Data Science Training in Lyon

This instructor-led, live training in Lyon (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

By the end of this training, participants will be able to:

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in Lyon (online or onsite) is aimed at intermediate-level data scientists and analysts who wish to use AWS Cloud9 for streamlined data science workflows.

By the end of this training, participants will be able to:

• Set up a data science environment in AWS Cloud9.
• Perform data analysis using Python, R, and Jupyter Notebook in Cloud9.
• Integrate AWS Cloud9 with AWS data services like S3, RDS, and Redshift.
• Utilize AWS Cloud9 for machine learning model development and deployment.
• Optimize cloud-based workflows for data analysis and processing.

This instructor-led live training, held in Lyon (online or onsite), is designed for intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led live training in Lyon (online or onsite) targets beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

This instructor-led, live training in Lyon (online or onsite) introduces the concept of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea". It guides participants through the creation of a sample data science project built on the Jupyter ecosystem.

By the end of this training, participants will be able to:

• Install and configure Jupyter, including the creation and integration of a team repository on Git.
• Leverage Jupyter features such as extensions, interactive widgets, multiuser mode, and more to facilitate project collaboration.
• Create, share, and organize Jupyter Notebooks with team members.
• Select from Scala, Python, or R to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface.

This instructor-led, live training in Lyon (online or onsite) is designed for data scientists and developers who wish to learn and build their careers in Data Science using Kaggle.

By the end of this training, participants will be able to:

• Learn about data science and machine learning.
• Explore data analytics.
• Learn about Kaggle and how it works.

Python has become immensely popular in the financial sector. Adopted by top investment banks and hedge funds, it is used to develop a diverse array of financial applications, from core trading systems to risk management platforms.

Through this instructor-led live training, participants will learn how to leverage Python to create practical solutions for various financial challenges.

By the conclusion of this training, participants will be able to:

• Grasp the fundamentals of Python programming
• Download, install, and configure the most effective development tools for building financial applications in Python
• Select and apply appropriate Python packages and programming techniques to organize, visualize, and analyze financial data from diverse sources (CSV, Excel, databases, web, etc.)
• Build applications that address issues such as asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quants

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training is designed to address key challenges faced by finance professionals. If you have a specific topic, tool, or technique you would like to include or expand upon, please contact us to arrange.

This course offers a practical exploration of Data Science and AI through Python, empowering professionals with the capabilities to analyze data, construct machine learning models, and implement AI-powered applications within business environments. It addresses CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning, deep learning with Tensorflow, natural language processing, big data analytics via Spark, and data-driven storytelling. This program is ideal for beginners aiming to obtain a Python data science certification and acquire career-ready analytics skills.

This instructor-led, live training in Lyon (online or onsite) is designed for data scientists who want to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a single platform.

By the end of this training, participants will be able to:

• Install and configure Anaconda components and libraries.
• Understand the core concepts, features, and benefits of Anaconda.
• Manage packages, environments, and channels using Anaconda Navigator.
• Use Conda, R, and Python packages for data science and machine learning.
• Get to know some practical use cases and techniques for managing multiple data environments.

This course is designed for marketing and sales professionals seeking to deepen their understanding of data science applications within their respective fields. It offers comprehensive coverage of various data science techniques applied to upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).\n

Distinctions Between Marketing and Sales - What sets sales apart from marketing?

In simple terms, sales focuses on targeting individuals or small groups, whereas marketing aims at a broader audience or the general public. Marketing involves research to identify customer needs, product development to create innovative offerings, and promotion through advertising to build consumer awareness. Essentially, marketing generates leads or prospects. Once a product reaches the market, it becomes the salesperson's role to persuade customers to make a purchase. Sales focuses on converting these leads into actual orders and purchases, operating with shorter-term goals, while marketing is concerned with long-term strategic objectives.

KNIME Analytics Platform stands out as a premier open-source solution for data-driven innovation. It empowers users to uncover hidden potential within their data, extract fresh insights, and forecast future trends. Boasting over 1000 modules, numerous pre-configured examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for both data scientists and business analysts.

This course on KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts to get acquainted with KNIME, enhance their proficiency, and learn how to generate clear, detailed reports using KNIME workflows.

This instructor-led live training, available either online or onsite, is designed for data professionals seeking to leverage KNIME to address complex business challenges.

The course targets participants who may not have programming knowledge but aim to utilize cutting-edge tools to implement analytics scenarios.

Upon completion of this training, participants will be capable of:

• Installing and configuring KNIME.
• Developing Data Science scenarios.
• Training, testing, and validating models.
• Implementing the end-to-end value chain for data science models.

Format of the Course also allows for the evaluation of participants.

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request customized training for this course or to learn more about the program, please contact us to arrange details.

This instructor-led, live training in Lyon (online or onsite) targets intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Lyon (online or onsite) is designed for data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms such as XGBoost and cuML.

By the end of this training, participants will be able to:

• Set up the necessary development environment to build data models with NVIDIA RAPIDS.
• Understand the features, components, and advantages of RAPIDS.
• Leverage GPUs to accelerate end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
• Learn how to perform machine learning tasks with XGBoost and cuML algorithms.
• Build data visualizations and execute graph analysis with cuXfilter and cuGraph.