Data Science and Engineering 4:

DSE 4: Large Scale Data Science, Ops, and Engineering

This advanced DSE course teaches you how to engineer performant data science and deep learning pipelines, databases, and computation on more than one core of a machine, and then on more than one machine. It picks up where Data Science 3 ends, taking your ability to run models on one machine and teaching you how to scale your data access, preprocessing, and model size up. We start with parallel programming on single systems, and move to using dask and scikit-learn to explore python only data-centric and compute-centric workflows. We learn to use spark for pre-processing data and for machine learning. We then learn about data structures important in data science and databases, and how to construct data stores for our own applications. Finally we move to the ops part, where we learn how to manage our own clusters on cloud systems, and we then use this knowledge to scale up our own deep learning applications.

  • AI 1
  • DSE 1
  • DSE 2
  • AI 2

DSE 3 will certainly help, and is best taken earlier.

  • Parallel programming using asyncio, threads and processes. State.
  • Dask data and program parallelism and scikit-learn and other libraries
  • Data structures for databases and data management
  • data storage models using sql and nosql, data streaming
  • Graphs and their applications in data science: computational graphs and social structures
  • large data analysis using spark RDDs and large scale data access
  • Managing machine learning pipelines and clusters using kubernetes
  • Deeper dive into cloud technologies for large scale systems
  • parallel processing paradigms for deep learning

After you finish this module, you will be able to scale your teams’ and your machine learning and deep learning infrastructure to very large models and to very large data sizes. You will learn about data-science relevant data structures (this will help you in interviews as well!) You will be able to choose the right technologies for data storage and streaming, and manage and code large clusters for your deep learning applications.

Prerequisites

Topics

Learning Objectives

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Schedule

Students

Summer Cohort

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Conducted alongside elective

Winter Cohort

Intake 1:

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Conducted along with elective

Intake 2:

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Conducted along with elective

Working Professionals

Summer Cohort

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Conducted along with elective

Fall Cohort

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Conducted along with elective

Winter Cohort

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Conducted along with elective