Data Analytics Trainer

About the Role

NIDADS is looking for a skilled and passionate Data Analytics & AI Trainer to deliver industry-focused training through interactive sessions, practical assignments, and real-world projects. The ideal candidate should have strong technical expertise in data analytics, programming, business intelligence, and machine learning, along with the ability to simplify complex concepts and mentor learners from diverse academic backgrounds.

Key Responsibilities

• Design and deliver structured training sessions in Data Analytics, Python, SQL, Advanced Excel, Power BI, Tableau, Statistics, and Machine Learning.
• Conduct hands-on labs, practical assignments, and industry-based projects.
• Develop training materials, assessments, and project-based learning activities aligned with the curriculum.
• Guide students in data cleaning, exploratory data analysis, predictive modeling, dashboard development, and data visualization.
• Evaluate student performance and provide constructive feedback to improve technical proficiency.
• Mentor students through capstone projects, portfolio development, and technical interview preparation.
• Collaborate with the academic team to enhance course delivery and maintain industry relevance.
• Stay updated with emerging analytics tools, AI applications, and industry trends.
• Participate in academic workshops, technical events, and student development initiatives.

Requirements & Tech Stack

• Advanced Excel: Pivot Tables, Power Query, advanced formulas, dashboards, and automation.
• SQL: Joins, subqueries, CTEs, window functions, and database management.
• Python: NumPy, Pandas, Matplotlib, Seaborn, and Plotly.
• Business Intelligence: Power BI, DAX, Tableau, data modeling, and interactive dashboards.
• Statistics: Descriptive statistics, hypothesis testing, regression, and exploratory data analysis.
• Machine Learning & AI: Supervised and unsupervised learning, predictive analytics, classification, clustering, and scikit-learn.
• Software Development: Programming fundamentals, coding best practices, version control, and modular design.

Qualifications

• Undergraduates can apply.
• Prior experience in Data Analytics, Data Science, Business Intelligence, or technical training.
• Experience designing practical assignments, assessments, and real-world analytical projects.
• Excellent communication, interpersonal, and mentoring skills.
• Passion for teaching and helping learners become industry-ready.