Kaggle is the most popular platform for Data Science. It has multiple free datasets, projects that you can use for practice, and competitions that have insane prizes! It also has a helpful community where you can share your thoughts and learn new things. But the best feature of Kaggle is Kaggle Learn. Even if you don’t know anything about data science, you can learn all the basics from Kaggle Courses and then move on to sharpening your skills by doing projects.
These Kaggle courses for Data Science are the micro-courses that are the fastest way to gain the skills you need for data science projects. They provide a quick introduction to Data Science if you are a beginner by covering all the important topics like Python, machine learning, data visualization, Pandas, SQL, deep learning, natural language processing (NLP), etc. So let’s see all of these courses in detail and understand what all you can learn from them.
This is the first mini-course in the series of courses provided for Data Science. And that’s because you need to learn Python before venturing into deeper waters! This course gives you a basic understanding of the Python language starting from its syntax to functions, booleans, conditionals, lists, loops, list comprehensions, strings, dictionaries, and external libraries. Each of these modules in the course contains basic information followed by examples and exercises, so you can learn by doing. These give a holistic knowledge about Python in 2 hours which you can build upon until you have mastered the most popular language for Data Science!
2. Introduction to Machine Learning
Machine Learning is an important part of Data Science as ML algorithms are trained using data and then used with data based on their requirement. That’s why this second mini-course on Kaggle deals with the introduction to Machine Learning with a special focus on Machine Learning models, model validation, underfitting, overfitting, random forests, and an exercise that teaches more about Machine Learning competitions. There are also some bonus lessons on Introduction to AutoML and getting started on your own Kaggle notebooks for submitting in competitions. And you’ll use the Titanic: Machine Learning from Disaster competition as an example. That’s a famous competition on Kaggle!
3. Intermediate Machine Learning
After the introduction, Kaggle then has the Intermediate Machine Learning micro-course that delves deeper into Machine Learning. It focuses mainly on missing values in a dataset, Pipelines, Cross-Validation, XGBoost, Data Leakage, etc. Learning this course will ensure that your Ml models are much more accurate and useful than they otherwise might be.
4. Data Visualization
The next course focuses on Data Visualization, which is an immensely important part of Data Science. It is very difficult to convey data insights and patterns to people when that data is stored in rows and rows of tables. That’s where data visualization is extremely helpful as it conveys the data insights in an easily understandable manner. This mini-course starts with Seaborn and then teaches you how to create line charts, bar charts, heatmaps, scatter plots, histograms, and density plots. It also helps in selecting the right visualization for the data and then uses a final project to test your skills in all you have learned.
Pandas is a very popular Python software library for data analysis and data handling. So it stands to reason that this is the next mini-course you are going to learn. It starts with creating, reading, and writing data using Pandas and then moves on to indexing, selecting, combining, sorting, renaming, assigning, grouping, etc. These are all techniques that are fundamental in Data Science as they help in cleaning and preparing your datasets. This course also teaches you how to investigate data types within a DataFrame or Series and how to tackle missing values in data.
6. Intro to Deep Learning
Kaggle Courses also focus on Deep Learning at a basic level so that you can move on to advanced topics on your own later. This course starts with an introduction of deep learning in computer vision and then moves on to building models from convolutions, TensorFlow, and Keras programming, building highly accurate models using transfer learning, as well as making more data available for model training using data augmentation. Then it teaches you a deeper understanding of Deep Learning with stochastic gradient descent and back-propagation and how to build models without transfer learning. There is also a bonus lesson that helps you in joining the Petals to the Metal Kaggle competition where you are required to build a machine learning model that identifies the type of flowers in a dataset of images.
7. Introduction to SQL
Now we move into the realm of databases and with it, comes SQL! As you know, SQL is a very popular database management language, so it’s obvious that a Kaggle micro-course covers this as well. The course deals with the basics of SQL and BigQuery and teaches you how to create SQL queries using common keywords like Select, From, Group By, Where, Having, Count, Order By, As & With, etc. It also teaches you how to combine various data sources using the Join and the different types of Join.
8. Advanced SQL
After the introduction to SQL, Kaggle then moves on to the advanced SQL micro-course that teaches this topic in further detail. This includes more information about the various Joins and Unions as well as explaining analytic functions, nested data, and repeated data. Finally, it teaches you various strategies on how to write more efficient queries than before.
9. Geospatial Analysis
Geospatial Analysis focuses on Geospatial data and how to handle it correctly. This mini-course starts with learning how to plot in GeoPandas, an open-source project that makes working with geospatial data in Python much easier. You will also learn about coordinate reference systems that represent around 3-D Earth in 2-D along with the basics of creating interactive heatmaps and choropleth maps. This course also teaches you how to manipulate geospatial data in addition to the basics of proximity analysis.
10. Natural Language Processing
This is a short course that teaches the basics of Natural Language Processing. NLP is a part of Artificial Intelligence that focuses on teaching language like speech and text to machines. Siri, Alexa, etc. are a prime example of this! Since Natural Language Processing is such a complex topic, Kaggle only has a basic micro-course that covers the Introduction to NLP, text classification that combines machine learning with NLP skills, and then ends up with a module on word vectors.
Apart from all these courses, there are some more courses as well on Kaggle that cover various other aspects of Data Science. These include Feature Engineering that teaches you how to improve your models with baseline models, categorical encoding, feature generation, and feature selection, Computer Vision, Data Cleaning, and Machine Learning Explainability. Another course is Intro to Game AI and Reinforcement Learning. This is a fun course that allows you to create your video game bots using the minimax algorithm and deep reinforcement learning. In addition to all these courses, there is also a separate module for Micro challenges that will allow you to apply all that you have learned and test your skills.
All these Kaggle micro-courses and challenges might not make you an expert Data Scientist single-handedly, but they will make you SMARTER and more capable of using these basics to further build up your knowledge. And they are free! So you have nothing to lose and a lot to gain!