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Data Structures, Data Mining and Big Data with Python

A required course in the Python for Data Science, Web and Core Programming Specialized Studies Program.

Course Description

Learn advanced Python programming features used to solve large data problems. Topics include ETL using command line interface, functional programming, mySQL, MapReduce framework using Hadoop streaming and MRJob, and Spark with SparkML. Students will gain practical experience with Amazon Web Services Elastic Computing and Elastic MapReduce. Explore how the Python built-in data structures such as lists, dictionaries, and tuples can be used to perform increasingly complex data analysis. An introduction to regression and cluster models for data mining and basic machine learning for analysis will also be covered. The course will emphasize the use of cloud computing to solve large data problems. Prerequisite: I&C SCI X426.59 Intermediate Python.

Prerequisites: I&C SCI X426.64 Introduction to Programming for Python or I&C SCI X426.62 Python for Data Analysis. It is assumed that the students have familiarity with jupyter notebooks, python core libraries such as os, sys, collections, programming flow control, functions, classes, and basic data types such as string, tuples, dictionary and the methods that are available for these data structures.

  • Details
  • $820
  • January 31, 2022 to March 20, 2022
  • Delivery Mode: Online
  • Reg#: 00227
  • ID/Units: I&C SCI X426.70  (2.00)
    ( Section 1 )
  • Quarter: WINTER 2022


Ted Pham, B.S., Ph.D., received his BS in Electrical Engineering from UCLA and PhD in Biomedical Engineering from UC Irvine where he was a National Science Foundation and Department of Education Fellow. His scientific achievements include 8 peer-reviewed publications and 1 issued patent. He has worked with embedded software for microelectronic sensors, reducing healthcare cost using machine learning, and IT service management using time series analysis. Ted’s expertise includes data architecture, machine learning at scale, and statistical analysis.

Textbook Information

Textbooks for your course may be purchased from any vendor or bookseller of your choice.

No textbooks are required for this course.

Meeting Schedule

EventDateDayStart TimeEnd TimeLocationRoom
START01/31/2022Monday------Online (Access Begins)---
END03/20/2022Sunday------Online (Access Ends)---