5 Free Tutorials on Building Data Science Projects from Scratch
Whether you’re just starting out or looking to sharpen your expertise, finding the right resources can make all the difference. Fortunately, there are numerous free data science tutorials available that not only teach the fundamentals but also guide you through building data science projects from scratch.
This article will introduce you to five standout tutorials, each designed to equip you with the knowledge and practical experience to confidently tackle real-world data science challenges.
1. Kaggle’s Titanic: Machine Learning from Disaster
Kaggle’s Titanic: Machine Learning from Disaster is a widely recognized project that serves as an excellent starting point for beginners in data science. The tutorial utilizes a historical dataset from the Titanic disaster, where you’ll be tasked with predicting passenger survival based on factors such as age, gender, and class. This project is highly practical, providing hands-on experience in essential data science tasks.
You’ll begin with data exploration, where you learn to handle missing data, outliers, and categorical variables. Next, the tutorial guides you through feature engineering, a step where you create new variables that might improve the predictive power of your model. This is followed by building machine learning models using Python libraries like pandas and scikit-learn.
You’ll experiment with different algorithms such as logistic regression and decision trees, learning how to tune your models for better accuracy.
What makes this tutorial stand out is its focus on both technical skills and the ability to derive insights from data. By the end, you’ll have developed a solid understanding of the end-to-end process of building a predictive model, from data cleaning to model evaluation. Additionally, you’ll gain experience in using Python, which is a critical tool for any aspiring data scientist.
The course is available free of charge on Kaggle’s website.
2. Coursera’s Python for Data Science and AI by IBM
The Python for Data Science and AI course, offered by IBM on Coursera, is a comprehensive resource that introduces Python programming in the context of data science.
This tutorial is ideal for beginners who want to learn Python while applying it to real-world data science tasks. The course covers a broad range of topics, from the basics of Python syntax and data structures to more advanced concepts like data analysis, visualization, and machine learning.
Throughout the course, you’ll work on various projects that help reinforce the concepts being taught. These include practical exercises in data manipulation with pandas, data visualization using Matplotlib and Seaborn, and even basic machine learning applications with Scikit-learn.
The hands-on approach ensures that by the end of the course, you’ll not only understand Python but also how to apply it effectively in data science projects.
The course also integrates AI concepts, giving you a glimpse into how Python can be used for machine learning and artificial intelligence tasks. This makes it a well-rounded introduction to both Python programming and its applications in the broader field of data science and AI.
The course is available free of charge on Coursera, although payment is required in order to get a certificate upon finishing it.
3. Google’s Machine Learning Crash Course
Google’s Machine Learning Crash Course is a well-structured, free resource designed to provide a solid foundation in machine learning. It’s particularly suited for those who already have some programming experience and are looking to delve into machine-learning concepts. This course is highly practical, offering interactive exercises that allow you to apply what you’ve learned directly in your browser.
The course covers a wide array of topics, starting with the basics of machine learning, such as supervised and unsupervised learning, and progressing to more complex subjects like neural networks and deep learning. One of the key strengths of this course is its emphasis on real-world applications. You’ll work with datasets and tools that are used in professional data science environments, giving you a feel for how machine learning is applied in the industry.
Another notable feature is the use of TensorFlow, Google’s open-source machine learning framework. The course provides hands-on tutorials where you’ll learn to build and train models using TensorFlow, making it an excellent introduction to this powerful tool. By the end of the course, you’ll have a comprehensive understanding of how to approach machine learning problems, from data preprocessing to model deployment.
If you’re interested, the course can be accessed here for free.
4. DataCamp’s Data Science Projects
DataCamp offers a variety of free projects that are ideal for those looking to gain practical experience in data science. These projects are particularly beneficial because they focus on applying theoretical knowledge to solve real-world problems, making them an excellent complement to more traditional coursework. DataCamp’s project-based learning approach allows you to work on actual datasets and apply various data science techniques in a guided environment.
The projects cover a range of topics, including data cleaning, exploratory data analysis, data visualization, and machine learning. Each project is designed to be completed in a few hours and includes step-by-step instructions, code templates, and hints to help you along the way. This makes them accessible even to beginners, while still providing enough depth to be valuable for more experienced learners.
One of the key advantages of DataCamp’s projects is the instant feedback provided by their platform. As you write and execute your code, the system checks your work and provides real-time feedback, which is incredibly helpful for reinforcing learning and correcting mistakes.
Additionally, these projects often simulate real-world scenarios, such as predicting house prices or analyzing customer churn, which helps build practical skills that are directly applicable in a professional setting.’
DataCamp’s courses can be found here — while some of them, particularly introductory courses, are free, most require a paid subscription.
5. FreeCodeCamp’s Python Data Science Course
freeCodeCamp offers a 12-hour Python Data Science course that takes you from complete beginner to being able to analyze real-world data. The course covers the essential tools every data scientist needs: Pandas, NumPy, and Matplotlib. You’ll get hands-on experience building projects that reinforce each concept, with a focus on step-by-step coding.
The tutorial begins with a one-hour overview of basic programming concepts, before diving into Python installation, using Jupyter Notebooks, and key Python data structures like lists, dictionaries, and functions.
It then moves into core data science topics like manipulating datasets with Pandas, performing mathematical computations with NumPy, and creating data visualizations with Matplotlib.
This hands-on approach is highly applicable to real-world scenarios such as analyzing financial data, extracting data from invoices, or analyzing trends — one project even includes building a COVID-19 trend analyzer app, which integrates all the skills covered in the course into a practical application.
This course is ideal for those who prefer a project-based, interactive approach to learning data science, and it’s available for free on the freeCodeCamp website.
Conclusion
These five free tutorials provide a strong foundation for anyone looking to build data science projects from scratch. Whether you’re interested in machine learning, Python programming, or practical data science applications, these resources offer hands-on experience and step-by-step guidance.
After going through a couple of tutorials and subsequent exercises, you’ll gain the skills and confidence needed to tackle real-world data challenges, setting you up for success in the rapidly growing field of data science.
