Data scientist has been called “the sexiest job of the 21st century,” presumably by someone who has never visited a fire station. Nonetheless, data science is a hot and growing field, and it doesn’t take a great deal of sleuthing to find analysts breathlessly prognosticating that over the next 10 years, we’ll need billions and billions more Data Scientists than we currently have.There are lots and lots of data science libraries, frameworks, modules, and toolkits that efficiently implement the most common (as well as the least common) data science algorithms and techniques.If you become a data scientist, you will become intimately familiar with NumPy, with scikit-learn, with pandas, and with a panoply of other libraries.They are great for doing data science. But they are also a good way to start doing data science without actually understanding data science.
Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.
To handle data structures, such as Python lists, Numpy arrays, and Pandas DataFrames. Along the way, you'll learn about Python functions and control flow. Plus, you'll look at the world of data visualizations with Python and create your own stunning visualizations based on real data.The demand for skilled data science practitioners in industry, academia, and government is rapidly growing.It introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and Machine Learning.