DATA ANALYTICS TRAINING

Data Analytics Training is an innovative programme that integrates data science and machine learning (ML) with practical applications in Astronomy. This 8-week virtual course is designed to support participants at all skill levels, from beginners to advanced users, by strengthening their understanding of data science concepts and enhancing their research capabilities.

The programme offers hands-on learning across core areas of data science and machine learning, with a strong emphasis on real-world astronomical applications. Participants will be guided in using modern tools and techniques to manage, analyse, and interpret large datasets, while exploring opportunities for innovative research within the BRICS countries and IDIA.

Key Objectives:

1. 1. 1.  Enhance Skills: Equip participants with the knowledge and practical tools needed to effectively manage and analyse scientific data.
   2. 2. Foster Innovation: Encourage the development and application of machine learning techniques in astronomy and related scientific fields.
   3. 3. Build Community: Create a collaborative environment where participants can share ideas, challenges, and solutions, strengthening the broader research network.
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The programme is designed for university students, professionals, and aspiring analysts from BRICS countries who wish to excel in an increasingly data-driven world. It delivers a comprehensive curriculum covering foundational Python programming, statistics, and data analysis, through to advanced machine learning models and data visualisation techniques. The overall goal is to empower participants to transform raw data into meaningful insights that support scientific discovery and informed decision-making.

Topics Covered:

1. 1. 1. Introduction to Python (installation, variables, control flow, functions, NumPy)
   2. 2. Data analysis with Pandas, statistical methods, and hypothesis testing
   3. 3. Data visualisation with Matplotlib and Seaborn, including colour theory and astronomical imaging
   4. 4. Astronomical data sources and handling FITS files (Astropy, Astroquery)
   5. 5. Time-series analysis for astronomical data (Lightkurve)
   6. 6. Machine learning fundamentals for astronomy (clustering, classification, and regression using Scikit-learn)
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The training is delivered by a diverse team of experienced instructors from leading institutions across the BRICS countries. Their combined academic expertise and real-world experience ensure a practical and engaging learning environment. Participants will work on hands-on projects, collaborate with peers, and receive personalised mentorship to support their professional development in the dynamic field of data analytics.