Data analysis as we know it is the process taking the source data, refining it to get useful information, and then making useful predictions from it.

Python features numerous numerical and mathematical toolkits such as: Numpy, Scipy, Scikit learn and SciKit, all used for data analysis and machine learning. With the aid of all of these, Python has become the language of choice for data scientists for data analysis, visualization, and machine learning.

We will have a general look at data analysis and then then discuss the Web scraping tools and techniques in detail. We will show a rich collection of recipes that will come in handy when you are scraping a website using Python, addressing your usual and unusual problems while scraping websites by diving deep into the capabilities of Python’s web scraping tools such as Selenium, BeautifulSoup, and urllib2.

We will then discuss the visualization best practices. Effective visualization helps you get better insights from your data, and help you make better and more informed business decisions.

After completing this Learning Path, you will be well-equipped to extract data even from dynamic and complex websites by using Python web scraping tools, and get a better understanding of the data visualization concepts, how to apply them, and how you can overcome any challenge while implementing them.