DataScientest

The Analytics Engineer specializes in the management, transformation, and modeling of data. This engineer delivers data sets that are understandable to all, while applying visualization best practices. It is essential for strategic data optimization, enabling optimal analysis and exploitation to solve complex problems or even develop predictive models.

The Cloud Engineer specializes in the management, transformation, and modeling of data along with the scalable deployment of cloud applications. This engineer delivers IT projects in a scalable manned, while ensuring security & cost-effectiveness. Thus, he/she is essential for strategic app building & deployment, enabling long-term cost-cuts, secure & scalable IT architecture and faster roll-out.

Intro to Python for Data Sciences, Data Visualization, Introduction to Machine Learning classification, PySpark, ML on text data.

System of management of Databases, introduction to distributed systems, handling of important data, fast data management, Big Data, and Artificial Intelligence.

Test Mining, Machine Learning, Computer Vision, NLP, ML on graphs, Deep Learning.

Automate
The DevOps Engineer is responsible for automating the deployment of a cloud infrastructure. This task refers to the processes and tools that reduce or eliminate manual efforts used to provision and manage cloud computing workloads and services.

Deploy
The task of continuous application deployment refers to a strategy in software deployment, where code changes to an application are released automatically into the production environment driven by a series of predefined tests.

Supervise
The DevOps Engineer is responsible for not only being in charge of these changes, but supervising the different teams connected to certain processes and tasks. Therefore, your communication skills are asked for, on top of your technical expertise!

At the end of the training program, participants will be able to
- Clean data, develop a strategy for data analysis and gain advanced knowledge in Python programming.
- Create predictive models for machine learning and perform automation of model updates.
- Understand the application of neural networks and implement algorithms for computer vision and natural language processing.
- Master the use of Artificial Intelligence in a production environment.
- Master the implementation of a classical machine learning algorithm and its application to text data
- Provide models in cloud-based solutions and create data visualizations to evaluate the results.

Advanced programming
Acquire the essential tools of advanced programming.

Master APIs
Be able to build, deploy and secure an API.

Orchestrate and automate
Learn how to manage, orchestrate and automate the necessary tasks within applications.

Manage and monitor
Ensure, monitor, operationalize and manage AI models used in production.