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Applied Data Analytics with AI - Introduction 
Cost 615.12$
Age from 16 years
Available place FULL
Duration 30 hours


Online registration is closed. Please add your name to the Waiting list so that you will be contacted when the course is next available.


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Applied Data Analytics with AI - Introduction
(133027)
Activity confirmed
From Feb 20th to Apr 23rd, 2024
Tuesday 18:30 to 21:30, room VIR-Non-CR Non-Credit (Online)
Group number W24-133027-1


Requirements: No programming is required for this course, required software and tools will be provided.

Equipment Required:
  • Windows/MAC Laptop with at least 4 GB RAM and an internet connection of at least 25 Mbps.
  • Headset with a microphone and webcam.
  • Ability to communicate orally in English.
  • Ability to take a college/university-level course (undergraduate studies or equivalent work experience is ideal).
Objectives:

This hands-on course is intended for anybody wanting to learn how to turn data into actionable insights. Students will learn the principles of data exploration, cleaning, modelling and deployment using statistics and machine learning algorithms to solve real business problems.

In the first part of the class, the instructor will deliver a live theoretical and practical demonstration followed by questions from students; in the second part of the class, students will work on a specific assignment. A final project presentation might be required.

Topics Covered:
  • Intro to the Data Analytics Process (1 hr).
  • Intro to Data Types and Data Management in Python (4 hrs).
  • Visual Data Exploration & Statistical Data Analysis in Python (5 hrs).
  • Intro to Data Modelling (1 hr).
  • Classification Models in Python (5 hrs).
  • Regression Models in Python (5 hrs).
  • Performing Clustering in Python (4 hrs).
  • Group or Individual Project Coaching and Presentation (5 hrs).
By the end of this course, students will be able to:
  • Understand the data analytics process from setting up objectives to organizational deployment.
  • Explore data from several sources, including the web, excel and flat files into Python. Build and interpret standard statistical graphs including histograms, bar plots, scatterplots, line charts and boxplots.
  • Apply machine learning algorithms to regression and classification data models. Communicate key findings to a non-technical audience.



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