Working in a hands-on learning environment, led by our Data Science expert instructor, students will learn about and explore: Parsing webpages with the BeautifulSoup library.Storing and processing data with pandas DataFrames.Converting raw text to numeric features (TF-IDF vectors) with the sklearn (scikit-learn) library.Measuring text similarity with a cosine distance function from the sklearn library.Dimensionality reduction with singular value decomposition (SVD) using sklearn.K-means clustering using sklearn.Creating word clouds with the WordCloud library for text cluster visualization.
- Our first step is to take the raw HTML job postings and extract relevant information from them, such as the skill requirements for each job. 00:00:00
- Next, we will find the jobs that are most similar to our resume using cosine similarity. 00:00:00
- After that, we’ll use the most similar job postings to analyze what type of skills are typically asked for by clustering the skill requirements from the job postings. 00:00:00