Advanced Concepts in Data Science - Course - ZISHI

Advanced Concepts in Data Science



Advanced Concepts in Data Science

For group bookings, to discuss tailored delivery or for any questions about this course, please get in touch:

Course Overview


The Advanced Concepts in Data Science course is designed for participants looking to deepen their understanding of complex data science principles and applications.


This course covers a range of advanced topics across six comprehensive modules, starting with kNN Design to explore the limitations and computational costs associated with neural networks.


Module Two delves into Big Data Manipulation, introducing Hadoop and MapReduce functions, and discussing the trade-offs between storage and computation costs. Advanced Statistics and Probability are tackled in Module Three, covering correlation, covariance, and moment function derivatives. Module Four offers insights into Bayesian Inference and Gaussian Process, including an introduction to new machine learning model techniques.


The course then explores NGrams and Their Applications in Module Five, providing a foundation in text analysis and the value of NGrams. Finally, Module Six focuses on PyTorch Applications, highlighting the benefits and pitfalls of this open-source initiative alongside supporting operating system tools.


This course is perfect for data science professionals seeking to leverage advanced techniques and tools in their work.



Learning Objectives



  • Understand the limitations and computational implications of extending neural network models, including an analysis of when more complexity strengthens or weakens a network’s predictive power.
  • Gain proficiency in manipulating large datasets with Big Data technologies such as Hadoop and MapReduce, including an ability to weigh the costs of storage versus computation in data science projects.
  • Master advanced statistical and probabilistic methods, including correlation, covariance, moment, and function derivatives, to enhance data analysis and model accuracy.
  • Learn the principles and applications of Bayesian Inference and Gaussian Processes, enabling the development of sophisticated machine learning models that can improve decision-making processes.
  • Acquire skills in text analysis through the understanding and application of NGrams and Bag of Words concepts, broadening the ability to extract insights from textual data.
  • Explore the capabilities and limitations of PyTorch as an open-source machine learning library, including how to integrate it with supporting operating system tools for advanced data science projects.
Advanced Concepts in Data Science - download prospectus

Course Modules


  • kNN Design
  • Big Data Manipulation
  • Advanced Statistics and Probability
  • Bayesian Inference and Gaussian Process
  • NGrams and Their Applications
  • PyTorch Applications

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Advanced Concepts in Data Science

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