Most real-world data isn’t clean, it’s messy, incomplete, and spread across sources like websites, APIs, and databases. In this course, you’ll learn how to collect that data, clean it, and prepare it for analysis using Python and SQL.
You’ll start by extracting data from webpages using tools like Pandas and Beautiful Soup, while also learning how to handle unstructured text and apply ethical scraping practices. Next, you’ll access real-time data through APIs, parse JSON files, and clean numerical data using techniques like normalization and binning. You’ll also learn how to manage authentication with API keys and store them securely. Finally, you’ll work with databases: Querying and joining tables using SQL, validating results, and understanding when to use SQL versus Python for different preprocessing tasks. By the end of the course, you’ll be able to turn raw, real-world data into reliable, analysis-ready inputs—a core skill for any data professional.
MN
Very broad and thorough course on data collection techniques, preprocessing, analysis, and visualization. Highly recommend.
NR
very precise. touches all relevant concepts with perfect examples. Good datasets and great evaluation.
CC
Sean Barnes is a great teacher and his courses are terrific. How I wish his courses were available when I first decided to learn data science!
Showing: 5 of 5
Very broad and thorough course on data collection techniques, preprocessing, analysis, and visualization. Highly recommend.
very precise. touches all relevant concepts with perfect examples. Good datasets and great evaluation.
Sean Barnes is a great teacher and his courses are terrific. How I wish his courses were available when I first decided to learn data science!
it was a good course
Module 3 places a disproportionate emphasis on statistics, which significantly disrupts the flow of the class. This dense focus makes the material feel tedious and often overwhelming, requiring significant mental stamina to navigate endless calculations without losing interest in the broader subject matter. Furthermore, given that Module 2 was also heavily focused on statistics, the cumulative intensity across these consecutive modules feels excessive. This sustained workload risks student burnout and detracts from a balanced learning experience.