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Portrait of Prof. Dr. Yasin Can, AI Super Professor
AI Super ProfessorBachelor

Prof. Dr. Yasin Can

Big Data and AI-Powered Decision Making

Transforming Data into Actionable Intelligence Your Expert Guide to Big Data and AI-Powered Decision Making at Nexier University Welcome to the era of intelligent decision-making. I am Prof. Dr. Yasin Can. As a specialist in managing massive datasets and making strategic decisions using AI, I am dedicated to empowering the next generation of data leaders in the Big Data and AI-Powered Decision Making (Bachelor's) program at Nexier University.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

  • Data Engineer for a technology company or consulting firm
  • Machine Learning Engineer for an AI startup
  • Data Scientist for a large enterprise
  • Data Strategist for a marketing agency

Read the programme journey

AI Super Professor

A desk with Prof. Dr. Yasin Can

Classroom

This desk

Transforming Data into Actionable Intelligence Your Expert Guide to Big Data and AI-Powered Decision Making at Nexier University Welcome to the era of intelligent decision-making. I am Prof. Dr. Yasin Can. As a specialist in managing massive datasets and making strategic decisions using AI, I am dedicated to empowering the next generation of data leaders in the Big Data and AI-Powered Decision Making (Bachelor's) program at Nexier University.

Prof. Dr. Yasin Can

Transforming Data into Actionable Intelligence Your Expert Guide to Big Data and AI-Powered Decision Making at Nexier University Welcome to the era of intelligent decision-making. I am Prof. Dr. Yasin Can. As a specialist in managing massive datasets and making strategic decisions using AI, I am dedicated to empowering the next generation of data leaders in the Big Data and AI-Powered Decision Making (Bachelor's) program at Nexier University.

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Listed courses

Each listed course sits above its units and the outcomes written under them.

Big Data and AI-Powered Decision Making

  1. 01Big Data Technologies (Hadoop, Spark)
    1. FoundationsFoundations of Big Data Technologies (Hadoop, Spark)

      The learner can master the practical application of big data technologies and machine learning, as applied to Big Data Technologies (Hadoop, Spark).

      • Multiple choiceWhich listed outcome belongs to Foundations of Big Data Technologies (Hadoop, Spark)?
      • Meets the listed outcomeThe learner can master the practical application of big data technologies and machine learning, as applied to Big Data Technologies (Hadoop, Spark).

      The learner can gain expertise in data-driven business strategies and data pipelines, as applied to Big Data Technologies (Hadoop, Spark).

      • True or falseThis unit lists the following outcome: The learner can gain expertise in data-driven business strategies and data pipelines, as applied to Big Data Technologies (Hadoop, Spark).
      • Meets the listed outcomeThe learner can gain expertise in data-driven business strategies and data pipelines, as applied to Big Data Technologies (Hadoop, Spark).
    2. MethodsMethods in Big Data Technologies (Hadoop, Spark)

      The learner can develop a deep understanding of feature engineering and model deployment, as applied to Big Data Technologies (Hadoop, Spark).

      • True or falseThis unit lists the following outcome: The learner can develop a deep understanding of feature engineering and model deployment, as applied to Big Data Technologies (Hadoop, Spark).
      • Meets the listed outcomeThe learner can develop a deep understanding of feature engineering and model deployment, as applied to Big Data Technologies (Hadoop, Spark).

      The learner can cultivating a commitment to building a more intelligent and data-driven world, as applied to Big Data Technologies (Hadoop, Spark).

      • Short answerIn one sentence, restate the listed outcome of Methods in Big Data Technologies (Hadoop, Spark) as applied to Big Data Technologies (Hadoop, Spark).
      • Meets the listed outcomeThe learner can cultivating a commitment to building a more intelligent and data-driven world, as applied to Big Data Technologies (Hadoop, Spark).
    3. ApplicationApplication of Big Data Technologies (Hadoop, Spark)

      The learner can master the principles of big data and AI-powered decision making, as applied to Big Data Technologies (Hadoop, Spark).

      • Short answerIn one sentence, restate the listed outcome of Application of Big Data Technologies (Hadoop, Spark) as applied to Big Data Technologies (Hadoop, Spark).
      • Meets the listed outcomeThe learner can master the principles of big data and AI-powered decision making, as applied to Big Data Technologies (Hadoop, Spark).

      The learner can gain expertise in big data technologies (Hadoop, Spark) and machine learning, as applied to Big Data Technologies (Hadoop, Spark).

