Loading...

Course Description

Though visual representations of quantitative information were traditionally cast as the end phase of the data analysis pipeline, visualizations can play important roles throughout the analytic process and are critical to the work of the data scientist. Where static outputs and tabular data may render patterns opaque, human visual analysis can uncover volumes and lead to more robust programming and better data products. For students getting started with data science, visual diagnostics are particularly important for effective machine learning. When all it takes is few lines of Python to instantiate and fit a predictive model, visual analysis can help navigate the feature selection process, build intuition around model selection, identify common pitfalls like local minima and overfit, and support hyperparameter tuning to render more successful predictive models.

In this course, students will learn to deploy a suite of visual tools using Scikit-Learn, Matplotlib, Pandas, Bokeh, and Seaborn to augment the analytic process and support machine learning from preliminary feature analysis through model selection, evaluation, and tuning. 

Course Objectives

Upon successful completion of the course, students will be able to use visualizations to:

  • Summarize and analyze a range of data sets.
  • Support feature engineering and feature selection.
  • Diagnose common machine learning problems like bias, heteroscedasticity, underfit, and overtraining.
  • Evaluate their machine learning models' performance, stability, and predictive value.
  • Steer their predictive models toward more successful results.

 

 

Notes

Enrollment in this course is restricted. Students must submit an application and be accepted into the Certificate in Data Science in order to register for this course.

Current Georgetown students must create an application using their Georgetown NetID and password. New students will be prompted to create an account.

Course Prerequisites

Course prerequisites include:

  • A bachelor's degree or equivalent
  • Completion of at least two college-level math courses (e.g. statistics, calculus, etc.)
  • Successful completion of Data Analysis II: Machine Learning (XBUS-505)
  • Basic familiarity with programming or a programming language
  • A laptop for class meetings and coursework

Applies Towards the Following Certificates

Loading...

Enroll Now - Select a section to enroll in

Type
Class
Days
Sa
Time
9:00AM to 4:00PM
Dates
Jun 05, 2021 to Jun 12, 2021
Schedule and Location
Contact Hours
12.0
Course Tuition
Tuition non-credit $833.00 Click here to get more information
Section Notes

Welcome to the Flex Learning Experience - Real-time learning using live Zoom video conferencing— mirroring a more traditional classroom with regular interaction, - engaging activities, and the dynamic exploration of topics and concepts.

  • Dynamic exploration of topics, ideas and concepts with the instructor and students in the class
  • Interact regularly and frequently with your instructors and other students
  • Comparable level of accountability and engagement as classroom attendance
  • Lectures, discussions, and presentations occur at a specific hour
  • Face-to-face discussion, individual guidance, speed and immediacy to synchronous online learning
  • Immediate feedback - encouraging quick feedback on ideas, and support consensus and decision making
  • Pacing - encouraging students to keep up-to-date and provide a discipline to learning
  • Spontaneity - making it easy to add new ideas to the conversation, brainstorming or decision making
  • Familiarity - simulating a more traditional face-to-face environment

 

Computing Requirements

Students will be expected to use a personal laptop to complete analytics and programming workshops and a Capstone project. Students should have administrative access and be able to install required course software and libraries. We recommend the following minimum computing requirements:

  • A laptop with at least a dual-core 1.8 GHz processor, 4GB of RAM, and 20 GB free hard disk space (e.g. a laptop purchased in the past two years).
  • A modern operating system: Windows 10 or newer (updated to the latest semi-annual channel version), OS X 10.15 Catalina or newer, or Ubuntu 20.04 or newer (or an equivalent Linux distribution). OS X and Linux are strongly encouraged.
  • Administrator access on your system to install new software.
  • Python 3.8 (or later) or Anaconda 2020.07 (or later) installed on your system.
  • A command prompt available (Powershell on Windows, Terminal on OS X or Linux).

Please note that computing requirements and software dependencies may change. 

Type
Class
Days
Sa
Time
9:00AM to 4:00PM
Dates
Aug 21, 2021 to Aug 28, 2021
Schedule and Location
Contact Hours
12.0
Course Tuition
Tuition non-credit $833.00 Click here to get more information
Section Notes

Welcome to the Flex Learning Experience - Real-time learning using live Zoom video conferencing— mirroring a more traditional classroom with regular interaction, - engaging activities, and the dynamic exploration of topics and concepts.

  • Dynamic exploration of topics, ideas and concepts with the instructor and students in the class
  • Interact regularly and frequently with your instructors and other students
  • Comparable level of accountability and engagement as classroom attendance
  • Lectures, discussions, and presentations occur at a specific hour
  • Face-to-face discussion, individual guidance, speed and immediacy to synchronous online learning
  • Immediate feedback - encouraging quick feedback on ideas, and support consensus and decision making
  • Pacing - encouraging students to keep up-to-date and provide a discipline to learning
  • Spontaneity - making it easy to add new ideas to the conversation, brainstorming or decision making
  • Familiarity - simulating a more traditional face-to-face environment

 

Computing Requirements

Students will be expected to use a personal laptop to complete analytics and programming workshops and a Capstone project. Students should have administrative access and be able to install required course software and libraries. We recommend the following minimum computing requirements:

  • A laptop with at least a dual-core 1.8 GHz processor, 4GB of RAM, and 20 GB free hard disk space (e.g. a laptop purchased in the past two years).
  • A modern operating system: Windows 10 or newer (updated to the latest semi-annual channel version), OS X 10.15 Catalina or newer, or Ubuntu 20.04 or newer (or an equivalent Linux distribution). OS X and Linux are strongly encouraged.
  • Administrator access on your system to install new software.
  • Python 3.8 (or later) or Anaconda 2020.07 (or later) installed on your system.
  • A command prompt available (Powershell on Windows, Terminal on OS X or Linux).

Please note that computing requirements and software dependencies may change. 

Required fields are indicated by .