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Data Science

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Data Analysis

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

From the basics of Numpy and Pandas to data preprocessing, visualization, and crawling, all in one place! This is an introductory Python course for data analysis.

(4.6) 8 reviews

53 learners

  • ilifo
파이썬입문
이론 실습 모두
Python
Numpy
Pandas
Seaborn
Matplotlib

What you will learn!

  • Basic concepts of Python such as variables, data types, conditional statements, loops, functions, modules, and packages

  • How to process large amounts of data quickly and efficiently based on an understanding of Numpy and Pandas

  • Data visualization using Seaborn and Matplotlib

  • How to create a program that receives an HTML document from a server and extracts only the desired results

  • Writing a program that dynamically operates the browser

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

📌[[SPAN_1]][[/SPAN_1]][[SPAN_2]][[STRONG_3]]탐색적 데이터 분석[[/STRONG_3]][[/SPAN_2]][[SPAN_4]]을 위한 파이썬 프로그래밍 기초 강의입니다.[[/SPAN_4]]

📌 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

Python Basics Complete, What's Next?
You've finished the beginner course
but when trying to apply it in real life
you don't know where or how to start
and find yourself stuck in many ways

I'm interested in data analysis.
Starting from Python basics
and learning step by step
to acquire data analysis skills
for those who want to learn

I want to collect data directly myself.
Those who want to crawl HTML from the web
to directly collect the data they want
and utilize it for analysis

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.

Basic Concepts of Python

You will learn Python's basic concepts including variables and data types, conditional statements, loops, functions, modules, packages, and more.

Numpy, Pandas

Learn how to process large amounts of data quickly and efficiently.

Data Visualization Using Seaborn and Matplotlib

Learn how to visualize data using methods appropriate for the characteristics of your data.

Web Crawling

Learn how to retrieve HTML documents from servers and extract only the desired results from HTML.

[Getting Started with Data Analysis using Python]If you've built the foundation of analysis with

[Machine Learning + Deep Learning Starting with Python]to AI Application!

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

Practice Environment

  • The hands-on practice will be conducted using Jupyter Notebook from Anaconda.

  • For data collection and crawling, the results of the practice may vary depending on whether there are changes to the target website.

Recommended for
these people

Who is this course right for?

  • For those who are new to Python for data analysis

  • For those who dream of becoming a data analyst

Hello
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270

Learners

15

Reviews

2

Answers

4.4

Rating

3

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인스타그램: https://www.instagram.com/ilifo0182/

유튜브: https://www.youtube.com/channel/UCYqYscK7l_1Z5AT1Of0KUkQ

Curriculum

All

29 lectures ∙ (6hr 40min)

Course Materials:

Lecture resources
Published: 
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Reviews

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8 reviews

4.6

8 reviews

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            $26.40

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