistics of a data set
Module 2 - Foundations of Data Science   
Duration - 2 week
  • Inferential Statistics
  • Essential SQL Skills
  • Basic Python (Pandas, Seaborn)
  • SPSS Statistics- analyse
  • Data Science powered by AI
Module 3 - Intelligent Data Analysis & Storytelling   
Duration - 1 week
  • Data Storytelling and Performance Metrics
  • Master data visualization techniques with Tableau user
  • Data Analysis and visualization using Microsoft Power Bi
Module 4 - Responsible Management in the Age of AI   
Duration - 1 week
  • Integrative Responsible Management
  • Compliance and Responsibility
  • Ethics in Data Science
  • Corporate Digital Responsibility and AI Alignment
Module 5 - ESG & Sustainability - Data and Regulation   
Duration - 2 week
  • Key elements and conceptual frameworks
  • The rapidly changing context of finance
  • Sustainable Finance Regulation - CSRD, SFDR, EU Taxonomy
  • ESG Data
  • EU AI Act
  • Disruptive technologies as opportunity for Sustainable Finance

Pathway  1 Accelerated Data Science

Module 1 - Machine Learning in Data Science  
Duration - 2 week
  • Introduction to supervised learning: Regression, Classification, Decission trees
  • Introduction to unsupervised Learning: Clustering
  • Model evaluation
  • Identify the current and potential applications of DL, ML, and AI
  • Artificial Neural Network and Deep Learning
Module 2 - Data Science Advancement: AI-driven Methods and Strategies   
Duration - 2 week
  • Python Programming Advanced
  • Data Scientist with Python Libraries
  • Advanced SQL Skills
  • Introduction to LLM models
  • Prompt engineering
  • Open AI in Azure
  • Gemini in Google cloud

Pathway  2 Accelerated Data Science

Module 1 - Machine Learning in Data Science  
Duration - 2 week
  • Introduction to supervised learning: Regression, Classification, Decission trees
  • Introduction to unsupervised Learning: Clustering
  • Model evaluation
  • Identify the current and potential applications of DL, ML, and AI
  • Artificial Neural Network and Deep Learning
Module 2 - Data Science Advancement: AI-driven Methods and Strategies   
Duration - 2 week
  • Python Programming Advanced
  • Data Scientist with Python Libraries
  • Advanced SQL Skills
  • Introduction to LLM models
  • Prompt engineering
  • Open AI in Azure
  • Gemini in Google cloud

Pathway  3 AI-Enhanced Low-Code Data Science

Module 1 - Discovering AI  
Duration - 2 week
  • Foundations of AI - Understanding the roles of algorithms, models, and data in AI applications
  • Generative AI and Prompt Engineering
  • Cloud-based AI Services - Open AI in Azure, Gemini in Google Cloud, Microsoft Copilot Studio, Azure AI Studio
  • Low-Code and No-Code Solutions
Module 2 - Machine Learning in Data Science   
Duration - 2 week
  • Introduction to supervised learning: Regression, Classification
  • Decission trees
  • Introduction to unsupervised Learning: Clustering
  • Model evaluation
  • Identify the current and potential applications of DL, ML, and AI
  • Artificial Neural Network and Deep Learning

Optional Self-Paced Modules

API Integration & Development
Cloud Development Foundations
Applied Deep Learning
Data Governance
Digital Complience

Highly Individualized Pathways

Centered around the learner, roles, and company needs

Transfer Learning: Use Case Extract

Calculations will be carried out by participants using their previously acquired skills, supported by experienced facilitators

Use case extract

Setup up technical environment
Query required data from SQL databases
Write functions for necessary calculations
Apply functions to data
Interpret results

Program Information

DURATION

6 weeks
4-6 hours per week

PROGRAM FEES

USD 2,800
Flexible payment options are available for this program

CONTACT

stanford@emeritus.org
+1 (401) 443 9709 (US)              
+44 12 7959 8043 (UK)              
+65 3129 4367 (Singapore)
SCHEDULE A CALL  TO LEARN MORE
REGISTER FOR THE PROGRAM
REFER YOUR COLLEAGUE AND RECEIVE A BENEFIT

Who is this program for?

This program is tailored for emerging leaders who are:

Acknowledging the importance of Twin Transformation and wish to leverage data and digital culture to steer decisions across their organizations towards strategic solutions.
Aiming to use Data combined with ESG Principals for effective decision-making in order to develop resilient ESG digital culture, optimize operational processes and sustain competitiveness through newly shaped digital approach.
Intending to launch technical projects or those already occupying positions that mandate a mindset prioritizing digital aspects to spreadhead digital transformation within their organizations.