
Explore the ethics of web scraping, including intellectual property, copyright, terms of service, robots.txt, and legal gray areas, and learn to scrape responsibly by respecting site owners.
Install Anaconda to get Python, Jupyter Notebook, and data science packages, choosing Windows (64-bit) with Python 3, and complete the standard installer to launch the Jupyter dashboard.
Navigate the Jupyter dashboard to manage files and folders with checkboxes, rename or delete items, upload notebooks, and create ipynb notebooks with an interactive shell.
Explore how http requests fetch web content, comparing get and post methods, and understand status codes like 200 and 404 and json data in web APIs.
Build a python currency converter using the exchange rates API, collecting date, base and target currencies, and quantity, then fetch rates, compute the result, and display it with error handling.
Discover the five-step workflow of web scraping: inspect the page, fetch HTML with a GET request, choose a parser, parse, and build a BeautifulSoup object to navigate the parse tree.
Identify div tags with a role attribute to extract links labeled as main article or see also, then loop through them to build a list of urls using url join.
Learn to scrape multiple pages automatically by extracting main text from paragraph tags. Loop through each page in Python, handle errors, and build a url-to-text dictionary for clean, usable data.
Extract cast information from movie pages by locating the div class cast, retrieving names through links, and joining them into a single string, then store in a simple structure.
Learn how CSS selectors filter and select HTML elements for web scraping in Python, using tag, id, class, and attribute selectors, plus combining and nesting techniques.
Are you tired of manually copying and pasting values in a spreadsheet?
Do you want to learn how to obtain interesting, real-time and even rare information from the internet with a simple script?
Are you eager to acquire a valuable skill to stay ahead of the competition in this data-driven world?
If the answer is yes, then you have come to the right place at the right time!
Welcome to Web Scraping and API Fundamentals in Python!
The definitive course on data collection!
Web Scraping is a technique for obtaining information from web pages or other sources of data, such as APIs, through the use of intelligent automated programs. Web Scraping allows us to gather data from potentially hundreds or thousands of pages with a few lines of code.
From reporting to data science, automating extracting data from the web avoids repetitive work. For example, if you have worked in a serious organization, you certainly know that reporting is a recurring topic. There are daily, weekly, monthly, quarterly, and yearly reports. Whether they aim to organize the website data, transactional data, customer data, or even more easy-going information like the weather forecast – reports are indispensable in the current world. And while sometimes it is the intern’s job to take care of that, very few tasks are more cost-saving than the automation of reports.
When it comes to data science – more and more data comes from external sources, like webpages, downloadable files, and APIs. Knowing how to extract and structure that data quickly is an essential skill that will set you apart in the job market.
Yes, it is time to up your game and learn how you can automate the use of APIs and the extraction of useful info from websites.
In the first part of the course, we start with APIs. APIs are specifically designed to provide data to developers, so they are the first place to check when searching for data. We will learn about GET requests, POST requests and the JSON format.
These concepts are all explored through interesting examples and in a straight-to-the-point manner.
Sometimes, however, the information may not be available through the use of an API, but it is contained on a webpage. What can we do in this scenario? Visit the page and write down the data manually?
Please don’t ever do that!
We will learn how to leverage powerful libraries such as ‘Beautiful Soup’ and ‘requests HTML’ to scrape any website out there, no matter what combination of languages are used – HTML, JavaScript, and CSS.
Certainly, in order to scrape, you’ll need to know a thing or two about web development. That’s why we have also included an optional section that covers the basics of HTML. Consider that a bonus to all the knowledge you will acquire!
We will also explore several scraping projects. We will obtain and structure data about movies from a “Rotten Tomatoes” rank list, examining each step of the process in detail. This will help you develop a feel for what scraping is like in the real world.
We’ll also tackle how to scrape data from many webpages at once, an all-to-common need when it comes to data extraction.
And then it will be your turn to practice what you’ve learned with several projects we'll set out for you.
But there’s even more!
Web Scraping may not always go as planned (after all, that’s why you will be taking this course). Different websites are built in different ways and often our bots may be obstructed. Because of this, we will make an extra effort to explore common roadblocks that you may encounter while scraping and present you with ways to circumnavigate or deal with those problems. These include request headers and cookies, log-in systems and JavaScript generated content.
Don’t worry if you are familiar with few or none of these terms… We will start from the basics and build our way to proficiency. Moreover, we are firm believers that practice makes perfect, so this course is not so much on the theory side of things, as it adopts more of a hands-on approach. What’s more, it contains plenty of homework exercises, downloadable files and notebooks, as well as quiz questions and course notes.
We, the 365 Data Science Team are committed to providing only the highest quality content to you – our students. And while we love creating our content in-house, this time we’ve decided to team up with a true industry expert - Andrew Treadway. Andrew is a Senior Data Scientist for the New York Life Insurance Company. He holds a Master’s degree in Computer Science with Machine learning from the Georgia Institute of Technology and is an outstanding professional with more than 7 years of experience in data-related Python programming. He’s also the author of the ‘yahoo_fin’ package, widely used for scraping historical stock price data from Yahoo.
As with all of our courses, you have a 30-day money-back guarantee, if at some point you decide that the training isn’t the best fit for you. So… you’ve got nothing to lose – and everything to gain ?
So, what are you waiting for?
Click the ‘Buy now’ button and let’s start collecting data together!