Training camps Academic Year 2018-2019


Training camps Year 2018-2019

  • SAS (July)
  • ENEL (September) Registration for training camps is over. 30 students have been notified about their enrolment.

Training Camp ENEL   ** Data Science for Business **

Priority will be given to the students enrolled in academic year 2018-2019 (§)

(§) We remind that Data Science training camps are regularly organized on behalf of first-year students. They receive an email with information and updates about the content and schedule and a link to the on-line registration form. If you are a student enrolled in academic year prior to 2018/2019 and have never attended any training camp, please, send an email to  in order to receive a link to the form. Participation will be extended to all interested student, but first-year students have priority.

1 - Introduction (September 12th)
2 - Use Case definition (September 12th)
3 - Business Engagement (September 13th)
     Tech talk 1 - Productionizing Machine Learning (September 18th)
     Tech talk 2 - Microservices (September 19th)
     Tech talk 3 - Version control (October 3rd) (*)
4 - Agile methodology (September 26th)
5 - Data visualization and storytelling: part 1 (September 27th)
6 - Data visualization and storytelling: part 2 (September 27th)
7 - Final presentation (October 4th) (*)



Lesson 1 - Introduction

[TUITION – 2h]

  1. Introduction to Enel and its Business Lines
  2. Introduction to Data Science team and its relationships within the Organization
  3. Examples of DS use cases for each BLs
  4. How to start a new project
    1. Understanding Business needs / context
    2. Understanding available data
    3. Approach definition / Feasibility study
    4. Involved actors:
      1. Data scientist
      2. Business Translator (PM, PO)
      3. Development team: Data Engineer, Data Architect, DevOPS

Lesson 2 - Use Case definition

[TUITION – 1.5h]

  1. How to prioritize projects
    1. Trade-off between business value and feasibility
    2. Business roadmap
      1. Priority
      2. Monetization (ROI, efficiency, workload reduction)
    3. Data availability / Data quality
  2. Overview of BL data

[HANDS-ON – 0.5h]

  1. Students will be organized in groups. A specific Business line will be assigned to each group.
  2. Every group will identify one or more use case for the assigned Business Line

Lesson 3 - Business Engagement

[HANDS-ON - 1h]

  1. Presentation of works from each group and identification of the most important use case  between the ones identified (using info from lesson2)
  2. The most interesting use case will be awarded

[TUITION – 1h]

  1. Interpretability & Business value
    1. Working with different business users: how to be efficient in communication with someone who don’t know anything about datascience
    2. Answer to the question: “Why do we have to trust you?”: how to gain trust from Business
    3. How to assess a model value (not only precision, recall, RMSE but also KPIs interesting for businessnon. How to evaluate a model in production environment.
    4. Trade off between performance and interpretability of a model
    5. An efficient way to explain data science solutions
    6. Advantages to have a model easy to understand

Tech talk 1 - Productionizing Machine Learning

[TUITION – 2h]

  1. Delivery of the software
    1. Build 
    2. Deploy 
    3. Release 
    4. Testing 
    5. Advantages of a consolidated procedure 
    6. Continuous delivery, continuous improvement
  2. Environments management
    1. Execution environment (provisioning) 
    2. Development, quality and production environment
    3. Environments modelling
  3. Cloud computing
    1. Introduction
    2. Cloud architectures: SaaS, PaaS, IaaS
    3. Differences and flexibility in cloud architectures 
    4. Cloud computing economics (fixed costs, resources management, strategies etc..) 
  4. Scalability
    1. Vertical and horizontal
    2. Data and services scalability
    3. Asynchronous communication and consistency  
    4. Stateful or stateless services 
    5. Caching 

Tech talk 2 - Microservices 

[TUITION – 2h]

  1. System virtualization
    1. VM: characteristics 
  2. Virtualization based on containers
    1. Difference with system virtualization
    2. What is a container
    3. Docker 
    4. Containers orchestration (Kubernetes) 
  3. RestAPI

Tech talk 3 - Version control

[TUITION – 0.5h]

  1. Introduction to Github

[HANDS-ON – 1.5h]

  1. Hands-on process simulation and how to use code versioning 

Lesson 4 - Agile methodology

[TUITION – 1h] (possibly with the help of ATO)

  1. Advantages / Differences from traditional waterfall way of working
  2. Who are the figures involved in an Agile room: Scrum Master, PO, stakeholder e dev team
  3. Ceremonies according to Agile framework: Review, retrospective, planning, refinement, stand up
  4. Resources allocation in an agile room
  5. Infrastructure set up
  6. Estimation of developments time

[HANDS-ON – 1h]

  1. Using groups created in the previous lessons, define objectives, epics, stories and backlog for the use case chosen as the most interesting in lesson 3

Lesson 5 - Data visualization and storytelling: part 1

[TUITION – 2h]

  1. How to present results, story-telling, how to do efficient and effective slides, how to do effective plots

Lesson 6 - Data visualization and storytelling: part 2

[HANDS-ON – 2h]

  1. Using the work done in previous lessons prepare a presentation for 2 different targets: business people and technical people.

Lesson 7 – Final presentation

[HANDS-ON – 2h]

  1. Exposition of presentations prepared during lesson 6

(*) These dates are tentative and subject to change


Training camp SAS (8, 9, 10 July 2019)

Day 1 :: Monday 8 July :: Lab 17 - via Tiburtina, 205 (09:30-13:00 + lunch break + 14:00-17:30)

Day 2 :: Tuesday 9 July :: Lab 17 - via Tiburtina, 205 (09:30-13:00 + lunch break + 14:00-17:30)

Day 3 :: Wednesday 10 July :: multimedia room - Aula IX (CU002 - Giurisprudenza, 1st floor) [morning] (09:30-13:00) + lunch break

                                        + Lab 17 - via Tiburtina, 205 [afternoon] (14:00-17:30)

Priority will be given to the sudents enrolled in academic year 2018-2019. 

For students enrolled previous in academic years, who have never attended any training camp: please, send an email to  in order to receive a link to the form
However, participation will be extended as far as possible to all interested students. 


  • Introduction to SAS Viya, Data Preparation, and Exploration Machine learning in business decision making, essentials of supervised prediction. Data Preparation, exploration, feature extraction and selection, transformations, clustering
  • Decision Tree, Neural Network, Vector Machine, and more Tree-structure models, recursive partitioning, pruning, ensemble of trees. Optimizing complexity, interpreting models and transformations
  • Model Assessment and Scoring Model assessment and comparison, model deployment