Pfizer Bootcamp – Data Masterclass
Description
Τhe Pfizer Bootcamp – Data Masterclass commences!
Duration & Schedule
This specific program lasts 6 weeks and consists of 100 hours of lectures and hands-on exercise on real case studies and projects via virtual classroom environment* and online collaboration platforms.
Start Date: 26 September 2022
End Date: 3 November 2022
Detailed Schedule (*The Schedule is subject to adjustments)
| Introduction Day Monday 26, September |
Approximate (17.15-21.15) |
| Week 1 (Sep.) 26, 27, 29, 30, (Oct.) 1 |
Weekdays – (18.00 – 21.15) Weekends – (10.00 – 15.30) |
| Week 2 03, 04, 06, 07, 08 |
Weekdays – (18.00 – 21.15) Weekends – (10.00 – 15.30) |
| Week 3 10, 11, 13, 14 |
Weekdays – (18.00 – 21.15) |
| Week 4 17, 18, 20, 21, 22 |
Weekdays – (18.00 – 21.15) Weekends – (10.00 – 15.30) |
| Week 5 24, 25 |
Weekdays – (18.00 – 21.15) |
| Week 6 31, (Nov.) 01, 02, 03 |
Weekdays – (18.00 – 21.15) |
| Presentations Day Thursday 3, November |
Approximate (17.30-21.15) |
Key Objectives – Curriculum
The key learning objectives of this program can be summarized as follows:
| Module Description | Module Scope |
| Software Engineering | Academy Workflows & Processes Professionalism, SE and Data related roles and Industry reality Software Development Lifecycle & Models Managing the Agile Data Project & Scrum |
| DevOps & Cloud | Tooling & Collaboration principles Jira Version Control & GIT CI/CD, Jenkins, Kubernetes AWS |
| Programming & Python fundamentals | Python syntax, data types Iteration and conditional constructs Data structures: lists, dictionaries, sets, tuples Functions, packages File handling, store and access data Libraries: numpy, scipy, pandas |
| Databases & Business Intelligence | Databases & Database Management Systems Data Modeling, SQL (DDL, DML) The Big Data Technology Wave – The new Software Stack (briefly) OLAP Systems & Data Warehouses |
| Data Visualization | Extract-Transform-Load processes with Python Matplotlib Tableau |
| Artificial Intelligence & Machine Learning | Introduction to Artificial Intellige and Machine Learning Scikit-learn Library Preprocessing Supervised Learning Unsupervised Learning |
| Publishing Data to Web | Web applications Architecture & Development logic Web API Design Web Python Framework (Flask) (basics) HTML & CSS (basics) JavaScript (basics) Basics of a Front-end development framework – React |
| Dataiku Data Science Studio | ML in Dataiku WebApps in Dataiku |
| Project Development & Capstone Project Presentations | |
Qualifications
- BS in computer science, data science, and/or an engineering/quantitative field.
- 0 – 3 years of experience in a software related industry (working, educational and/or project).
- Hands-on skills in data engineering, machine learning and/or data visualization skills (through university or on the job).
- Ideally, experience with writing SQL queries on relational databases.
- We appreciate experience with coding in Python for data related aspects, such as: analysis, visualization, and/or machine learning.
- Strong English communication skills (written & verbal).
*Some lectures might require your physical presence
