DATA SCIENCE & ANALYSIS FUNDAMENTALS WITH PYTHON

$1,250.00

The Data Science & Analysis Fundamentals with Python course provides students with essential skills for data collection, cleanup, transformation, summarization, and visualization.

SKU: LUX-DSFUND Category:

The Data Science & Analysis Fundamentals with Python course will provide students with essential skills for data collection, cleanup, transformation, summarization, and visualization.

Students will do hands-on in-class and out-of-class assignments. This is a hands-on course that utilizes the related Python libraries to master these fundamentals

Next Classes

TBD

Course Prerequisites

  • Introduction to Data Science
  • Python for Data Science

Sample Schedule and Agenda

Six hours of instruction across four days. All times are Pacific.

9:30 am – 10:30 am Topic instruction
10:30 am – 11: 00 am Hands on topic experience
11: 30 am – 12:30 pm Lunch
1:00 pm – 2:00 pm Topic instruction
2:00 am – 2:30 pm Hands on topic experience
3:00 pm – 4:00 pm Topic instruction
4:00 pm – 4:30 pm Hands on topic experience

Cost

$1,250

Instructor: Christopher Gantz

Christopher Gantz successfully leverages his extensive mathematical, computer engineering and investment management expertise to enhance the design and implementation of various analytic software models/tools utilized to continuously improve quantitative investment research, analysis and forecasting.

He is the lead/primary designer and developer for various key quantitative analytic software model/tool projects i.e. designing, implementing, testing, integrating and maintaining the corresponding software and database repository. He is also a key contributor to both the underlying architecture and process.

Christopher is an accomplished advanced software system applied researcher and software developer whose resume includes positions with Sun Microsystems, Bell Laboratories, US West Advanced Technologies and the Mitre Corporation.

Suggested Reading

“Getting Started with Data Science: Making Sense of Data with Analytics (IBM Press)” by Haider, Murtaza

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