Hi there, Cornellius here! 👋
Welcome to Non-Brand Data!
Data Science is a field that keeps evolving. Over the years, we have seen many changes in how we analyze data, develop machine learning models, and use different tools to support our work.
With the development of AI, there are even more things we can explore.
For example, AI can now help us write Python code, explore datasets, and develop machine learning models. These capabilities are interesting, but we still need to understand how the results are produced and whether they answer our original problem.
This is one of the reasons I want to explore more about AI for Data Science.
I started Non-Brand Data to share what I have learned from working with Data Science and experimenting with different technologies. I also want it to be a place where we can continue learning together.
So, what can you expect from Non-Brand Data? Let’s explore.
What can you learn here?
Non-Brand Data mainly focuses on Data Science, Machine Learning, and AI.
As AI becomes more involved in our daily work, I want to explore how we can use it in our Data Science activities.
This includes using AI for data analysis, machine learning development, model evaluation, and other related workflows. I also want to understand the advantages and limitations of these technologies through examples and experiments.
However, our learning would not be limited to AI.
Python, SQL, statistics, and traditional Machine Learning are still important subjects. Understanding these fundamentals helps us make better decisions when working with AI-generated results.
You can expect articles that explain different concepts, compare tools, introduce Python packages, and explore how certain methods work.
Apart from individual articles, I also develop written courses for those who want to learn a subject in more detail.
Where should you start?
There are already many articles available in Non-Brand Data. If you are new here, I would suggest starting with a topic that interests you.
Here are some articles you can explore:
Context Engineering for Data Science — Explore how project context can affect the decisions made by AI agents during machine learning development.
What Is Jev AI and How Can We Use It for Data Science? — Learn how we could use AI to turn text information into categories and scores for our analysis.
Data Frame EDA Packages Comparison — Compare different Python packages for exploring our datasets.
Simple RAG Implementation With Contextual Semantic Search — Explore one approach to implementing a RAG system.
You don’t need to follow any specific order. Feel free to explore other articles depending on what you want to learn.
I also have the NBD Focus Map if you prefer a learning guide with selected articles.
Learn more with NBD courses
Sometimes, reading an individual article is not enough to understand a subject.
We might want to follow a series of lessons, try the examples ourselves, and understand how everything connects.
This is why I also develop written courses for Non-Brand Data.
One of the courses already available is the SQL and Analytics Course.
The course focuses on how we use SQL for data analysis. We will learn how to write queries, define metrics, check our results, and explain the decisions behind our analysis.
The first module is free, while the remaining modules are available to paid subscribers.
I also plan to develop more courses, especially around AI for Data Science.
Free and paid subscriptions
Most readers can start learning through the free articles available in Non-Brand Data.
You can subscribe for free to receive articles about Data Science, Machine Learning, AI, and other related subjects.
For those who want to learn more, the paid subscription provides access to in-depth articles, tutorials, written courses, and the full archive.
Depending on the content, I also share additional materials such as notebooks, code, and templates that you can use while learning.
Paid subscribers can also receive special discounts on separate NBD courses, books, and workshops when they are available.
You can read more about the subscription through the Subscriber Benefits page.
A little about me
I’m Cornellius Yudha Wijaya, a Data Science professional with more than eight years of experience in Data Science, Machine Learning, and AI.
Throughout my career, I have worked on various Data Science projects and managed teams working with data and AI technologies.
I’m also one of the authors of Python Data Analysis, Fourth Edition, published by Packt.
Writing has always been one of my ways to keep learning. Whenever I find something interesting, I like to explore how it works and share what I have learned with other people.
That is also what I want to continue doing with Non-Brand Data.
Hopefully, the articles and courses here can help you understand a new subject or improve something you are already learning.
If there is anything you want me to explore, feel free to leave a comment or reply to one of my emails.
I would love to hear your thoughts!
Thank you for reading Non-Brand Data, and happy learning!