Practitioner’s Guide to Data Science
In the early years of our data science career, we were bewildered by all the hype surrounding the field. There was – and still is – a lack of definition of many basic terminologies such as “big data,” “artificial intelligence,” and “data science.” How big is big data? What is data science? What is the difference between the sexy title “Data Scientist” and the traditional “Data Analyst?” The term data science stirs so many associations such as machine learning (ML), deep learning (DL), data mining, pattern recognition. All those struck us as confusing and vague as real-world data scientists!
However, we could always sense something tangible in data science applications, and it has been developing very fast. After applying data science for many years, we now have a much better idea about data science in general. This book is our endeavor to make data science a more concrete and legitimate field. In addition to the “hard” technical aspects, the book also covers soft skills and career development in data science.