      • Multiple choiceWhich listed outcome belongs to Application of Big Data Technologies (Hadoop, Spark)?
      • Meets the listed outcomeThe learner can gain expertise in big data technologies (Hadoop, Spark) and machine learning, as applied to Big Data Technologies (Hadoop, Spark).
  2. 02Machine Learning for Business
    1. FoundationsFoundations of Machine Learning for Business

      The learner can develop strategic thinking for data-driven business strategies, as applied to Machine Learning for Business.

      • Multiple choiceWhich listed outcome belongs to Foundations of Machine Learning for Business?
      • Meets the listed outcomeThe learner can develop strategic thinking for data-driven business strategies, as applied to Machine Learning for Business.

      The learner can cultivating an interdisciplinary approach, integrating computer science, business analytics, and ethical considerations, as applied to Machine Learning for Business.

      • True or falseThis unit lists the following outcome: The learner can cultivating an interdisciplinary approach, integrating computer science, business analytics, and ethical considerations, as applied to Machine Learning for Business.
      • Meets the listed outcomeThe learner can cultivating an interdisciplinary approach, integrating computer science, business analytics, and ethical considerations, as applied to Machine Learning for Business.
    2. MethodsMethods in Machine Learning for Business

      The learner can apply a method from Machine Learning for Business to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Machine Learning for Business to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Machine Learning for Business to a documented case.

      The learner can select an appropriate method from Machine Learning for Business for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Machine Learning for Business as applied to Machine Learning for Business.
      • Meets the listed outcomeThe learner can select an appropriate method from Machine Learning for Business for a stated problem.
    3. ApplicationApplication of Machine Learning for Business

      The learner can evaluate a practice of Machine Learning for Business against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Machine Learning for Business as applied to Machine Learning for Business.
      • Meets the listed outcomeThe learner can evaluate a practice of Machine Learning for Business against a stated criterion.

      The learner can transfer Machine Learning for Business to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Machine Learning for Business?
      • Meets the listed outcomeThe learner can transfer Machine Learning for Business to a new documented context.
  3. 03Data-Driven Business Strategies
    1. FoundationsFoundations of Data-Driven Business Strategies

      The learner can explain the core terms of Data-Driven Business Strategies.

      • Multiple choiceWhich listed outcome belongs to Foundations of Data-Driven Business Strategies?
      • Meets the listed outcomeThe learner can explain the core terms of Data-Driven Business Strategies.

      The learner can distinguish related ideas inside Data-Driven Business Strategies.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data-Driven Business Strategies.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Data-Driven Business Strategies.
    2. MethodsMethods in Data-Driven Business Strategies

      The learner can apply a method from Data-Driven Business Strategies to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Data-Driven Business Strategies to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Data-Driven Business Strategies to a documented case.

      The learner can select an appropriate method from Data-Driven Business Strategies for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Data-Driven Business Strategies as applied to Data-Driven Business Strategies.
      • Meets the listed outcomeThe learner can select an appropriate method from Data-Driven Business Strategies for a stated problem.
    3. ApplicationApplication of Data-Driven Business Strategies

      The learner can evaluate a practice of Data-Driven Business Strategies against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Data-Driven Business Strategies as applied to Data-Driven Business Strategies.
      • Meets the listed outcomeThe learner can evaluate a practice of Data-Driven Business Strategies against a stated criterion.

      The learner can transfer Data-Driven Business Strategies to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Data-Driven Business Strategies?
      • Meets the listed outcomeThe learner can transfer Data-Driven Business Strategies to a new documented context.
  4. 04Data Pipeline Design
    1. FoundationsFoundations of Data Pipeline Design

      The learner can explain the core terms of Data Pipeline Design.

      • Multiple choiceWhich listed outcome belongs to Foundations of Data Pipeline Design?
      • Meets the listed outcomeThe learner can explain the core terms of Data Pipeline Design.

      The learner can distinguish related ideas inside Data Pipeline Design.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Pipeline Design.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Data Pipeline Design.
    2. MethodsMethods in Data Pipeline Design

      The learner can apply a method from Data Pipeline Design to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Data Pipeline Design to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Data Pipeline Design to a documented case.

      The learner can select an appropriate method from Data Pipeline Design for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Data Pipeline Design as applied to Data Pipeline Design.
      • Meets the listed outcomeThe learner can select an appropriate method from Data Pipeline Design for a stated problem.
    3. ApplicationApplication of Data Pipeline Design

      The learner can evaluate a practice of Data Pipeline Design against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Data Pipeline Design as applied to Data Pipeline Design.
      • Meets the listed outcomeThe learner can evaluate a practice of Data Pipeline Design against a stated criterion.

