How to Practice Data Science Skills Using Kaggle?
One of the largest communities of data scientists and machine learning experts worldwide is Kaggle.
One of the largest communities of data scientists and machine learning experts worldwide is Kaggle. More importantly, this platform is actively utilized by some of the top data scientists in the world. It includes more than 1 million registered users, thousands of public datasets, and code snippets (also known as notebooks). I'm not sure how many other professions provide something comparable. It offers ambitious data scientists a rare chance to study for free with the best in the world.
1. Equip yourself with the basic skills
Kaggle is a fantastic place to learn and hone your data science skills, but it could quickly become overwhelming if you don't have a firm grasp of the fundamentals. Therefore, perform a gap analysis on your skill set, ascertain your degree of proficiency, and determine what is necessary for you to advance to a level of competency where you feel at ease with the following: Basic programming in any one programming language. Python and R are the most popular programming languages for data science. Therefore, many notebooks available in Kaggle will also be in either Python or R. Having some basic programming expertise would be very beneficial in examining and understanding the notebooks provided.
2. Explore the datasets
If you are new to data science, start with dataset investigations. Start with small datasets to ensure that importing, analyzing, and visualizing the data won't take too long. Also, try to choose datasets from relevant domains for you because this will aid in subsequent data analysis.
You can frame your queries for the exploratory data analysis by looking at the dataset descriptions, which often include information about how the data were acquired, the period to which they belong, and other facts. Then, start exploring the dataset and captures the insights.
Check out the "Tasks" tab for further analysis ideas. This is a recently-introduced tool in which users can contribute intriguing data-related tasks, and others can submit their answers.
3. Learn from the EDA code snippets
It's time to study with the top experts in data exploration. Go to the Notebooks tab for the datasets you have been working on, and search for analysis code snippets with many upvotes and those from highly qualified people.
Next, investigate the analysis being conducted and attempt to contrast it with what you have completed. Finally, find the areas of analysis or gaps you missed; this retrospective analysis will ensure that the learning is effective. Finally, analyze different datasets and notebooks with analysis scripts in a similar manner to comprehend the types of analysis carried out by some of the more seasoned data scientists.
Overall, Kaggle is a great start for your data science career. Keep exploring, engage with experts and participate in competitions, and eventually, you'll become a pro data scientist.
Additionally, an IBM-certified data science course in Mumbai could be of interest if you're seeking a comprehensive data science Bootcamp.