Data analysis starting with Python (from Python grammar for data analysis to data collection, preprocessing, and exploration)

Data analysis is no longer a trend, but essential knowledge

Those of you who clicked on this course right now probably either found yourselves in a situation where you needed data analysis, or experienced pressure (?) from others telling you to 'learn data analysis.' And I'm confident that everyone will experience this sooner or later, with only differences in timing. This is because data analysis provides essential information for understanding this world and making important decisions.

Data analysis skills, which were considered the domain of a select few experts just a few years ago, have now become popularized technology that's useful everywhere, like a required liberal arts course. We live in an era where anyone should be able to perform data analysis.

Features of this course

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📌 Focuses on core features frequently used in data analysis.

📌 This is a practical Python course where you can build fundamental skills in exploratory data analysis through various hands-on examples.

📌 Samsung Electronics stock price data, National Health Insurance Service health screening information data, and other data that can be used in real life datawere used to construct the examples.

I recommend this for people like this

After taking the course

• Large-scale data numerical computations become possible.

• You can now create a graph showing the stock price movements of Company A over the past 5 years.

• You can examine whether there's a correlation between temperature and sales through graphs.

• You can collect price information from multiple shopping malls and save it to Excel.

• You can create a program that automatically downloads photos of your favorite idols.

You'll learn this kind of content.

Why Python?

While you may know that Python and R are widely used for data analysis, it's not easy to understand exactly what differences exist between the two languages. That's why many people often worry about which language to choose before even starting data analysis in earnest. Since both Python and R are good programming languages for data analysis, there's no right answer, but if your purpose for starting data analysis is as follows, I recommend choosing Python.

1. I want to work as an IT engineer
   Programs written in Python have the advantage of being easily portable to existing IT systems. There are various IT systems in the world, such as portal sites, shopping malls, and financial transaction systems. Programs written in Python are well-suited for adding to already established IT systems.
   
   

2. I want to learn deep learning based on artificial neural networks
   Most of the deep learning algorithms that have been gaining attention recently are written in Python. Therefore, if you want to ultimately learn not only statistical data analysis but also machine learning and deep learning algorithms, I recommend starting data analysis with Python.
   
   

3. I want to learn a programming language that many people use

   
   Globally, Python is the third most widely used programming language after C and Java. Additionally, Python users are steadily increasing, and languages that many people use have the advantage of providing access to abundant information. Most of the algorithms I need are already available as packages, and since high-performance algorithms are constantly being developed and shared, it reduces the effort of having to implement every part of a program from scratch.

Pre-enrollment Reference Information