LEVEL: FOUNDATION
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, visualisations, 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.
Key Features of this Applied Data Science with Python and Jupyter Training:
After-course instructor coaching benefitWhat does a Python developer do?
Will I learn how to Program in Python?
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Learning Objectives
- Jupyter Fundamentals
- Data Cleaning and Advanced Modelling
- Web Scraping and Interactive Visualisations
- Machine learning classification strategy
- Exploratory data analysis and investigation
Pre-Requisites
Knowledge of programming fundamentals and some experience with Python, including Python libraries, Pandas, Matplotlib, and scikit-learn.
Course Content
Lesson 1: Jupyter Fundamentals
- Basic Functionality and Features
- Our First Analysis - The Boston Housing Dataset
Lesson 2: Data Cleaning and Advanced Machine Learning
- Preparing to Train a Predictive Model
- Training Classification Models
Lesson 3: Web Scraping and Interactive Visualisations
- Scraping Web Page Data