ADVANCED DATA SCIENCE TRAINING

Required qualifications: basic Data Science knowledge and practical experience
Number of training days: 5 days

Training objective:
To acquire advanced data analysis and machine learning skills for career advancement in Data Science.

Description of the training:
To learn advanced methods of data analytics such as deep learning and predictive modeling, as well as the practical application of big data management in real-world projects.

Detailed topics:

Day 1: Advanced data analysis techniques

  • Multivariate analysis and complex data processing methods
  • Multivariate data analysis and multivariate data analysis
  • Advanced statistical methods for categorisation and clustering
  • Data set dimension reduction and handling complex data types
  • Analysis and improvement of user experience based on dataDay 2: Deep learning and neural networks
  • Deep learning fundamentals and deeper understanding of machine learning models
  • Deep learning and deeper insights into machine learning
  • Recurrent neural networks (RNN) and their applications in time series analysis
  • Using deep learning tools and frameworks (e.g. TensorFlow, PyTorch)
  • Practical exercises and projects using deep learning techniquesDay 3: Managing large amounts of data and using databases
  • Concepts and methods for managing large data sets
  • NoSQL databases and the basics of distributed database management
  • Data pipelines and data integration techniques for large scale data
  • Data architectures and data management strategies for large scale data
  • Using Big Data tools and frameworks (e.g. Hadoop, Spark)Day 4: Predictive modelling and machine learning techniques
  • Predictive model design and evaluation in machine learning
  • Ensemble models and their effectiveness in predictive modelling
  • Model fine-tuning and performance optimization
  • Generalisation and transferability of models to different data sets
  • Practical projects and case studies using predictive modelling techniquesDay 5: Machine learning projects and career paths
  • Design, development and evaluation of machine learning projects
  • Career opportunities and professional development paths in Data Science
  • Future trends and directions in machine learning
  • Sharing professional experiences and networking opportunities

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