Essential Mathematics for Data Science


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About this session

In this talk, we walk through the required mathematics to succeed in both theoretical and applied aspects of data science. Data science problems are modelled and solved using mathematical algorithms. For example, to predict store revenues using advertisement expenditure, we could model this problem as: Store Revenues = A + B*adExpenditure where 'A' and 'B' are model parameters. These parameters could be estimated by a mathematical method called Ordinary Least Squares(OLS). OLS could be understood with 12th standard mathematical concepts.

About the Speaker

Dr. D Narayana

PHD (Pierre & Marie Curie University, Paris)

Professor, Professor, Artificial Intelligence and Machine Learning, Great Learning, PhD (Pierre & Marie Curie University, Paris)


Dr. Narayana holds a PHD in Mathematics from Pierre and Marie Curie University, Paris, France.

Dr. Narayana has more than 11 years of industry experience and over 4 years academic research experience. Over these years, he has worked as a professor, trader, project manager, team lead, and developer.

His area of expertise includes Machine learning, Optimization, Financial engineering and High frequency & algorithmic trading. He is proficient in R Language, Hadoop, Python & Big Data Technologies. He has published over ten research articles in international journals and conferences. He has taught courses in Big Data technologies, guided student projects and mentored students at premier institutes.

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