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Data science master’s applicants often wonder what key programming know-how is essential for succeeding in quantitative heavy coursework. While statistics and calculus set the math foundation–these coding languages prime practical application readiness boosting employability later.

Python’s simplicity allows rapid insights generation from manipulating datasets, visualizing trends and building machine learning models demanded by tech recruiters. R’s open-source power and packages repository fuels deeper statistical analysis, predictive models and data visualization sophistication expected in research. 

SQL querying skills extract and organize complex datasets for modeling. Shell scripting in Linux environments standardizes large scale cloud data ops. Familiarity using data visualization tools like Tableau which generate interactive reports are valued too. 

While degrees cover platform specific training–having base proficiency to logically structure, analyze, make inferences and communicate data-driven findings using these languages accelerates real-world learning application across projects.

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-Hithika Mekala