Data Science, Data Analytics, and AI

• Policymakers and analysts.
• Public servants, non-profit and community leaders.
• Public service organizations.

• Business associates, operations managers, project managers, and intelligence analysts.
• Finance, securities, and insurance professionals.
• Digital marketing and communication specialists.
• Professionals from every level or industry who work with analytics or data.

Designed for professionals who want to gain a deeper understanding of how AI systems are built and operate, as well as those who want hands-on experience building and deploying them. It’s ideal for individuals who are ready to explore the mechanics, frameworks, and real-world applications of AI, including:

• Software engineers, data scientists, DevOps engineers, and cloud engineers looking to deepen their technical AI expertise.
• Professionals aspiring to roles such as AI systems engineer, AI developer, ML engineer, NLP engineer, computer vision engineer, or deep learning engineer.
• Technical professionals and researchers who want to build, deploy, and maintain AI models in production environments.
• Professionals in adjacent technical fields who may not code daily but want to understand how AI systems are developed, integrated, and scaled.

• Prior mathematics and statistics experience are recommended, but not required. 

• A degree in engineering, mathematics, or computer science is recommended, but not required.
• Basic knowledge of programming and programming languages is strongly recommended.

• Proficiency in Python, including: 
• Basic understanding of linear algebra, including:  
• Familiarity with statistics, including:  
• Introductory probability
• Ability to work with structured datasets:  
• Comfort with concepts like:  
  • Regression and classification 
  • Correlation vs. causation 

• 40 -42 weeks (Note: This program includes four 10-week courses)
• 8 - 10 hours per week (per course)

• 6 to 10 weeks  (varies by program)
• 8-10 hours per week

• Basics of Modern Statistics and Analysis
• Python and Public Data
• Behavioural Insights, Modelling, and Predictive Analytics

• Foundations of Data Science
• Statistics for Data Science
• Machine Learning
• Big Data Management Systems and Tools

• Machine Learning
• Language Models
• Reinforcement Learning

• Agentic AI  

• Online learning with live, weekly Q&A sessions.

• Online learning with weekly live sessions.

• Online learning with weekly live sessions.

• Use basic methods of coding to conduct statistical analysis with R and Python.
• Draw behavioural insights to tell effective stories that support decision-making.
• Draw conclusions to impact your decision-making and policy development.
• Analyze data with the statistical methods used by social scientists.

• Explore the evolution of data science and predictive analytics.
• Know statistical concepts and techniques including regression, correlation, and clustering.
• Apply data management systems and technologies that reflect concern for security and privacy.
• Adopt techniques and technologies including data mining, neural network mapping, and machine learning.
• Represent big data findings visually to aid decision-makers.

• Core concepts and frameworks for building and using LLM-powered agents 
• Using context engineering for agent performance and reliability 
• Function calling to enable the agent to execute tasks 
• Designing autonomous and multi-agent systems using tools like AutoGen 
• Understanding agent memory types using vector stores, episodic memory, and RAG and their role in agent behaviour 
• Strategies for memory and knowledge compression