Data Scientist for Operational Excellence

Big Data: the centrality of data as a strategic factor for business development

• Data Driven Economy and Data Driven Decision: the role of data and information in business decisions
• Applications of Big Data Analysis
• Data Science and Data Analysis
• The role of the data scientist in the business organization

Process data using industrial statistics tools

• Introduction to statistics and elements of uncertainty management
• Models of statistical analysis
• Stratification and clustering of samples
• Numerical statistical summary: indicators of central tendency and dispersion
• Graphic statistical synthesis: the construction of effective reports (Histograms, Box-Plots & Whiskers, scatterplots)
• Analysis of sample behaviors: the main continuous and discrete probability distributions
• Process Capability Analysis

Inferential analysis tools & model fitting

• Verification of sample behaviors: analysis of outliers and anomalies
• Industrial use and applications of Hypothesis Testing
• Use and interpretation of ANOVA
• Use and interpretation of regression models
• Pattern analysis and synthesis of residue behavior (MAD, CV, ME,...)
• Hints at Control Cards for behavior verification

Programming methods

• Introduction to programming languages: Phyton™ and the Jupiter Notebook environment
• Typical basic Phyton™ program constructs and syntax from data types to functions
• Libraries: Numpy, for operations on vectors and matrices; matplotlib for visualization
• The Pandas Data Analysis library for data manipulation: importing, analyzing, extracting, sorting, grouping, and exporting data.

Data Collection

• Strategies for collecting, systematizing, and integrating heterogeneous data
• Main data models and formats: SQL vs NoSQL, CSV, Json, images, etc.
• Scraping tools (SW libraries and interactive graphical tools) and REST APIs

Big Data

• Introduction and basic concepts
• Data preparation: data cleaning, normalization, missing data and anomaly management
• Criticality, evolution, tools and platforms for Big Data management

Data Visualization

• Introduction to information visualization (infoview): purpose, fundamentals, patterns and antipatterns
• Tools for Data Visualization: mockups, wireframes & UI Prototyping
• Implementation of interactive dashboards with Power BI
• Models and tools for evaluating interfaces and dashboards

Machine Learning

• Introduction to Machine Learning and evaluative metrics for discrete and continuous problems
• Advantages and disadvantages of supervised and unsupervised systems, applicability of different methodologies to different contests
• Classification and regression
• Classifiers: k-Nearest Neighbor, Support Vector Machine, Random Forest
• Clustering (k-means) and Dimensionality Reduction (PCA).
• Introduction to Deep Learning, Convolutional Neural Networks, Recurrent Networks and Reinforcement Leraning