Building an Impressive Data Science Portfolio: Essential Datasets for Your Projects 📝 (With links)

As a data scientist, your portfolio is a powerful tool that showcases your skills, expertise, and problem-solving abilities to potential employers or collaborators. One way to enhance your portfolio is by working on projects that leverage diverse datasets. In this article, we will explore eight essential datasets that can help you build a strong data science portfolio. These datasets cover a wide range of applications and provide valuable insights into various domains. Let’s dive in!

Census data 👨‍👩‍👧‍👦:

Census data is a treasure trove of information, offering valuable insights into demographics, education, income, and more. Leveraging packages such as CenPy for US census data or accessing Statistics Canada’s website for Canadian census data, you can build projects ranging from simple regression analysis to uncovering correlations among variables. Exploring census data allows you to understand societal trends and make data-driven decisions.

Dataset link — CenPy documentation

Dataset link — Statistics Canada

NYC taxi trips data 🚕:

The New York City Taxi and Limo Commission releases monthly datasets containing information about taxi trips, including pick-up and drop-off times, locations, distances, and fares. This large-scale dataset presents an excellent opportunity for building projects like demand forecasting models. By analyzing this data, you can gain insights into transportation patterns and develop predictive models to optimize taxi services.

Dataset link — NYC Taxi Trip dataset

US Accidents dataset 💥:

The freely available US Accidents dataset on Kaggle provides detailed records of 2.8 million accidents, including weather conditions, accident location, and severity. Utilizing this dataset, you can develop models to predict traffic severity levels or investigate the impact of weather on accidents. Additionally, Kaggle hosts numerous analysis notebooks, offering a wealth of inspiration and guidance for your projects.

Dataset link — US Accidents (2016–2023)

OpenStreetMap (OSM) dataset 🗺️:

OpenStreetMap (OSM) is a rich geospatial database that can be accessed using Python packages like Osmnx. With OSM data, you can embark on projects such as building vehicle routing algorithms or visualizing the shortest path between different locations. Tools like Folium or GeoPandas enable you to create interactive maps, allowing for insightful data exploration and visualization.

Dataset link — OSM dataset

Satellite imagery data 🛰️:

The Rasterio Python package provides access to satellite imagery data from NASA’s Landsat 9 satellite. This dataset is invaluable for projects involving time series analysis, such as monitoring changes in green spaces over time by calculating vegetation indices. You can also explore remote sensing data to unlock further opportunities for analysis and insights.

Dataset link — Python Rasterio documentation

GTFS data 🚌:

GTFS (General Transit Feed Specification) is publicly available data that provides information about transit systems. Leveraging this dataset, you can develop isochrone maps or machine learning models to study areas lacking equitable access to transit systems. Exploring GTFS data enables you to contribute to improving transportation services and accessibility within cities.

Dataset link — GTFS dataset

COCO dataset 🖼️:

The COCO (Common Objects in Context) dataset is a vast collection of images with labeled objects in 80 different categories. It is a go-to resource for computer vision tasks, including object detection, segmentation, and captioning. By working with the COCO dataset, you can build sophisticated computer vision models and develop innovative solutions for a wide range of applications.

Dataset link — COCO dataset

ImageNet dataset 🏜️:

The ImageNet dataset is a massive collection of more than 14 million labeled images, spanning over 20,000 categories. While the entire dataset is extensive, a subset of over 2 million images is available on Kaggle for free. Leveraging this dataset, you can develop projects involving image classification and segmentaion.

Dataset link — ImageNet Object Localization Challenge

Remember, as you embark on your data science journey, constantly seek new datasets to expand your portfolio. Explore platforms like Kaggle, government databases, and open-source repositories to discover additional resources that align with your interests and project goals. Continuously honing your skills and staying updated with emerging datasets will keep you at the forefront of the data science landscape.

By incorporating these diverse datasets into your projects, you will not only enhance your portfolio but also develop a deeper understanding of the intricate connections between data, insights, and impactful decision-making. So, roll up your sleeves, dive into these datasets, and unleash the power of data science to transform industries and drive innovation.

Happy exploring and may your data science journey be filled with exciting discoveries and successful projects!