Comprehensive knowledge of statistical methods, hypothesis testing, and experimental design.

Exposure to machine learning algorithms and frameworks like TensorFlow and scikit-learn for predictive modelling and pattern recognition.

Hands-on experience with data mining techniques, including data preprocessing, feature selection, and dimensionality reduction.

Application of data visualization tools and libraries such as Tableau and Matplotlib to effectively communicate analytical insights.

Real-world case studies and projects involving data-driven decision-making in various domains such as finance, healthcare, and marketing.

Student must have completed a bachelor’s degree in a relevant discipline.

The bachelor’s degree should be from a recognized university.