About Fundamental Data Science

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The Digital Transformation: Fundamental Data Science course introduces business and IT professionals to the essential role of data science in shaping modern organizations. Covering artificial intelligence (AI), machine learning, and big data, the course highlights both opportunities and challenges while unpacking key methods in data analysis, processing, and interpretation. Participants gain practical insight into how data-driven approaches enhance efficiency, strategy, and innovation.

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Program Structure  

• 100% online  

• Self-paced  

• 5 modules (50 hours total)  

• Includes video lessons and quizzes  

• Certificate of Completion provided  

 

Modules

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Fundamental Digital Transformation   

• Key drivers and challenges of digital transformation  

• Shifting from product-centric to customer-centric strategies  

• Customer journeys, omni-channel experiences, and data intelligence  

• Data sources, collection methods, and smart data use  

• Intelligent and automated decision-making in business processes  

 

Digital Transformation in Practice   

• Core concepts of distributed solution design and data integration  

• Key automation technologies: cloud computing, blockchain, IoT, and RPA  

• Introduction to data science in digital transformation: big data, machine learning, and AI  

• Benefits, risks, and challenges of emerging technologies  

• Designing and mapping end-to-end, customer-centric digital solutions  

• How data intelligence is collected, processed, and applied in transformation initiatives.

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Fundamental Big Data Analysis & Analytics 

• Core concepts, terminology, and business drivers of big data

• Characteristics and types of datasets in big data environments

• Fundamentals of analysis, analytics, and the big data lifecycle

• Business intelligence, visualization, and data storytelling techniques

• Key analytical methods: A/B testing, regression, classification, clustering, filtering, and sentiment analysis

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Fundamental Machine Learning

• Core business and technology drivers of machine learning

• Benefits, challenges, and common usage scenarios

• Data types, models, algorithms, and training processes

• Key learning methods: supervised, unsupervised, semi-supervised, and reinforcement learning

• Machine learning practices: preprocessing, model selection, optimization, and deployment

• Advanced connections to deep learning and AI applications

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Fundamental Predictive AI 

• Predictive AI drivers, benefits, and challenges

• Business applications and use cases

• Types of predictive AI and learning approaches

• Model training, data preparation, and learning processes

• Functional designs: computer vision, NLP, robotics, speech recognition

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***Participants may specialize in either Fundamental Machine Learning or Fundamental Predictive AI according to their interest.

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Fundamental Generative AI 

• Business and technology drivers of generative AI

• Benefits, risks, and challenges of generative AI

• Business problem categories addressed by generative AI

• Key models, algorithms, and neural network architectures (GANs, VAE, Transformers)

• Training generative models and building AI systems

• Best practices and types of generative AI