Data Analysis with Python: Zero to Pandas

The objective of the course project is to apply all the skills & techniques learned during the course to a real-world dataset. In this project, you will:
1. Find a real-world dataset of your choice online
2. Use Numpy & Pandas to parse, clean & analyze data
3. Use Matplotlib & Seaborn to create visualizations
4. Ask and answer interesting questions about the data
5. (Optional) Write a blog post to showcase your work

Learn how to perform exploratory data analysis with Python in this course project. Pick a real-world dataset, ask and answer questions, create visualizations, and document your work using Markdown cells. Submit your work on Jovian.ml and share your insights with the community.

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Explore jobs and salaries in Data Science and Machine Learning with this EDA project. Analyze factors like experience, remote work, and year-on-year salary trends.

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Learn how to analyze and visualize data science salaries using Python. Follow the step-by-step process of downloading and cleaning the dataset, dealing with categorical features, and conducting exploratory analysis. Get insights from the data with the help of matplotlib and seaborn.

data-science-salaries

Explore the characteristics of top-rated movies with an analysis of IMDb's Top 250 Movies dataset using Python libraries like Numpy, Pandas, Matplotlib, and Seaborn. Learn how to run the code with this beginner-friendly guide from "Data Analysis with Python: Zero to Pandas."

Explore shopping habits of Turkish customers with this dataset! Analyze age, gender, spending, payment method, and more with Python libraries like numpy and pandas. Get insights with visualizations.

Explore the exciting world of IPL cricket with this data analysis project! Discover insights on teams, players, and more using Python and Pandas. Follow the link to run the code and get started.

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"Explore nutrient values in different food categories with this EDA project using Python libraries like Pandas, Matplotlib, and Seaborn. Learn how to clean data and visualize insights. #nutrition #datascience #python"

nutrition-data-analysis

Explore hotel booking demand dataset with Python. Analyze time of year with highest demand, guest consumption habits & visualize with Matplotlib/Seaborn/Plotly. Download, clean & prepare data for analysis.

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Learn how to analyze video-games data using Python and Pandas. Download the dataset, clean and prepare it for analysis, and gain insights through exploratory analysis and visualization. Follow along with the Zero to Pandas course and discover new knowledge.

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Explore the "Layoffs Data" dataset with Pandas and visualize the distribution of layoff counts, top companies by layoff count, and total layoff count over time.

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Learn how to perform Exploratory Data Analysis (EDA) on Google Play Store Apps Dataset using Python's Pandas, Numpy, Seaborn, and more. Analyze and visualize the dataset with 16 columns to uncover trends and patterns.

eda-zero2pandas

Explore Spotify's Daily Top 200 charts in 35+1 countries for over 3 years with the help of Kaggle. From data preparation to visualization, this project covers it all.

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Analyzing the World Happiness Report from 2015 to 2019, this project explores the correlation between happiness and factors like GDP, generosity, and social support. The Cantril Ladder survey is used to evaluate the quality of life factors, and the dataset is downloaded from Kaggle. Explore the data and answer the question: Does our happiness depend on money or generosity?

zerotopandas-world-happiness-report-kevika

Perform an impact analysis on US health insurance coverage after the ACA was signed into law using Kaggle's 'Health Insurance Coverage' dataset. Analyze the data's layout and structure, clean and tidy the dataset, and conclude whether the ACA positively or negatively impacted the US healthcare system.

aca-health-insurance-coverage-analysis

Learn how students tackled a project and used code to create something amazing. Discover their process and see the end result. Exciting read ahead!

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Explore FPI and Nifty indices data using Pandas and Plotly. Understand FPI investments in India and sectoral indices behavior. Get the datasets from Kaggle and NSDL.

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Explore women's representation in tech with an analysis of HackerRank's Developer Survey 2018. Find correlations based on education, age, and more. Run the Jupyter notebook on Jovian.ml or locally using Conda. Download the dataset using opendatasets.

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Notebook Link

* Find a real-world dataset of your choice online
* Use Numpy & Pandas to parse, clean & analyze data
* Use Matplotlib & Seaborn to create visualizations
* Ask and answer interesting questions about the data

Course Project - Exploratory Data Analysis

Data Analysis with Python: Zero to Pandas