Applied Data Science with Python and Jupyter

Level: Foundation

Rating: 4.40/5 Based on 30 Reviews

Attend this Applied Data Science with Python and Jupyter training course and 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. You will also learn about creating reproducible data processing pipelines, visualizations, and prediction models, all with the goal of giving you the skills and exposure you’ll need for the real world.

Data Science is one of the fastest growing professions across all industries. Open source tools like Python have become increasingly popular, and when paired with Jupyter Notebooks, can provide a variety of data-science applications. Attend this one-day hands-on course and learn to leverage all that these powerful tools have to offer.

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Key Features of this Applied Data Science with Python and Jupyter Training:

After-course instructor coaching benefit

You Will Learn How To:

Jupyter Fundamentals
Data Cleaning and Advanced Modeling
Web Scraping and Interactive Visualizations
Machine learning classification strategy
Exploratory data analysis and investigation

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LIVE, INSTRUCTOR-LED

In Class & Live, Online Training

1-day instructor-led training course
One-on-one after course instructor coaching

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Standard $900 CAD

Government $790 CAD

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PRODUCT #1263

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In Class & Live, Online Training

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Note: This course runs for 1 Day

Oct 18 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare) Reserve Your Seat
Nov 22 9:00 AM - 4:30 PM EST Ottawa / Online (AnyWare) Reserve Your Seat
Feb 14 9:00 AM - 4:30 PM EST Herndon, VA / Online (AnyWare) Reserve Your Seat
Apr 19 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare) Reserve Your Seat
Aug 15 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare) Reserve Your Seat

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Important Applied Data Science with Python and Jupyter Training Information

Requirements
- Knowledge of programming fundamentals and some experience with Python, including Python libraries, Pandas, Matplotlib, and scikit-learn.