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

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 PhD 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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