Inflearn brand logo image
Inflearn brand logo image
Inflearn brand logo image
Data Science

/

Data Analysis

[Data Preprocessing] Don't worry! Because we have 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!

(5.0) 1 reviews

13 learners

  • aonekoda
판다스
데이터분석
데이터분석실습
데이터처리
공공데이터
Python
Pandas
Data Engineering
data-science
data-processing

Reviews from Early Learners

What you will learn!

  • 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.

📢 Benefits of this course

  • 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.

📌 Data Preprocessing using Pandas

  • 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. 😄

📌 Data preprocessing? We answer these questions!

  • 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.

📌 Prepared for these people!


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

🏅 What can I do after completing this course?

  • 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 .

🤔 Do you have any questions?

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 .

🛍 Things to note before taking the class

Practice environment

  • Tools you'll need: Google Colabatory. All you need is a Google account and a web browser.


Learning Materials

  • We provide learning materials in PDF format.

  • Provides practice files (.ipynb), practice data, etc.

Player Knowledge and Notes

  • 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

Recommended for
these people

Who is this course right for?

  • Thirsty for Pandas data preprocessing

  • Those new to data analysis

Need to know before starting?

  • Python Basics

Hello
This is

  • 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

     

Curriculum

All

24 lectures ∙ (6hr 43min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

1 reviews

5.0

1 reviews

  • sprun님의 프로필 이미지
    sprun

    Reviews 1

    Average Rating 5.0

    Edited

    5

    29% enrolled

    파이썬 데이터 전처리 공부에 많은 도움이 되었습니다. 후속 강의도 마련되면 좋겠습니다. 기초부터 차근차근 잘 알려주셔서 감사합니다.

    • 디디
      Instructor

      좋은 수강평 감사합니다.

$34.10

Similar courses

Explore other courses in the same field!