Call for Application: Data Analytics & AI Training Programme

BRICS Astronomy is excited to announce an opportunity for participants to engage in an innovative training course that blends data science and machine learning (ML) with practical applications to Astronomy. This 10-week virtual program aims to empower individuals at all skill levels, from beginners to advanced users, to deepen their understanding of data science concepts and enhance their research capabilities. The training program will provide hands-on learning experiences across core areas of data science and machine learning, emphasising real-world applications in Astronomy. Throughout the course, participants will be guided in using these tools to manage, analyse, and interpret large datasets, while also exploring new possibilities for innovative research within the BRICS countries. 

Key Objectives

• Enhance Skills: Equip participants with the knowledge and practical tools that will allow them to effectively manage and analyse scientific data.
• Foster Innovation: Support the development of applications in machine learning for use in astronomy and related scientific fields.
• Build Community: Create a collaborative space for participants to share ideas, challenges, and solutions, strengthening the broader research network.

Who Should Apply?

This program is designed for early-career professionals, university students, and individuals working within the field of Astronomy who are looking to upskill themselves, particularly those from BRICS member countries. If you are eager to enhance your data science skills in fields like Astronomy or other STEM disciplines, this program is for you. This program covers key topics such as Python programming, data structures, control flow, data analysis, and handling astronomical data. You’ll also explore statistical and time series analysis, and machine learning basics for astronomy, and complete a capstone project to showcase your skills. 

What’s Required?

• Basic Knowledge: No prior experience is required for beginners. However, participants should be interested in Python programming, data science, and astronomy.
• Tools: Access to a working personal computer and internet access to participate in the virtual training fully.
• Commitment: The participant should commit to attending all virtual sessions during the 10-week course. The participants will also be required to work on the course material in their own time and participate in online discussions. 

Training Structure

The virtual program will span over 10 weeks, with a new topic covered each week. Each week’s session will include:

• Workshopping: Focused on core data science and programming skills.
• Hands-on Coding: Practical learning of data science tools and techniques.
• Interactive Q&A Discussions: Opportunities to ask questions and gain deeper insights into the training material.
• Resources: A week before each session, participants will be given the training material based on the week’s topic, including various problem sets and resources to work on in preparation for the week’s session.  
• Tutors: Tutors will be available weekly to support participants and help with problem-solving.

Topics covered: 

1. Introduction to Python 
2. Statistical Analysis
3. Data Analysis
4. Data Visualization with Matplotlib & Seaborn
5. Data representation and Colour theory
6. Astronomical Data Sources & Handling FITS Files and Visualisation
7. Time Series Analysis for Astronomical Data
8. Machine Learning Basics for Astronomy
9. eXplainable AI

Project Timeline

• Applications open: 05 March 2026
• Application Deadline: 31 March 2026
• Program Start Date: 04 May 2026
• Program End Date: 10 July 2026

Application Process

1. Apply Online: Complete the online application form.
2. Selection Criteria: Applicants will be selected based on interests, relevant experience, and alignment with the program’s objectives.

Selection Framework

• Alignment of Interests

• Criteria: Applicants should demonstrate a genuine interest in learning more about data science, machine learning (ML), AI, and their applications in astronomy or related scientific fields.
• Evaluation: Candidates will be assessed on how their personal or professional interests align with the objectives of the program, including how they plan to apply the skills gained in their field of study or work.

Willingness to Contribute Collaboratively

• Criteria: Applicants must show a commitment to working collaboratively and actively contributing to the program’s community.
• Evaluation: The ability to engage in group discussions, share ideas, and work effectively with others will be assessed through personal statements and past collaborative experiences (e.g., team projects, research work, or community involvement).

Availability and Commitment

• Criteria: Participants must be available to commit to all 8 weeks of the course, as well as engage with the program’s workshops, coding sessions, pre-session training material, and discussions.
• Evaluation: Candidates will be assessed based on their stated availability and ability to balance program participation with other responsibilities. Preference will be given to those who can demonstrate consistent time commitment.

Communication Tools

• Slack: For ongoing communication and community-building.
• Google Workspace: For document sharing and collaborative work.

Support & Resources

• Learning Materials: Participants will receive tutorials, coding notebooks, datasets, and other resources tailored to the specific needs of BRICS.
• Tutors: Tutors will be available weekly to support participants and help with problem-solving.

Tutors and Support

Experienced tutors will be available to assist participants throughout the learning process. They will offer guidance to ensure a smooth and productive experience. 

Evaluation

The program’s success will be evaluated through continuous feedback from both mentors and mentees, allowing for adjustments to ensure it meets the needs of participants and achieves the intended outcomes.