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 datascience@i3s.uniroma1.it 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]
- Introduction to Enel and its Business Lines
- Introduction to Data Science team and its relationships within the Organization
- Examples of DS use cases for each BLs
- How to start a new project
- Understanding Business needs / context
- Understanding available data
- Approach definition / Feasibility study
- Involved actors:
- Data scientist
- Business Translator (PM, PO)
- Development team: Data Engineer, Data Architect, DevOPS
Lesson 2 - Use Case definition
[TUITION – 1.5h]
- How to prioritize projects
- Trade-off between business value and feasibility
- Business roadmap
- Priority
- Monetization (ROI, efficiency, workload reduction)
- Data availability / Data quality
- Overview of BL data
[HANDS-ON – 0.5h]
- Students will be organized in groups. A specific Business line will be assigned to each group.
- Every group will identify one or more use case for the assigned Business Line
Lesson 3 - Business Engagement
[HANDS-ON - 1h]
- Presentation of works from each group and identification of the most important use case between the ones identified (using info from lesson2)
- The most interesting use case will be awarded
[TUITION – 1h]
- Interpretability & Business value
- Working with different business users: how to be efficient in communication with someone who don’t know anything about datascience
- Answer to the question: “Why do we have to trust you?”: how to gain trust from Business
- 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.
- Trade off between performance and interpretability of a model
- An efficient way to explain data science solutions
- Advantages to have a model easy to understand
Tech talk 1 - Productionizing Machine Learning
[TUITION – 2h]
- Delivery of the software
- Build
- Deploy
- Release
- Testing
- Advantages of a consolidated procedure
- Continuous delivery, continuous improvement
- Environments management
- Execution environment (provisioning)
- Development, quality and production environment
- Environments modelling
- Cloud computing
- Introduction
- Cloud architectures: SaaS, PaaS, IaaS
- Differences and flexibility in cloud architectures
- Cloud computing economics (fixed costs, resources management, strategies etc..)
- Scalability
- Vertical and horizontal
- Data and services scalability
- Asynchronous communication and consistency
- Stateful or stateless services
- Caching
Tech talk 2 - Microservices
[TUITION – 2h]
- System virtualization
- VM: characteristics
- Virtualization based on containers
- Difference with system virtualization
- What is a container
- Docker
- Containers orchestration (Kubernetes)
- RestAPI
Tech talk 3 - Version control
[TUITION – 0.5h]
- Introduction to Github
[HANDS-ON – 1.5h]
- Hands-on process simulation and how to use code versioning
Lesson 4 - Agile methodology
[TUITION – 1h] (possibly with the help of ATO)
- Advantages / Differences from traditional waterfall way of working
- Who are the figures involved in an Agile room: Scrum Master, PO, stakeholder e dev team
- Ceremonies according to Agile framework: Review, retrospective, planning, refinement, stand up
- Resources allocation in an agile room
- Infrastructure set up
- Estimation of developments time
[HANDS-ON – 1h]
- 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]
- 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]
- 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]
- 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 datascience@i3s.uniroma1.it in order to receive a link to the form
However, participation will be extended as far as possible to all interested students.
Topics:
- 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