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Kaggle micro-course: Python
If you are familiar with Python you can skip this part. Here you’ll learn basic Python concepts that will help you start learning data science. There will be a lot of things about Python that are still going to be a mystery. But as we advance, you will learn it with practice.Link: Price: FreeKaggle micro-course: Pandas
Pandas is going to give us the skills to start manipulating data in Python. I consider that a 4-hour micro-course and practical examples is enough to have a notion of the things that can be done.Link: Price: FreeKaggle micro-course: Data Visualization
Data visualization is perhaps one of the most underrated skills but it is one of the most important to have. It will allow you to fully understand the data with which you will be working.Link: Price: FreeKaggle micro-course: Intro to Machine Learning
This is where the exciting part starts. You are going to learn basic but very important concepts to start training machine learning models. Concepts that later will be essential to have them very clear.Link:
Precio: FreeKaggle micro-course: Intermediate Machine Learning
This is complementary to the previous one but here you are going to work with categorical variables for the first time and deal with null fields in your data.Link:
Price: FreeLet’s stop here for a moment. It should be clear that these 5 micro-courses are not going to be a linear process, you are probably going to have to come and go between them to refresh concepts. When you are working in the Pandas one you may have to go back to the Python course to remember some of the things you learned or go to the pandas documentation to understand new functions that you saw in the Introduction to Machine Learning course. And all of this is fine, right here is where the real learning is going to happen.Now, if you realize these first 5 courses will give you the necessary skills to do exploratory data analysis (EDA) and create baseline models that later you will be able to improve. So now is the right time to start with simple Kaggle competitions and put in practice what you’ve learned.Kaggle Playground Competition: Titanic
Here you’ll put into practice what you learned in the introductory courses. Maybe it will be a little intimidating at first, but it doesn’t matter it’s not about being first on the leaderboard, it’s about learning. In this competition, you will learn about classification and relevant metrics for these types of problems such as precision, recall, and accuracy.Link:Kaggle Playground Competition: Housing Prices
In this competition, you are going to apply regression models and learn about relevant metrics such as RMSE.Link:By this point, you already have a lot of practical experience and you’ll feel that you can solve a lot of problems, buuut chances are that you don’t fully understand what is happening behind each classification and regression algorithms that you have used. So this is where we have to study the foundations of what we are learning.
Many courses start here, but at least I absorb this information better once I have worked on something practical before.Book: Data Science from Scratch
At this point, we will momentarily separate ourselves from pandas, scikit-learn ,and other Python libraries to learn in a practical way what is happening “behind” these algorithms.This book is quite friendly to read, it brings Python examples of each of the topics and it doesn’t have much heavy math, which is fundamental for this stage. We want to understand the principle of the algorithms but with a practical perspective, we don’t want to be demotivated by reading a lot of dense mathematical notation.Link: Price: $26 aproxIf you got this far I would say that you are quite capable of working in data science and understand the fundamental principles behind the solutions. So here I invite you to continue participating in more complex Kaggle competitions, engage in the forums, and explore new methods that you find in other participants' solutions.Online Course: Machine Learning by Andrew Ng
Here we are going to see many of the things that we have already learned but we are going to watch it explained by one of the leaders in the field and his approach is going to be more mathematical so it will be an excellent way to understand our models even more.Link: Price: Free without the certificate — $79 with the certificateBook: The Elements of Statistical Learning
Now the heavy math part starts. Imagine if we had started from here, it would have been an uphill road all along and we probably would have given up easier.Link: Price: $60, there is an official free version on the page.Online Course: Deep Learning by Andrew Ng
By then you have probably already read about deep learning and play with some models. But here we are going to learn the foundations of what neural networks are, how they work, and learn to implement and apply the different architectures that exist.Link: Price: $49/monthAt this point it depends a lot on your own interests, you can focus on regression and time series problems or maybe go more deep into deep learning.I wanted to tell you that I launched a Data Science Trivia game with questions and answers that usually come out on interviews. To know more about this .Also published at