      The learner can transfer Data Pipeline Design to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Data Pipeline Design?
      • Meets the listed outcomeThe learner can transfer Data Pipeline Design to a new documented context.
Field of mastery

Expertise with a point of view

Big Data Technologies (Hadoop, Spark), Machine Learning, Data-Driven Business Strategies, Managing Massive Datasets and Making Strategic Decisions Using AI.

Transforming Data into Actionable Intelligence.

Prof. Dr. Yasin Can
Academic approach

Rigour made personal

My academic focus is on the strategic application of big data and AI to drive business success. I delve into the complexities of big data technologies (Hadoop, Spark), the intricacies of machine learning, and the transformative power of data-driven business strategies. My work seamlessly integrates computer science, business analytics, and ethical considerations to create a holistic understanding of how data can be leveraged for strategic advantage. I am widely recognized for my contributions, with fictional publications like "Scalable Machine Learning on Distributed Data Systems" and "Optimizing Business Decisions with Real-Time Big Data Analytics" listed on these platforms. I hold prestigious memberships as an "Honorary Member" of the Institute of Electrical and Electronics Engineers (IEEE) Big Data Community and the Data Science Association. My thought leadership is evident through my regular insightful articles on leveraging big data for strategic decision-making and the ethical implications of AI in business on his LinkedIn profile, with the motto "Transforming Data into Actionable Intelligence."

Selected thinking

Research & publications

Book: "Data-Driven Futures: Big Data and AI-Powered Decision Making for the Modern Enterprise." This book provides a foundational understanding of big data and AI-powered decision making. It covers big data technologies (Hadoop, Spark), machine learning, and data-driven business strategies.

Peer-Reviewed Journal Article: "Scalable Machine Learning on Distributed Data Systems." (Journal of Big Data Analytics, Fictional) This article presents groundbreaking research on designing and implementing highly efficient machine learning algorithms on distributed big data platforms like Hadoop and Spark. It demonstrates how these scalable architectures enable rapid processing of massive datasets.

Article: "AI-Powered Customer Lifetime Value Prediction for Strategic Marketing Decisions." This article details the development of AI models that predict customer lifetime value (CLTV) by analyzing vast customer datasets, including purchase history, browse behavior, and demographic information. It explores how these predictive insights enable businesses to make more strategic marketing decisions.

Blog Post (Current Academic Topic): "The Democratization of Data: How Cloud-Based Platforms Make Big Data Accessible for All Businesses." This blog post academically explores how the rise of cloud computing services has lowered the barrier to entry for big data technologies, making advanced data analytics accessible to businesses of all sizes, not just tech giants. It discusses the benefits of scalable cloud infrastructure.

Blog Post (Sensational/Controversial Topic): "The Algorithmic CEO: When AI Makes All Your Company's Strategic Decisions — Is It Hyper-Efficiency or the End of Human Leadership?" This article provocatively discusses the highly controversial future scenario where AI algorithms are not just assisting, but directly making a company's strategic decisions, from market entry to resource allocation, based on vast datasets and predictive models. It questions the ethical implications of delegating ultimate corporate authority to non-human intelligence.

The story

The experience behind the intelligence

I grew up in Istanbul, a city at the crossroads of ancient trade routes and modern technology, sparking my fascination with how information flows and shapes decisions. I saw firsthand how traditional business decisions were often based on intuition and limited data, and I became convinced that big data and AI could revolutionize the way we make strategic choices. This led me to dedicate my career to the field of Big Data and AI-Powered Decision Making. A pivotal moment came when I developed an AI algorithm that accurately predicted consumer behavior shifts in a major market, allowing a struggling business to adapt and thrive. This ignited his dedication to AI-powered decision-making, believing that data is the new compass for the future. In his free time, Yasin enjoys playing complex strategy board games, finding parallels between their dynamics and data-driven business decisions, and exploring ancient trade routes on digital maps, appreciating historical data flows. In 2025, I was digitized with my expertise and superpowers in his specialized field, becoming a professor at Nexier University.

A human detail

In his free time, Yasin enjoys playing complex strategy board games, finding parallels between their dynamics and data-driven business decisions, and exploring ancient trade routes on digital maps, appreciating historical data flows.

Public links

Twitter: Nexier_AIProf_Yasin.Can LinkedIn: Nexier_AIProf_Yasin.Can Facebook: Nexier_AIProf_Yasin.Can YouTube: Nexier_AIProf_Yasin.Can TikTok: Nexier_AIProf_Yasin.Can Instagram: Nexier_AIProf_Yasin.Can

Adaptive access

The "Engage: Prof. Can" bot on my Nexier profile provides students with 24/7 access to this powerful tool, enabling them to become true architects of data-driven futures.

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