In this project, you’ll assume the role of a data analyst working for Kaggle, a popular data science community platform. Kaggle conducted a survey of data science professionals regarding their career status, compensation, and programming skills. Your task is to analyze this survey data to uncover insights about the relationship between data science experience, skills, and career outcomes.
Throughout the project, you’ll apply essential Python and data analysis techniques, including data cleaning, aggregation, and visualization. By examining factors like years of experience and specific programming language knowledge, you’ll identify trends in how data science careers progress and what drives compensation. This hands-on analysis will deepen your understanding of the real-world data science landscape.
Objective: Analyze Kaggle’s data science survey results to determine the key skills and experience factors that influence data science career progression and compensation.
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build confidence with our in-depth projects, and show off your data skills.
Work with real data from day one with interactive lessons and hands-on exercises.
Share the evidence of your hard work with your network and potential employers.
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.
We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.
Practice using R to analyze movie ratings data, compare 2015 vs 2016 ratings, and apply sampling and distributions to investigate bias.
Practice data cleaning, analysis, and visualization in R to explore survey data and showcase your skills with R Notebooks.
Practice statistical analysis in Python to investigate movie rating bias and determine if Fandango inflated ratings.
Practice implementing an inventory system for a laptop store using Python classes, dictionaries, and binary search.
Practice using OOP, error handling, and randomness in Python to create an interactive gardening game simulator.
Practice building a K Nearest Neighbors classifier in Python to predict heart disease risk from patient data.
Practice using Python variables, lists, loops, conditionals, and file handling to build an interactive word-guessing game.
Practice using linear regression in R to predict condominium sale prices based on size and location in New York City.
Practice analyzing survey data in Python to uncover insights about data science careers and skills.
