Practical Data Science Part 1. Introduction to Python
This course is for those who need to introduce data analysis, machine learning, AI, etc. to their work but are not familiar with Python programming. You will systematically learn the core functions of Python required to become a data scientist in a short period of time.
Python core functions for implementing data analysis and machine learning models
Basics of handling data and processing file input/output
Contains only the essentials! Essential Python for Data Analysis
Big data analytics, machine learning, deep learning, artificial intelligence, and digital transformation (DT) are among the most in-demand technology fields today. In nearly every industry, training data scientists to handle these technologies is crucial and urgent.
To become a data scientist, you must first and foremost be able to use Python fluently. However, learning Python takes so much time that many people give up early on.
📕 Learn only the essentials you need in the field!
This course focuses on the fundamentals and core concepts of Python required for practical data analysis and machine learning model development in the field. It doesn't delve into in-depth beginner topics or introduce numerous Python features. Instead, it focuses solely on the core concepts.
STEP 00
First, learn how to use Jupyter Notebook and GitHub by setting up your Python environment. The GitHub tutorial shares the source code location, so follow along and learn.
STEP 01
Beginning with a solid foundation in Python, you'll learn the differences and characteristics of handling numbers, strings, and Boolean variables, as well as lists, tuples, and dictionaries for grouping and processing multiple data sets. You'll also learn how the "if," "for," and "while" statements manage program flow.
STEP 02/03
Next, we will learn how to manipulate data frames, a two-dimensional table structure provided by the pandas package (searching, adding, deleting data, etc.), and cover numerical operations on matrices (arrays) provided by NumPy.
STEP 04
We cover data visualization and introduce the usage and features of representative visualization techniques such as plot, scatter, hist, boxplot, and bar graphs.
STEP 05
Python's greatest strength is its ability to conveniently apply functions to data . Learn various ways to apply functions and the differences between lambda, map, and apply.
🙋♂️ 5 hours is enough!
Many people who study Python for problem solving give up because of its difficult theories. However, it's highly likely that you won't need those theories to solve the problems you want to solve.
As Saint-Exupéry said, "Perfection is not when there is nothing more to add, but when there is nothing more to take away." In this lecture, I aim to convey the core contents of Python required to become a data scientist in a minimal amount of time .
We hope that through this course, everyone will successfully advance to become a data scientist.
Kim Hwa-jong, CEO of Data Science Lab
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Who is this course right for?
Anyone who needs to adopt machine learning technology to solve their own tasks
For those who are not familiar with Python but want to learn the core Python in a short period of time
Need to know before starting?
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"고장난 라디오 고칠 수 있어?"
제가 전자공학과에 입학한 후 친구로부터 받은 질문입니다. 뭐, 대답은 했습니다. "전자공학과에서는 라디오 만드는 원리를 배우는 것이지 고장난 전자제품 고치는 것은 우리 일이 아니고..."
이론으로 무장한 전문가보다 문제 해결사가 필요한 경우가 더 많습니다. 저는 실전 문제 해결이 더 중요하다고 생각합니다.
최근에는 머신러닝으로 금융, 에너지, 전자, 중장비, 물류, 신약개발, 식품 등 산업 영역의 문제를 해결하는 일을 하고 있는데, 정말 배울 것도 많고 할 일도 무궁무진한 영역인 것 같습니다. 본업은 교수지만 (강원대 컴퓨터공학과), 현장의 문제해결에 관심이 많아 여러 겸직을 하고 있습니다. AI신약개발지원센터장, KAIST 겸임교수, 그리고 데이터사이언스랩 대표를 맡고 있습니다.
AI 시대에 가장 필요한 인재는 실전 문제를 해결할 수 있는 데이터 사이언티스트라고 믿으며 여러분 모두 인기 있는 데이터 사이언티스트가 되기를 바랍니다.
Thank you. I made it with beginners in mind, but I tried to include as much basic information as possible. I think it would be good to use it to understand the scope of what even beginners need to know.
It was not difficult at all because it explained only the necessary content in an easy-to-understand manner, and it was a great help in reviewing basic concepts that I had forgotten.
Even if you are not familiar with programming, this lecture was really helpful because it explained the core concepts of Python for applying artificial intelligence to practice in an easy-to-understand manner, even though it was a short lecture! I think it was a lecture that extracted the maximum efficiency in the minimum time!!
Even though I had no basic knowledge of Python, I think this is a really good lecture for beginners to learn the basics, as the core concepts are conveyed in an easy-to-understand and concise manner, from setting up a development environment to utilizing functions.