fr
en
Mastering data science: methods, tools and reinforcement learning
Your analyses need structured data and appropriate models. Connect exploration, modelling and evaluation to turn a business question into an analytical approach. Strengthen your ability to explain findings and the limitations of the methods used.
- Duration
- 5 days 35 hours
- Code
- DATA004FR Code
Presentation
Data science has become a fundamental pillar of innovation and strategic decision-making across all sectors. It enables businesses to unlock the hidden value of their data, predict trends, optimise processes and create new opportunities. Through advanced analytical methods and powerful tools, data science opens the way to a better understanding of complex phenomena and intelligent automation.
This intensive 5-day course helps you develop a solid command of data science fundamentals, covering the main data analysis methods and processing and modelling tools. You will explore data preprocessing and exploration, using machine learning algorithms for classification, regression and dimensionality reduction, while applying reinforcement learning to autonomous decision-making.
By the end of this comprehensive programme, you will master the methods, key tools (Python, Pandas, NumPy, etc.) and advanced techniques needed to design, implement and deploy practical projects, including those incorporating reinforcement learning. You will be ready to turn data into strategic decisions and lead innovative initiatives.
Objectives
By the end of this advanced data science course, you will be able to:
- Understand the foundations of data science and its key stages;
- Master essential data science tools such as Python, Pandas and NumPy to manipulate and analyse data;
- Prepare and explore data, including cleaning, transformation and preliminary visualisation;
- Use machine learning algorithms for classification, regression and dimensionality reduction tasks;
- Apply reinforcement learning concepts and algorithms to autonomous decision-making;
- Implement complete data science projects, including the development of reinforcement learning systems;
- Deploy data science models by saving them and exposing them through REST APIs.
Program
Module 1: Preparing and exploring data
- Fundamental data science principles (review).
- Data preparation and cleaning, including handling missing values, duplicates and inconsistencies.
- Data transformation (normalisation, standardisation and categorisation).
- Preliminary data visualisation with Matplotlib and Seaborn.
- Descriptive statistics and distribution visualisation.
- Detecting and identifying correlations.
- Dimensionality reduction methods (PCA).
Module 2: Applying machine learning (ML) and its concepts
- Fundamental principles of machine learning (review).
- Classification algorithms (logistic regression, K-nearest neighbours and decision trees).
- Regression algorithms (linear and polynomial regression).
- Model evaluation (confusion matrix, precision, recall and ROC curve).
- Basic reinforcement learning concepts.
- Differences between supervised, unsupervised and reinforcement learning.
- Reinforcement learning applications (games, robotics and recommendation systems).
- Practical implementation (developing an ML model).
Module 3: Mastering advanced modelling and reinforcement learning
- Clustering algorithms (K-means, DBSCAN and hierarchical clustering).
- Applications of clustering algorithms in data segment analysis.
- Evaluating clustering models (silhouette score and Rand index).
- Basic concepts of deep learning and neural networks.
- Advanced reinforcement learning (Q-learning and Deep Q Networks).
- Setting up a simple reinforcement learning problem (a simple game or robot).
- Applying a reinforcement learning algorithm to a practical case.
Module 4: Deploying data science models
- Saving a model with Joblib or Pickle.
- Deploying a model in a cloud environment or on a server.
- Creating REST APIs to expose the model and make remote predictions.
Audience
This course is intended for:
- Beginners or IT professionals changing careers who want essential foundations and a practical understanding of data science;
- Data analysts, engineers and researchers who want to deepen their skills in data processing, data modelling and reinforcement learning.
Prerequisites
This course requires the following prerequisites:
- Familiarity with computing environments and basic programming concepts;
- Intermediate mathematics and statistics knowledge is beneficial but not mandatory; the necessary concepts will be covered during the course.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Quiz / multiple-choice questions
- Practical exercises
Course highlights
- Expert trainers and practitioners: benefit from recognised trainers with in-depth knowledge of data analysis methods, essential data science tools and reinforcement learning.
- Interactive hands-on learning: master tools and techniques through concrete demonstrations and practical labs. Prepare to process data, use machine learning, apply reinforcement learning and deploy data science models.
- Operational skills development: develop practical expertise to design and implement data science projects, from data preprocessing to applying complex algorithms, including reinforcement learning.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.