Booz | Allen | Hamilton x Data Society
Intermediate Data Science Bootcamp

Intermediate Data Science Bootcamp

Hours of Instruction: 32 Hours
Duration of Bootcamp: 36 Hours
Modality: Virtual, Zoom classroom

Text Mining

Learn to prepare data for text mining. Integrate into commercial applications. Extract key summary metrics and words from a corpus of documents.

Download the syllabus
Download the FlexEd Funding Request
Who should enroll?

Intermediate Data Science Bootcamp

Who is this class for?
  • Students applying for this course should have demonstrable experience using Python to manipulate and visualize data and feel comfortable learning new packages and interpreting new code snippets.
  • Applicants should have a background of building and assessing the performance metrics of basic classification models and are looking to expand on this skillset.
  • This course is for professionals ready to take learning from the classroom to the “real world.” Students in this course understand that working with applied/live datasets can present challenges that don’t exist in a controlled learning environment.
Suggested Hardware Setup

Although it is not a requirement, we strongly urge students to have a two-monitor setup to improve the virtual learning experience.

Certificate Requirements

Booz Allen and Data Society will enforce the following criteria to receive a course completion certificate:

  • Students will be required to attend at minimum 13 of the 16 live sessions. Please note: session recordings will be available for two weeks after the instruction date but will not count towards live-instruction attendance.
  • Capstone Projects will require a passing grade. For additional information/grading rubric criteria, please contact

Intermediate Data Science Bootcamp

Please complete the following steps to process your payment for the Intermediate DS Bootcamp. FlexEd options for advanced funding or reimbursement are available. More information can be found in Degreed.

Advanced Classification

Learn to make your classification model perform better and choose your methods and parameters based on the problem at hand. Logistic Regression as it relates to neural networks. Ensemble methods using Random Forest.

Questions? Get in Touch

Send us an email:

Booz Allen Hamilton

Or contact your Data Society representative:

Data Society

For urgent questions:

Call 202-670-6090