From Big Data to AI via Data Science [slides]

• Data-driven Decision Making for Data-driven Organizations
• What are Big Data, Data Science and AI?
• How do Big Data, Data Science and AI support Data-driven Decision Making?
• Who is using Big Data, Data Science and AI?

Key concepts

• • Big Data
    • Vertical vs. horizontal scalability [slides][drawing]
    • Scaling storage horizontally with K-V pairs [slides][drawings]
    • Scaling processing horizontally with MapReduce [slides][drawing]
    • Achieving usability implementing high level interfaces on horizontally scalable solutions for: data access (e.g., Spark SQL), machine learning (e.g., SparkML), graph processing (e.g., Spark GraphX) and stream processing (e.g., Spark Streaming)
  • Solving problems the AI way [drawings]
    • programming vs. reasoning
    • Gradient Descent and error minimization
  • Understanding data visualization
    • the “grammar of graphics”: data and aesthetic mappings, geometric objects, scales, facet specification, statistical transformations, and
      the coordinate system. [article]
    • exploring the “diamonds” dataset in Tableau
    • visualization in pandas

The main Machine Learning algorithms

• • • Venn Digram of ML algorithms [drawing]
    • Predicting house prices using Linear Regression and Gradient Descent [drawings]
    • Detecting spam emails using Naive Bayes Algorithm [drawings]
    • Recommending Apps based on Decision Trees [drawings][animation]
    • Finding the best location for a shop based on K-means clustering or Hierarchical Clustering [drawings]
    • Deciding to accept students at a university based on Logistic Regression and Gradient Descent with Log-loss function [drawings]
    • When a line is not enough … or the kernel trick of Support Vector Machines [drawings]

Introduction to Deep Learning

• • • Hands-on the Linear Perceptron and the linear classification problems it can solve
    • Non-Linearity + Perceptron = Universal Approximation
    • Hands-on Deep Learning and the non-linear classification problems it can solve

Project work part 1

• • • grouping and idea generation via thinking hats

A case of Web Analytics

• • • Scraping Amazon’s product reviews
    • Training a sentiment analysis model of Amazon’s product reviews using text analytics, and logistic regression
    • Testing the sentiment analysis model
    • Classify microposts about Amazon’s product as positive or negative using the sentiment analysis trained model
    • Discussion on measuring classification errors
    • MATERIAL: [drawing][nootbook for Databricks]

A case of Predictive Analytics

• • • The bike sharing dataset
    • Building three predictive modeling using Decision Trees, Random Forests and Gradient Boosted Trees
    • The role of K-fold cross validation and hyper-parameter exploration to compare the results
    • Discussion on bias, variance, error and model complexity
    • Discussion on compatibility between Big Data, K-fold cross validation and Random Forests

Project work part 2

• • • identifying solutions based on ML and quick feedbacks

Convolutional Neural Networks (CNN)

• • • The problem of converting an image of a handwritten character into its correct classification (i.e., which character is it?)
    • The MNIST Digits Dataset
    • Understanding Convolutional Neural Networks
    • Demo: how Keras + Tensorflow can classify correctly the MNIST Digits Dataset
    • Demo: how Keras + Tensorflow can understand images using Inception V3 model

Project work part 3

• • • identifying solutions based on Deep Learning and quick feedbacks

Two personal concrete experiences

• • • Predicting the aluminium price using Deep Learning (RNN)
    • Listening to the pulse of our cities

Project work part 4

• • Choosing between alternatives