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Decoding Data Science Job Postings to Improve Your Resume

prerequisites
intermediate Python • beginner scikit-learn • basics of pandas
skills learned
process data with pandas DataFrames • K-means clustering • information retrieval with tf-dif • text cluster visualization
Nate George
4 weeks · 4-6 hours per week · INTERMEDIATE

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In this liveProject, you’ll step into the life of a budding data scientist looking for their first job in the industry. There are thousands of potential roles being advertised online, but only a few that are a good match to your skill set. Your challenge is to use data science tools to automate the process of trawling through job listings, to save time as you optimize your resume, identify the most in-demand skills, and find jobs that are a good fit for you. To do this you’ll use Python to perform Natural Language Processing and text analysis on pre-scraped data from jobs posting websites.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Nathan George
Nate George started his career studying LEDs for his Ph.D. and working on solar cell manufacturing. He then leveraged his programming and mathematics experience to move to data science. Nate has been teaching and developing several data science and math courses at Regis University since 2017, mentors students at Udacity, and has developed a Python machine learning course at DataCamp. Nate's expertise includes data engineering (database technologies such as MongoDB and PostgreSQL and cloud technologies such as GCP and AWS), data science (Python, R, statistics), and machine learning.

prerequisites

The liveProject is for intermediate Python programmers who know basic data science techniques. To begin this liveProject, you should be familiar with the following topics:

TOOLS
  • Basics of Jupyter notebooks
  • Basics of pandas
  • Basics of scikit-learn
TECHNIQUES
  • Basics of K-means clustering
  • Basics of TF-IDF

you will learn

In this liveProject, you’ll learn how to use libraries in the Python data ecosystem to analyze text-based data. You’ll clean data derived from HTML files, use text similarity analysis to find the perfect job for you, and visualize your results using word clouds and display plots.

  • Parsing HTML web pages with the BeautifulSoup library
  • Storing and processing data with pandas DataFrames
  • Converting raw text to numeric features with the scikit-learn library
  • Measuring text similarity with a cosine distance function
  • Dimensionality reduction with singular value decomposition using scikit-learn
  • k-means clustering using scikit-learn
  • Creating word clouds with the WordCloud library for text cluster visualization

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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