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TDWI Chicago Conference

T1 Hands-on: Python for Data Analysis

April 30, 2019

8:00 am - 5:30 pm

Duration: Full Day Course

Prerequisite: This course is hands-on. In order to participate in this course, all attendees are required to bring their own laptops. Program download and setup instructions will be sent upon registration for the course.

Python is one of the top languages used in data science and predictive analytics. In this hands-on course, we will use Jupyter notebooks to learn and develop Python code. Jupyter notebooks are widely used for data analysis and exploration projects. By the end of this workshop, you will have learned how to create and use Jupyter notebooks, how to load data from various sources, how to explore the data, how to discover and replace bad values, how to run analysis on the data and subgroups of the data, and how to produce basic visualizations. You will leave with complete code examples that you can use and build on in your own work.

You Will Learn

  • How to use Jupyter Notebooks to interactively explore data
  • How to load data into Python and Pandas from files and databases
  • How to sub-select, sort, and transform data
  • How to efficiently handle dates, times, and time zones in data
  • How to write your own Python functions
  • How to analyze data by groups and apply custom functions
  • How to write your own Python functions
  • How to plot your data

Geared To

  • Individuals with basic Python experience who are new to using Python for data analysis

Machine Requirements

  • Linux, OS X, or Windows
  • 32- or 64-bit computer
  • 8 MB available RAM
  • 600 MB available hard drive space to download and install Anaconda
    (We use Anaconda because it is a free, no-hassle way to install Python and the needed libraries. Instructions will be emailed to registrants prior to the event to prepare your laptop BEFORE the conference. There is no time allotted in class for laptop preparation.)

* Enrollment is limited to 40 attendees.

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TDWI Chicago

Hilton Chicago
Chicago, IL
April 28–May 3