Workload distribution
- Digital products: main dimensions (S1)
- Python Introduction (S1)
- Data structure + algorithms (S2)
- UX/UI and customer processes (S3)
- Introduction to Big Data (S3)
- Supervised learning (S4 + S5)
- Unsupervised learning (S6)
- Recommenders (S7)
- Reinforcement learning (S8)
- Digital product design in practice (S9+Presentation)
COURSE CONTRIBUTION TO PROGRAM
This course is designed to learn the basic concepts behind the design of a Digital Data Product realted to Big Data and Machine Learning.
Organizations uses data analysts to improve their processes, identify opportunities and trends, launch new products, draw conclusions, make predictions, and drive informed decision making.
In this module you will learn the most of the key data analytics topics, with labs that will allow you to accomplish common data analyst tasks with the best tools and resources.
At the end you will design an application with the concepts and tools acquired and you will share it with the rest of your colleagues in class.
You will learn how any Enterprise, from Amazon or Netflix to other type of companies as insurance, banks, games and other, use their data in order to create new digital products.
You will understand the algorithms that computers use to predict, recommend, classify or play without human intervention. At the end of the course, you will design a digital product yourself.
Course Learning Objectives
- Understand the main dimensions of the digital product design
- Understand the basic concepts of programming in Python.
- Do algorithms that start from very basic to intermediate level to read data and produce results.
- Use Python libraries to learn how to make predictions and recommendations.
- Elaborate a digital product to put in practice all the techniques learned. It will consist of a project to make predictions, recommendations, classification or a combination of all of them.
CONTENT
1. Planning 30% listen and understand technical concepts
40% exercises in class
30% design of a digital product with AI
|
Methodology
The teaching methodology of how artificial intelligence works pivots, towards models with new perspectives such:
- Connectivism: learning does not simply happen within an individual, but within and across the networks.
- Constructionism: involves students drawing their own conclusions through creative experimentation.
