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Meet the Fellow: Aahlad Puli

NYU Center for Data Science

Puli recently finished his PhD in Computer Science at NYU’s Courant Institute, advised by CDS Assistant Professor of Computer Science and Data Science Rajesh Ranganath. Standard algorithms aren’t designed for this scenario. Puli earned his MS in Computer Science from NYU in 2017.

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Unlocking data science 101: The essential elements of statistics, Python, models, and more

Data Science Dojo

They can be used to test hypotheses, estimate parameters, and make predictions. Machine learning is a field of computer science that uses statistical techniques to build models from data. Some of the most popular Python libraries for data science include: NumPy is a library for numerical computation.

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How Data Science and AI is Changing the Future

Pickl AI

What is Data Science and Artificial Intelligence? Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Bias in Algorithms Machine Learning models can inadvertently perpetuate biases present in training data.

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Roadmap to Become a Data Scientist: Do’s and Don’ts

Pickl AI

Key Takeaways: Data Science is a multidisciplinary field bridging statistics, mathematics, and computer science to extract insights from data. Understanding Data Science: Bridging the Gap Between Data and Insight It is the art of extracting meaningful insights from complex data sets. Practical experience is crucial.

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Data Science Course Eligibility: Your Gateway to a Lucrative Career

Pickl AI

Here are some of the most common backgrounds that prepare you well: Mathematics and Statistics These disciplines provide a rock-solid understanding of data analysis, probability theory, statistical modelling, and hypothesis testing – all essential tools for extracting meaning from data.

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A Guide to Choose the Best Data Science Bootcamp

Data Science Dojo

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Statistics : Fundamental statistical concepts and methods, including hypothesis testing, probability, and descriptive statistics.

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The innovators behind intelligent machines: A look at ML engineers

Dataconomy

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. They are the driving force behind the artificial intelligence revolution, creating new opportunities and possibilities that were once the stuff of science fiction. They build the future.

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