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Applied Data Science with Python and Jupyter

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 benefit

What does a Python developer do?

Python developers write the code necessary to develop applications using Python's built-in statements, functions, and collection types. Python is also a very popular language for data analytics.

Will I learn how to Program in Python?

No. This course is intended for people who already know the basics of Python Programming. This course will teach you the basics of Data Analysis.

Select specific date to see price, venue and full details.

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

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