This is an intensive hands-on training program for basic and advanced principles related to Big Data & Artificial Intelligence, which more specifically specializes on data science, machine learning and artificial intelligence for implementing and running relevant workloads on Azure, and has been carefully designed by ReGeneration and Microsoft, with Code.Hub being the training partner. The program lasts 180 hours in total, and includes two weeks of preparatory training (self-paced, 60 hours) using Microsoft’s online educational material, 60 hours of live online instructor-led training through Microsoft’s virtual classroom ecosystem and finally two weeks of post-training specialisation (self-paced, 60 hours) using Microsoft’s online educational material.
During the program, participants will “unlock” state-of-the-art techniques and new tools for planning and creating a suitable working environment for data science workloads on Azure, running data experiments, training, managing, and optimizing models, as well as deploying models into production. In addition, participants will be trained in core data concepts, acquiring the necessary skills to meet the particularly high demand for specialist scientists with hands-on knowledge in the above areas.
Why to participate
The program is designed for for STEM, Computer Science & Software Engineering early graduates and young professionals from various fields that, even though they usually have a strong academic background, they lack the necessary experience and confidence for the trending corporate environment that is data driven. The perfect candidates will be enthusiasts of the cutting-edge technologies of the 21st century enterprise and keen to explore and learn why it is widely accepted that data is considered the most important corporate asset. The program will enable participants to acquire a unique hands-on experience by using cutting edge technologies and tools for advanced analytics, data analysis, machine learning and artificial intelligence on Microsoft Azure.
Learning & Development Methodology & Structure
The overall training approach contains the engagement of the participants in three layers that require from them their active participation and corresponding commitment. More specifically the training structure is the following:
- Preparatory Training: 60 hours of Self-learning with online Microsoft material for initial upskilling and bringing all the participants to the same level.
- Academy: 60 hours of live online instructor-led training through Microsoft’s virtual classroom ecosystem
- Post-training development – Specialization: 60 hours of Self-learning with online Microsoft material
Duration & Schedule
*** The Schedule is subject to minor adjustments ***
The duration of the program is 180 hours in total, and each of the training phase is described below:
- The preparatory training duration will be 60 hours in 2 weeks
- 22 February – 6 March
- The extensive “coding for production” main program-academy will last 5 weeks and consists of 60 hours of lectures and hands-on exercise on real-life case studies and projects via virtual classroom environment and online collaboration platforms.
Weekdays – (18.00 – 21.15)
Weekends – (10.15 – 13.45)
- Main training ( 8 March – 9 April)
Week 1 – 8, 10, 13
Week 2 – 16, 18, 20
Week 3 – 22, 24, 27
Week 4 – 29, 30, 3 April
Week 5 – 5, 7
- Main training ( 8 March – 9 April)
- Project Presentations – Friday, 9 April
- The post-training development will follow the main training and will be 80 hours in 2 weeks.
- 12 – 24 April
Key Objectives – Curriculum
- Python syntax, data types, iteration and conditional constructs,
importing and creating libraries, data structures (lists, tuples
- I/O operations, file handling, functions
Data Analysis in Python
- Numpy Library: representing data in arrays, handling different shapes,
data manipulation, operations (broadcasting), I/O operations
- Pandas Library: Series, DataFrames, accessing data in DFs, filtering,
operations, transformations, handling missing values, I/O operations
- Microsoft account creation
- Familiarization with the basic entities of an Azure ML cloud solution
- Intro to Azure Machine Learning studio, Virtual Machines, Datasets, Notebooks, Pipelines, Experiments
Machine Learning in python
- Basic Machine Learning concepts
- Discussion in more advanced ML topics
- Popular ML algorithms (supervised, unsupervised)
- Scikit-learn Library: Preprocessing, classification, regression, clustering
Azure ML services
- Intro to Azure Auto-ML tool for quick model training and deployment
- Classification and regression paradigms with Azure Designer
- Use Azure Machine Learning SDK for python to programatically create
and interact with the Azure ML components.
- Hands-on paradigm to test the knowledge of the Azure ML workflow.
- Intro to Azure Artificial Intelligence Suite (azure cognitive services)
- Case studies: Sentiment Analysis with the Text Analytics API, Face
Recognition with the Computer Vision API
Conditions for participation:
The program is designed for STEM, Computer Science & Software Engineering early graduates and young professionals that, even though they usually have a strong academic background, they lack the necessary experience and confidence for the working environment.
Eligible for participation are early graduates of Greek or foreign higher education (ΑΕΙ / ΤΕΙ / College), on one of the following academic directions:
- Information Technology, Computer Science
- Data Science, Data Engineering, Data Analysis
- Computer Engineering
- Electrical and Electronic Engineering
- Digital Systems
- Web Development
Up to 29 years old, as the program is aimed at graduates at the beginning of their careers.
Zero or limited work experience:
Work experience from 0 to 3 years full time, upon completion of studies.
Active involvement in extracurricular activities (e.g. volunteering, sports, entrepreneurship, art and any other non-academic activity).
The purpose of the ReGeneration Academy on Cloud Tools & Technology powered by Microsoft is to prepare competitive and specialized software engineers and scientists with comprehensive knowledge and a strong theoretical and applied background in Azure Data Science technologies, and equip them with the necessary tools to make them competitive in the job market to claim positions in the areas of:
- Data Science
- Data Analysis
- Machine Learning
- Artificial Intelligence
- Data Visualization
- Big Data
- Data Engineering