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Beginning Data Science with Python and Jupyter
AWS Training Pass
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OverviewGet to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. You’ll finish up by learning how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this course, you’ll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You’ll start with the basics of Jupyter, which will be the backbone of the course. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the course ends by looking at data collection techniques. You’ll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.
This course covers every aspect of the standard data workflow process with a perfect blend of theory, practical hands-on coding, and relatable illustrations. Each module is designed to build on the learnings of the previous lesson. The course contains multiple demos that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.
This is a self-paced video course. Any files needed for exercises or activities will be available for download from the course page.
- Get up and running with the Jupyter ecosystem
- Plan a machine learning classification strategy and train models
- Tune and enhance models validation curves by reducing dimensionality
- Transform tabular data from web pages into Pandas DataFrames
- Build interactive, web-friendly visualizations to show your findings
- Basic Functionality and Features
- Our First Analysis - The Boston Housing Dataset
- Preparing to Train a Predictive Model
- Training Classification Models
- Scraping Web Page Data
- Interactive Visualizations
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
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Self-Paced Training Terms & Conditions
Web Based Training courses are sold on a per-user basis. WBT courses provide a training advantage for you and your teams, helping you get up to speed quickly. Take the courses you need, at your convenience and at your own pace.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 12 months to complete your course. Within this 12 months, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
After you register for the course, an access instructions email will be sent within 1-2 business days. Within the enrollment period, this self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours per day.
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