As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas. Refine a question of interest to one that can be studied with data Pursue data collection that may involve text processing, web scraping, etc. Glean valuable insights about data through data cleaning, exploration, and visualization Learn how to use modeling to describe the data Generalize findings beyond the data

A first in Malaysia, this book investigates interculturalism from the point of view of a dance practitioner-academician whose interest in pursuing expressions in a multicultural country stems from the experience of living in a society that has assented to the duality of the prescribed national cultural policy versus real artistic practices. This engaged writing opens new trajectories in intercultural studies within the domains of contemporary dance in Malaysia, and is a must for dance and cultural studies readers. Mohd Anis Md Nor, Professor of Ethnochoreology and Ethnomusicology, Cultural Center, University of Malaya