INE Online Bootcamp

Data Analysis, Visualization, and Predictive Modeling

David Mertz, Ph.D.

Data Scientist

Session: March 2021

To launch the material for this course, click on the Binder link:

Outline

Adjustments may be made to the outline based on student needs. The working plan is as follows:

• DAY 1

  • Python Essentials, part 1
  • Break and programming exercises
  • Pandas Series
  • Break and programming exercises
  • Pandas DataFrames
  • Break and programming exercises
  • Review and questions

• DAY 2

  • Continuing Python essentials
  • Break and programming exercises
  • The Ethics of Visualization
  • Break and thought exercises
  • Visualization using Pandas
  • Break and programming exercise
  • Advanced Pandas: Groupby and Timeseries
  • Break and programming exercise
  • Review and questions

• DAY 3

  • Seaborn statistical plots
  • Break and programming exercises
  • Linear and Polynomial Fitting
  • Break and programming exercises
  • Data Analysis for Machine Learning
  • Break and programming exercises
  • Review and evaluation

REFERENCE MATERIAL

• The Python Tutorial, Official Python Documentation Team

Books

• Cleaning Data for Effective Data Science: Doing the Other 80% of the Work. David Mertz, Packt Publishing, 2021
• Visual Explanations, Edward Tufte, 1997
• Data Visualization: A Practical Introduction, Kieran Healy, 2019
• Python Data Science Handbook, Jake VanderPlas
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Wes McKinney
• Introduction to Machine Learning with Python: A Guide for Data Scientists, Andreas C. Müller & Sarah Guido