This course covers the basic exploratory techniques for analyzing data sets and summarizing their main characteristics, often with visual methods. Data scientists use exploratory data analysis (EDA) as the starting point to better understand the data before the formal modeling begins. EDA helps in getting some sense of the methodology that would be appropriate to analyze the data and sometimes also provides non-obvious insights that can be invaluable. In this course, we will use Python to do some EDA on a real data set. Broadly, the course covers:
Python is a general-purpose language which is designed to be simple to read and write. It is one of the handy tools in a data scientist’s arsenal that offers a slew of active data science libraries and a vibrant community.