Data Science Course  

Brochure

Intermediate Course Overview 

Elevate your data science skills with our intermediate-level training, designed for those who already have a foundational understanding of data analysis and Python. This course dives into advanced machine learning techniques, real-world data pipelines, and effective data storytelling. Through hands-on projects with authentic datasets, you’ll gain the practical skills to deliver data-driven solutions in professional environments. 

Course Objectives 

  • Deepen understanding of data science workflows and methodologies 
  • Equip learners with practical experience in building and evaluating machine learning models 
  • Strengthen data storytelling skills to communicate insights clearly and effectively 
  • Enable the development of end-to-end data science projects using industry tools 

What Will You Learn? 

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

  • Work with complex, unstructured, and multi-source datasets 
  • Build, train, and evaluate advanced machine learning models 
  • Perform effective feature engineering and dimensionality reduction 
  • Apply model interpretability techniques and performance optimization 
  • Use visualization and storytelling techniques to present data insights 

Design and execute a complete data science pipeline from data acquisition to model deployment 

Who Needs This Course? 

This course is designed for: 

  • Junior Data Analysts/Scientists ready to move beyond the basics 
  • Developers or Engineers looking to integrate data science and ML into their work 
  • Graduates in IT, Computer Science, Math, or Statistics aiming to specialize 
  • Researchers & Academics seeking hands-on tools for applied data science 
  • Professionals with basic Python & data analysis knowledge who want to grow their skillset 
  • Tech Enthusiasts eager to work on real-world data science projects 

Eligibility Requirements 

To succeed in this course, participants should: 

  • Have a basic understanding of Python programming (variables, loops, functions) 
  • Be familiar with core data analysis libraries such as Pandas and NumPy 
  • Understand basic statistics and data visualization concepts 
  • Be comfortable working with CSVs, Jupyter notebooks, and data files 
  • Have a strong willingness to learn through practice and project work