Beginning Data Science with Python and Jupyter

Course Snapshot 

• Duration: 2 days 
• Skill-level: Foundation-level Data Science with Python and Jupyter skills for Intermediate skilled team members. This is not a basic class. 
• Targeted Audience: This course is geared for those who wants to Get started with data science doesn’t have to be an uphill battle.   
• Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
• Delivery Format: This course is available for onsite private classroom presentation. 
• Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Get 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. We’ll finish up by showing you 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. 

Working in a hands-on learning environment, led by our Data Science with Python and Jupyter expert instructor, students will learn about and explore: 

• Get up and running with the Jupyter ecosystem and some example datasets 
• Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests 
• Discover how you can use web scraping to gather and parse your own bespoke datasets 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

Audience & Pre-Requisites 

This course is designed for for beginners who know a little Python and are looking for a quick, fast-paced introduction. 

Pre-Requisites:  Students should have familiar with  

• Basics of Python  
• Knowledge of Python is assumed. 

Course Agenda / Topics 

1. Jupyter Fundamentals 

• Jupyter Fundamentals 
• Lesson Objectives 
• Basic Functionality and Features 
• Our First Analysis – The Boston Housing Dataset 

2. Data Cleaning and Advanced Machine Learning 

• Data Cleaning and Advanced Machine Learning 
• Preparing to Train a Predictive Model 
• Training Classification Models 

3. Web Scraping and Interactive Visualizations 

• Web Scraping and Interactive Visualizations 
• Lesson Objectives 
• Scraping Web Page Data 
• Interactive Visualizations