Hello. In this lesson you will learn how to
explore and analyze data with Python. Unsurprisingly, the role of data
scientists primarily involves exploring and analyzing data. The results of an analysis might
form the basis of a report or a machine learning model. But it all begins with data with Python
being the most popular programming language for data scientists. After decades of open source development,
Python provides extensive functionality with powerful statistical and
numerical libraries. NumPy and Pandas simplify analyzing and
manipulating data. Matplotlib provides attractive
data visualizations. Scikit-learn offers simple and
effective predictive data analysis. TensorFlow and PyTorch supply machine
learning and deep learning capabilities. Usually a data analysis project is
designed to establish insights around a particular scenario or
to test a hypothesis. For example, suppose a university professor
collects data from their students. Including the number of lectures
attended the hours spent studying and the final grade achieved
on the end of term exam. The professor could analyze the data
to determine if there is a relationship between the amount of studying
a student undertakes and the final grade they achieve. The professor might use the data to test
a hypothesis that only students who study for a minimum number of hours can
expect to achieve a passing grade. In this lesson, you will learn common
data exploration and analysis tasks. And explore how to use Python packages,
like NumPy, Pandas, and Matplotlib to analyze data.