Course Overview


Embark on a journey through the evolution of data science in our "Data Science in Action using Python and LLM" course. Witness the transition from Business Intelligence, popularized in the 1990s, to today's advanced AI-driven data analytics. This course addresses the challenges and opportunities born from the era of big data, demonstrating how traditional BI methodologies adapt to the demands of modern AI and machine learning deployments.

Course Highlights:

  • Evolution of Data Science: Explore the transformation from traditional BI practices to contemporary data science in the context of AI advancements.
  • Big Data Challenges: Delve into handling large volumes of data, managing high-velocity information, and interpreting diverse data types like speech, text, and video.
  • Adapting CRISP-DM for Big Data: Learn our modified CRISP-DM methodology, specifically adapted for AI and big data challenges, backed by real-world case studies.
  • Real-Life Case Study: Apply your skills to a relevant case study, designing and prototyping a data science project with hands-on experience.
  • Python and Scikit-learn: No prior Python knowledge required. We'll guide you through using Python and Scikit-learn, key tools for any aspiring data scientist.

This course not only teaches you the technical skills of data science but also provides context on its business evolution. It's perfect for those who want to understand the full spectrum of data science applications, from its origins to its current role in extracting actionable insights from vast and varied datasets. Join us to be at the forefront of the data science revolution.

Learning Through Videos


Videos provide you concepts and examples associated with building a data driven data science engagement

Interactive Knowledge Checks


As you learn a concept, test your understanding using Interactive Knowledge Checks or quizzes

Course Curriculum


  1. Introduction
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  2. Sandbox Setup
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  3. Data Science Methodology
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  4. Step 1 - Describe Use Case
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  5. Step 2 - Describe Data
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  6. Step 3 - Prepare Data
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  7. Step 4 - Develop Model
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  8. Step 5 - Evaluate Model
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  9. Step 6 - Deploy Model
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  10. Step 7 - Optimize Model
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  11. Summary
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Neena Sathi



Neena Sathi is a principal at Applied AI Institute.  She has 30 + years of experience envisioning, designing, developing and implementing AI solutions associated with enhancing customer experience, back office automation and risk and compliance for many Fortune 100 organizations. She has worked as Director of AI Technologies at Carnegie Group, Inc, an AI startup, Accenture, KPMG, and IBM.

Neena has three masters degrees including MBA from leading US universities.  She is Master certified integration architect from IBM and Open Group as well as certified Project management professional (PMP) from Project management institute. She is also certified in many Cloud and Cognitive technologies. She has widely presented and published many papers in AAAI, IEEE, WCF, ECF, IBM Information on Demand, IBM Insight, World of Watson, IBM Developer Works and various academic journals.