Training details

This intensive 3-day training course, "Advanced Data Science: Cutting-Edge Techniques and Practices," is designed to immerse seasoned data science professionals into the latest advancements and industry practices in this rapidly evolving field. The training delves deep into complex data analysis, advanced predictive modeling, and state-of-the-art machine learning algorithms.

As a sequel to the foundational Data Science Fundamentals program, this advanced training allows participants to explore high-level data science topics, including but not limited to, big data processing, neural networks, deep learning, and AI-driven analytics. The course combines theoretical knowledge with practical, hands-on exercises using real datasets, and the latest tools and technologies in data science.

Objective

• To deepen understanding of advanced data science concepts and methodologies.
• To gain hands-on experience with cutting-edge machine learning algorithms and AI techniques.
• To learn about the latest tools and technologies used in data analytics and big data processing.
• To develop skills in designing and implementing complex data science solutions.
• To enhance capabilities in predictive modeling, neural networks, and deep learning.

Target Audience

• Experienced Data Scientists and Analysts.
• Machine Learning Engineers.
• AI Professionals.
• Researchers in quantitative fields seeking advanced data science knowledge.
• IT professionals with a background in data science.

Prerequisites

• Completion of the Data Science Fundamentals training program is preferable.
• Solid foundational knowledge in data science and machine learning.
• Proficiency in programming languages commonly used in data science, such as Python or R.
• Laptop with at least 8Gb of memory, Anaconda Python distribution installed.

Pedagogical method

• A blend of lectures and hands-on workshops.

• Case studies and real-life examples to illustrate advanced concepts.

• Group projects and individual exercises for practical application.

• Interactive sessions to encourage discussion and idea exchange.

  • Proportion of presentations: 45%
  • Proportion of practical cases: 35%
  • Proportion of experience sharing: 10%

Evaluation and follow-up mode

• Continuous assessment through practical exercises, reflections and group projects.
• Feedback sessions to evaluate understanding and progress.
• Post-training homework and resources for further learning.
• Certificates of completion highlighting skills and knowledge gained.

Program

1. Day 1: Reinforcing Foundations and Exploring Advanced Concepts

   • Recap of Data Science Fundamentals
     • Modeling data science problems
     • Algorithm families: Supervised (classification/regression), Unsupervised
     • Classic algorithms review
     • Performance evaluation techniques
     • Overfitting and bias/variance trade-off
   • Ensemble Models
     • Introduction to ensemble methods
     • Techniques: Bagging, Boosting, Stacking
     • Effective use of ensemble models in case studies
     • Comparative analysis of ensemble approaches
   • Text Mining
     • Fundamentals of text processing and NLP
     • Techniques: Bag of words, Standard normalizations, Stemming, Lemmatization
     • Distances: Levenshtein, Hamming, Jaro-Winkler
     • Advanced concepts: Word2Vec

2. Day 2: Delving into Complex Features and Neural Networks

   • Advanced Feature Engineering
     • Advanced feature extraction and transformation techniques
     • Handling high-dimensional data and dimensionality reduction
     • Feature selection strategies
     • Feature creation from trees (Facebook Trick)
   • Neural Networks and Deep Learning
     • Neural network architecture
     • Deep learning frameworks and applications
     • Implementation of a neural network
     • CNNs and RNNs concepts and applications
   • Practical Use Case
     • Implementing a real-world neural network project
   • Semi-Supervised Learning
     • Combining supervised and unsupervised techniques
     • Applications and benefits
     • Algorithms and models used

3. Day 3: Consolidation and Hands-On Practice

   • Recap of Previous Days
     • Key concepts review from Days 1 and 2
     • Q&A session for clarity
   • Hands-On Sessions
     • Full day dedicated to practical application and projects
     • Implementing concepts on real datasets
     • Group and individual exercises