Dipanjan Sarkar is a Data Scientist at Intel, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems. He holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering from the International Institute of Information Technology, Bangalore. He is also an avid supporter of self-learning, especially Massive Open Online Courses and also holds a Data Science Specialization from Johns Hopkins University on Coursera. Dipanjan has been an analytics practitioner for several years now, specializing in statistical, predictive, and text analytics.

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You'll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Improved techniques and new methods around parsing and processing text are discussed as well. There is also a chapter dedicated to semantic analysis where you'll see how to build your own named entity recognition (NER) system from scratch.

COVID-19 has led to shifting priorities, and companies are re-assessing strategies across their business as resources are constrained. This has led to companies coming to terms with the reality of business value with data science. The mass layoffs in the technology industry, including many data scientists, have many saying the talent bubble has finally popped. The pandemic gave a good reason for organisations to decrease the compensations for data scientists, which was earlier soaring high due to the rising demand. It is true that the number of candidates had been increasing exponentially in data science over the past five years.