DATA SCIENCE, RESOURCES

10 Useful Data Science Resources for Beginners

January 02, 2021

Introduction: Data Science

Data Science is the field of study combining specific field expertise, programming skills, and knowledge of statistics and mathematics to draw meaningful understandings from the data. The practitioners of Data Science apply Machine Learning to the text, numbers, images, audio, video, and more to produce Artificial Intelligence systems in performing the tasks that generally require human intelligence. Many companies are realizing the importance of Data Science, Artificial Intelligence, Machine Learning, etc. regardless of their industry or size. To remain competitive in the age of Big Data companies need to develop and execute data science capabilities efficiently.

Data Science and its domains

Data Science is an experimental way of focusing on a forward-looking approach by analyzing the historical and current data to predict future outcomes to make informed decisions. The relevant topics covered in Data Science are Statistics, Linear Algebra, Programming, Machine Learning, etc.

The best books written by great authors for getting a good understanding and knowing how to implement them in real-time are listed below:

1. Introduction to Statistical Learning (ISLR)

Text Book - An Introduction to Statistical Learning

The book Introduction to Statistical Learning does a great job of adding a statistical viewpoint to your knowledge by covering some of the algorithms as Hands-On Machine Learning, with a more statistical bend. It also goes much deeper into the world of regression models as well as provides R code for practical application. Some of the topics included in this book are linear regression, resampling methods, classifications, etc. where the real-world examples are used to explain those methods.

The book was written to be an “accessible overview of the field of statistical learning” and gets the job done. To do so, the book focuses more on intuitive explanations as opposed to math.

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2. Mathematics for Machine Learning

Text Book - Mathematics for Machine learning

This book introduces all the relevant Mathematical concepts needed to understand and use Machine Learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics.

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3. Linear Algebra by Gilbert Strang

Text Book - Introduction to Linear Algebra

I believe that one of the key benefits of taking linear algebra is to get exposed to abstract mathematical thinking and proofs.

Link: http://math.mit.edu/~gs/linearalgebra/

Linear Algebra video lectures by Gilbert Strang:

4. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville

Text Book - Deep Learning

This book gives an introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

Link: https://www.deeplearningbook.org/

5. cs231 Computer Vision Video Lectures by Stanford University

The video lectures in the below link provide wide information in various fields like computer vision, image classification, neural networks, deep learning software, etc. highlighting the different tasks that can be performed by machines similar to the human level of intelligence.

5. cs231 Computer Vision Video Lectures by Stanford University

The video lectures in the below link provide wide information in various fields like computer vision, image classification, neural networks, deep learning software, etc. highlighting the different tasks that can be performed by machines similar to the human level of intelligence.

6. cs224N Natural Language Processing with Deep Learning Video Lectures by Christopher Manning from Stanford University

Professor Christopher Manning has given a detailed explanation for each of the concepts involved in Deep Learning that helps in understanding and implementing them in real-time.

7. Data Structures and Algorithms Using Python

Text Book - Data Structures and Algorithms using Python

The knowledge of Data Structures and Algorithms forms the base to identify programmers. While data structures help in the organization of data, algorithms help find solutions to the unending data analysis problems. The underlying mechanisms of many of Python’s built-in data structures and constructs are covered. So if you are still unaware of Data Structures and Algorithms in Python, here is a book that will help you understand and implement them.

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8. Computer Vision (Algorithms and Applications) by Richard Szeliski

Text Book - Computer Vision

This book explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching.

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9. Deep Learning in Natural Language Processing by Li Deng · Yang Liu

Text Book - Deep Learning in NLP

This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field.

10. Pattern Recognition and Machine Learning by Christopher M. Bishop

Text Book - Pattern Recognition and Machine Learning

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning.

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Conclusion

As we understood that Data Science is emerging and working across all the data-driven domains, everyone needs to improve their skills in analyzing the data using various concepts of Data Science that helps in making the work simpler and provides more time than using the traditional methods of analyzing the data. Students, educators, or anyone, in that case, need to begin their preparation and keep them in pace with tomorrow’s data-driven world.