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Where Can I Find Sample Data Science Projects to Practice Python?

Looking to start a data science career? Just as in any new field, you’ll need a lot of practice. Let’s explore where you can find data science projects to practice your newly acquired Python skills.

Organizations large and small all over the world use Python in their software development and data science projects. But even if you are very excited about a career in data science, it can seem very challenging to learn a new programming language. So you may wonder whether Python is worth learning and how difficult it is to learn a programming language like Python.

In fact, Python is very beginner-friendly; you can learn it pretty fast, especially with enough practice. In this article, I’ll guide you through several resources for practicing Python coding skills with real-world projects. But first, let’s start with some basic definitions.

What Is Data Science?

Data science combines programming, math, statistics, and business expertise to extract meaningful insights from data. Basically, data scientists are given business problems to be solved. They apply their understanding of industry and business processes, statistical and machine learning tools, and Python to solve the problems.

Data scientists work along with data engineers and data analysts to assist businesses with data-driven decisions. However, their roles are different:

  • Data engineers focus on preparing the infrastructure for the data. This data will later be used by data analysts and data scientists.
  • Data analysts usually work with structured data to spot trends and patterns that can be translated into actionable insights.
  • Data scientists are generally considered a more advanced version of a data analyst. They can work with both structured and unstructured data. They usually use more advanced data techniques to spot the current trends as well as make predictions about the future. Most data scientists are expected to be comfortable using advanced machine learning and Artificial Intelligence models.

Data science is a career of the future and Python is one of its key tools. Big tech companies, small startups, research organizations, and even academia choose Python because of its simplicity, rich ecosystem, large and supportive community, efficiency, and scalability.

Practice python

If you are new to programming but excited to learn coding with Python, I recommend trying our Python Basics mini-track. Its three interactive courses have 200+ coding challenges.

Once you are familiar with the basics, you can continue your learning journey with your first data science project.

How to Start Your First Data Science Project

For your first project, it’s a good idea to choose a topic that you’re interested in – it’s a great source of motivation. So think about what you’d find fun to work on: football statistics, climate change visualization, forecasting cryptocurrency prices, etc. You can find more data science project ideas here.

For example, let’s say you want to explore crime statistics in your city so you can choose the safest neighborhood to buy a house. You can consider lots of different factors, including the number of murders, robberies, car thefts, and other crimes per 1,000 people; the number of policemen per 1,000 people; average household income, etc. Here are just a few examples of what you can do using the data science toolkit:

  • Predict the number of different crimes based on the historical data (i.e. time series analysis).
  • Analyze which factors have the largest impact on the number of crimes.
  • Build a machine learning model to predict the number of crimes next year based on crime dynamics and other factors
  • Visualize the intensity of crimes on the city map.

Python can assist with all these tasks, including time series forecasting, exploratory data analysis, building machine learning models, visualizing data, and more. Data science and Python are really powerful together. However, you need to practice Python a lot to become an effective data scientist. Writing code for different scenarios and testing your skills with various projects and challenges is the shortest path to getting expertise in data science. So, let’s see where you can find real-world data science projects.

Where to Find Datasets and Sample Data Projects

There are numerous resources that offer real-world datasets to practice newly acquired Python and data science skills. Here are a few options:

  • LearnPython.com is a learning platform with many interactive Python courses, including Python Basics: Practice, which offers 15 coding exercises to practice basic programming skills. These exercises offer some problems that you are likely to encounter in real-world job assignments. However, this is not like your independent data science project, but rather a set of coding challenges. So, it is best for total newbies.
  • Kaggle is arguably the largest data science community. The platform has 50,000 public datasets, allowing you to practice all kinds of data science and Python skills. Some examples include a dataset to predict credit card defaults, sales information from the largest US retailers, World Bank data by region and nation, and data on all episodes of the TV show House. You can also grow your data science skills by participating in their regular competitions, which have difficulty levels from beginner to expert.
  • Data.gov provides access to the US government’s open data. This includes agriculture and climate data, resources on key energy topics, datasets for marine transportation, and more.
  • NASA Open Data Portal is a catalog of publicly available NASA datasets. It includes tens of thousands of datasets that cover a very wide range of topics, including national aeronautics and space data, physical oceanography, ocean biology data, earth resources observations, social-economic data, and more.
  • Earthdata can be a very useful source if you are interested in topics like atmosphere, land, ocean, cryosphere, and similar. Here, you’ll find NASA Earth observation data that was made available to a broad base of users.
  • DrivenData is a small-scale data competition website focusing on datasets and use cases from non-profit organizations.
  • Registry of Open Data on AWS includes over 300 datasets covering healthcare, space, climate change, and other topics.
  • UCI Machine Learning Repository is one of the oldest data sources on the Web. Even though many of the datasets on this platform are very old, they can still be good for practicing basic Python skills.
  • NASDAQ Data Link is a premier source of data for financial and economic projects. If you are interested in analyzing stock prices, trading activity, or interest rate dynamics, this should be your primary source of data.

It’s Time to Practice Python!

Hopefully, you’ll find your perfect dataset for your next data science project somewhere on the above list. However, if you feel you need to refresh and/or consolidate your Python skills  – or if you’re like me and prefer to learn Python with fun, easy-to-follow interactive online courses – you might want to start with one of the following learning tracks:

  • Python Basics is a mini-track perfect for people who just want to see if programming is for them. The track includes 229 coding challenges covering the basics of Python syntax, variables, and their purposes, if statements, loops, functions, and basic data structures (including lists, dictionaries, and sets). No prior programming or IT knowledge is required.
  • Python for Data Science is a 5-course learning track covering the essentials needed to start working in the field of data science. It includes hundreds of coding challenges covering basic calculations, simple data analyses, data visualizations, working with tabular and text data, and processing data from CSV, Excel, and JSON files. You can read more about this learning track here.
  • Learning Programming with Python is aimed at newcomers who want to understand foundational Python and then go beyond the basics and learn more advanced programming concepts. In addition to the Python basics described above, it covers data structures and built-in algorithms.

The constant (and long-term) demand for data scientists shows how popular this field is. Today’s companies and organizations prefer to make data-driven decisions, and they need data scientists for this. So, do your best to learn and practice Python for data science. Very soon, you'll have a successful and well-paid career as a data scientist.

Thanks for reading, and happy learning!