ReGeneration Academy on Data Science powered by Papastratos
Description
ReGeneration Academy on Data Science powered by Papastratos is the new big collaboration of ReGeneration with Papastratos and is a pioneering training program on Data Science. It is an initiative that aims to transfer specialized, practical knowledge to young graduates, in a cutting-edge field, with great impact and growth in the labor market.
The Academy has a total duration of 100 hours over a period of 6 weeks (15 September 2021 – 22 October 2021) via virtual classroom environment and online collaboration platforms.
Young graduates from fields of Computer Science, Computer Engineering, Programming Mathematics, Statistics Engineering, Applied Mathematics Postgraduate programs related to data science have the opportunity to participate in an intensive series of live-online lectures and hands-on exercise on real-life case studies and projects in the field of Data Science.
The training program has been developed by ReGeneration and Papastratos, with Code.Hub as the training partner, and the academic guidance and support of Athens Tech College. The program combines basic and advanced Data Science principles with the necessary Business Intelligence aspects along with the participants’ familiarization with the corporate reality and conditions that rule the respective technology departments in the job market.
Participants will be trained in contemporary and state-of-the-art tools, methodologies and technologies with particular emphasis on Machine Learning and Artificial Intelligence aiming to acquire the necessary knowledge to meet the growing demand for data scientists and specialized experts that have the applied knowledge in the fields of Data (Data-related Industry Fields).
WHY PARTICIPATE?
series of studies by leading researchers and others bodies, such as the McKinsey Global Institute, emphasizes on the importance of “translating” data and analytics into data-driven decisions, as companies have realized that their most important asset is their data and want strategic and business decisions to be based on the application and utilization of data science techniques and methods.
Based on the aforementioned, during the program, participants will be immersed in up-to-date techniques and new tools on data collection and storage (databases, data warehouses), their transformation and utilization (python, SQL ), visualization and export of analytics (Power BI) as well as advanced thematics, algorithms and applications of data science (supervised & unsupervised algorithms, time series, forecasting, A / B Testing etc.), acquiring the required skills to meet the growing need of the modern business world for data scientists.
More specifically, data scientists are in demand; the field ranks among the best in the world, as highlighted in Forbes (based on a study by Glassdoor) while, according to relevant research, the use and importance of business intelligence in modern entrepreneurship continues to grow. In addition, a number of surveys and studies, such as GitHub‘s analysis, consider Python as a top programming language, with high demand today and in the future.
Hence, the training program has been formulated according to the logic “project-based industry simulation” and provides the appropriate theoretical background and hands-on experience that participants need to gain by analyzing and approaching advanced topics, industry patterns and best practices. Upon successful completion of the program, participants will be able to claim jobs, in the fields of Data Science, Business Intelligence, Data Integration/ ETL Development, Data Analysis, Data Engineering, Data Visualization, Analytics, Machine Learning, Artificial Intelligence, Big Data, and broadly in corresponding Consulting positions.
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.
Detailed Schedule (*The Schedule is subject to adjustments)
| Week 1 (Sep.) 15, 16, 17, 18 |
Weekdays – (17.15 – 21.15) Weekends – (10.15 – 13.45) |
| Week 2 20, 22, 24, 25 |
Weekdays – (18.00 – 21.15) Weekends – (10.15 – 13.45) |
| Week 3 27, 29, (Oct.) 1, 2 |
Weekdays – (18.00 – 21.15) Weekends – (10.15 – 13.45) |
| Week 4 4, 6, 8, 9, |
Weekdays – (18.00 – 21.15) Weekends – (10.15 – 13.45) |
| Week 5 11, 13, 15, 16 |
Weekdays – (18.00 – 21.15) Weekends – (10.15 – 17.45) |
| Week 6 18, 20, 21, 22 |
Weekdays – (18.00 – 21.15) |
| Presentations Day Friday 22, October |
Approximate (17.00-21.15) |
Key Objectives – Curriculum
This program will present, explore and adequately cover with extended hands-on sessions & real-life case studies the following areas:
| Module Description | Module Scope |
| Python Basics & R Overview | 1. Python syntax, data types, iteration and conditional constructs, importing and creating libraries, data structures (lists, tuples dictionaries) 2. File handling, functions and classes 3. R Basics |
| Data & Business Intelligence | 1. Data & Data Management Systems 2. Databases & SQL 3. Data Warehouses & Data Marts |
| Data Integration (with Python & SQL) | 1. Data Integration Architecture & ETL Principles 2. RDBMS Integration – connect, populate, create, manipulate, query 3. Extract-Transform-Load processes with Python |
| Data analysis in python | 1. Numpy Library: representing data in arrays, handling different shapes, data manipulation, operations (broadcasting), I/O 2. Pandas Library: Series, DataFrames, acceessing data in DFs, filtering, operations, transformations, handling missing values, I/O |
| Data Visualization | 1. Reporting Principles 2. Dashboards & Data Analytics (Power BI) 3. Visualizing data in python with matplotlib |
| Machine Learning & Artificial Intelligence | 1. Machine Learning basic and advanced topics (overview) 2. Popular algorithms (supervised, unsupervised) (overview) 3. Scikit-learn Library: Preprocessing, classification, regression, clustering, and dimensionality reduction, model selection 4. Timeseries & Forecasting 5. A/B Testing & Experimentation (overview) |
| Real Life DS Final Project | |
Conditions for participation:
Graduates of Greek or foreign schools of higher education (AEI / TEI / College), one of the following academic directions:
- Computer Science, Computer Engineering, Programming
- Mathematics, Statistics
- Engineering, Applied Mathematics
- Postgraduate programs related to data science
Additional Conditions:
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.
Extracurricular activities:
Active involvement in extracurricular activities (eg volunteering, sports, entrepreneurship, art and any other non-academic activity).
Purpose
ReGeneration Academy on Data Science Powered by Papastratos aims to prepare data scientists with a strong theoretical and applied background. Data Stores, Data Warehouses, SQL, Python, Business Intelligence, Data Visualization, Analytics, Machine Learning, Artificial Intelligence, and broader Data Technologies & Systems as well as familiarization with the use of Advanced Analytics platforms, are just some of the topics to be covered in the program, equipping participants with the necessary skills that will make them stand out in the competitive job market in data-related field.
- CERTIFICATION INSTITUTE: Athens Tech College
- DURATION: 100 Hours through the course of 5 Weeks
- PREMISES: Online virtual classroom environment and online collaboration platforms
