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The obvious Getting started
Data science is often confused with machine learning, but it is actually much more than that. It's about collecting, cleaning, analyzing, and visualizing data to find useful patterns that can help us make decisions. Machine learning is just one small part of this big picture. I started this series of fun projects to encourage active learning because honestly, you don't learn data science by looking at an endless perspective. You learn it by building.
In this article, I have selected five projects that cover the various stages of a typical data workflow, from basic data cleaning to data analysis, building models, and extracting them from real-world applications.
The obvious 1. The only data cleaner you need
This video is by Christine Jiang, who works as a data analyst, and she shares a really effective way of cleaning data that I think anyone working on projects will find helpful. When cleaning data, we often think “How clean is clean enough,” and Christine shows a clear way to deal with this using her five-step framework. You go through how you can find the flexibility of the incomparable constraints, measure the values, record everything, and make your data reliable without aiming for “perfect.” The examples he uses, such as correcting missing country codes or inconsistent product descriptions, are very relatable and emphasize their importance as tools. I found this to be a very practical practical guide for anyone trying to manage real world data effectively.
The obvious 2. Exploratory data analysis on Pandas
This video shows why having data is not enough and how looking at the numbers carefully can carefully reveal hidden patterns. The announcer walks through the data, summarizes the distribution, checks the remaining values and sellers, and sees the relationship between the columns using adulterous head and is born. I found it really effective because it doesn't just show the instructions, it explains why each step is important and how math can tell you things that aren't obvious at first. This is a great guide for anyone who wants to explore real world data and get meaningful insights before jumping into modeling.
The obvious 3. Data visualization using pandas and plotly
This video by Greg Kamadt, founder of independent data, shows that telling stories with your data is just as important as building models. You walk in the teaching hands that use it pandas with data integration and In old age With practical charts, they start with the basics of what makes visualization successful. You'll see how to load and extract data, choose the right chart types, and add formatting touches that make your charts clear and easy to understand. I really liked how useful it is, with tips on handling real-world issues like stores, axes, and small decisions that can improve learning. By the end, you'll know how to create effective, cost-effective charts that communicate effectively.
The obvious 4. Engineering aspects of machine learning engineering in Python
When your data is clean and understood, it's time to create better features. This course focuses on the “Element Engineering” section, where you modify and generate new data columns that can make your model better. The instructor explains techniques such as variable experience classification, lifting missing data, dimensionality reduction (principal component analysis (PCA)), and building correlation terms. I like that it also highlights what not to do like leaking data, overclocking features, and engineering features. This is a great resource for anyone who wants to go from raw data to building real-world Machine Learning models.
The obvious 5
Finally, the most satisfying part – bringing your model to life. In this tutorial, Yiannis Piptilides shows how to install a trained machine learning model using Support. You go through loading a saved model, setting up a clean interface with input boxes and buttons, and generating realistic car price forecasts. The video also covers the importance of the visual element in use Plotlyso you can see which installation is important. I liked how useful it is, with advice on keeping raw and clean data separate, managing dependencies, and running an application locally or on a host. It's a short course, but it's well done and gives you that “end to end” allowance that many beginners miss.
The obvious Wrapping up
These projects cover all the important stages of the data science workflow and show how an idea comes to life. Take your details and start experimenting. There is no better way to learn data science than by doing.
Kanwal Mehreen Is a machine learning engineer and technical writer with a strong interest in data science and the intersection of AI and medicine. Authored the eBook “Increasing Productivity with Chatgpt”. As a Google Event 2022 APAC host, she is a symbol of diversity and excellence in education. He has also been recognized as a teradata distinction in tech scholar, a mitacs Globalk research scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, who has created femcodes to empower women.
