![[Lớp căn bản] Học Pandas để sử dụng ngay강의 썸네일](https://cdn.inflearn.com/public/courses/328195/cover/bd199baf-6c2e-49d9-8e2c-1bbe65eebbb3/pandas_main_img.jpg?w=420)
[Lớp căn bản] Học Pandas để sử dụng ngay
breachers
Học các tính năng cốt lõi của Pandas bằng cách so sánh với SQL!
초급
Python, Pandas
Do you have data but feel lost on how to read and process it in Python? Don't worry. Pandas can handle it with its magic. Pandas is the most powerful, efficient, and useful data processing library. Skill-UP your data preprocessing with Pandas! Insights galore!
13 learners
Data processing skills usable across one's career
Pandas, widely established as an essential element for data analysis!
Data merging, restructuring, handling missing values, handling duplicate data
Text data, Categorical data, Date data processing
Downloadable textbooks (PDF) and practice files provided.
This is not just about showing you the features of Pandas. It explains the context of “why”, “when”, “how”, and “what criteria” you should use to preprocess data, so that you can understand and make your own judgment .
You can practice coding right on Google Colab with just a web browser, without having to install anything on your PC.
We provide PDF tutorial files and ready-to-use practice code .
You can develop a sense of practical preprocessing with the real movie IMDB dataset. You can develop problem-solving skills by encountering preprocessing problems that can occur in real data.
Pandas is a powerful and flexible Python library specialized in data preprocessing .
Data preprocessing is an essential process of converting raw data into a form suitable for analysis before data analysis or data modeling.
You can improve data quality and enhance analysis efficiency by appropriately handling missing values, outliers, and duplicate data.
It can process text data, categorical data, and time series data .
Check out the lecture for more details. 😄
How do I load data from a file ?
How do I select rows or columns in a DataFrame that meet certain criteria ? Is there a way to filter or sort the data by a desired criterion?
When merging or concatenating multiple DataFrames , I am confused about the difference between merge() and concat() and when each is appropriate to use. Can you explain it clearly?
What is an effective way to handle missing values ? When should we delete them and when should we replace them? For example, how should we determine the criteria for replacing them with a specific statistic?
Besides visual methods for detecting outliers , are there any statistical criteria or functions that can be used? And is it best to always remove detected outliers?
When preprocessing text data , "regular expressions" are said to be important. What are they?
How do you distinguish categorical data ? One-Hot Encoding vs. Label Encoding - When is each method better to use?
When dealing with time series data , are there any special preprocessing considerations other than date/time format conversion? For example, can preprocessing include things like adjusting time intervals or calculating moving averages?
We provide friendly and detailed practical training courses that anyone can easily follow and understand.
For those who want to get started with data analysis
Beginners who want to challenge themselves in data analysis work and strengthen their data processing capabilities
Those who feel that they lack basic skills
For those who want to start data analysis but don't know where to start
For those new to Pandas
Those who have already studied data analysis but are having difficulty using it because they are not familiar with Pandas
You can master the basics of Pandas .
Even those who have been frustrated time and time again because they are not familiar with using Pandas can now use Pandas with confidence .
You will be able to understand data preprocessing techniques and become familiar with the main tasks and techniques performed in the preprocessing stage .
Q. Can I take the course even if I don't know much about Python?
You should have a basic understanding of Python's grammar .
Q. Why should I learn data preprocessing?
There is a saying that "80% of data analysis work is data preprocessing," so much time is spent on data preprocessing. In the real world, there is no clean data (raw data) such as "no value, strange value, incorrect format, etc." Unrefined data can distort the results of data analysis. Therefore, data preprocessing can be said to be an essential step in data analysis .
Tools you'll need: Google Colabatory. All you need is a Google account and a web browser.
We provide learning materials in PDF format.
Provides practice files (.ipynb), practice data, etc.
This course is for beginners in data analysis and requires a basic understanding of Python syntax.
You don't have to study all the lectures in order. If you are somewhat familiar with Pandas, you can just choose the parts you need. If you are new to Pandas, please start from the beginning and learn slowly.
Python, Pandas, data-science, data-analysis, data-cleaning
Who is this course right for?
Thirsty for Pandas data preprocessing
Those new to data analysis
Need to know before starting?
Python Basics
전산학 학사, 통계학 석사
삼성디스플레이, 삼성 전자, 한국 오라클 교육센터, 멀티 캠퍼스, 에티버스러닝 등 다수의 기업체 강의 경력
Oracle 공인 강사, Oracle Cloud Infrastructure(OCI) 공인 강사
Google Cloud Authorized Trainer(GCP) 공인 강사
데이터 분석, 데이터 시각화, 머신러닝, 딥러닝, Cloud, RDBMS 등 강의
All
24 lectures ∙ (6hr 43min)
Course Materials:
$34.10
Explore other courses in the same field!