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Getting started with Python data analysis using public data

There was a rumor that Ediya would open a store near Starbucks. How different would the locations of Ediya and Starbucks be? Will the real estate price fluctuation trend from 2013 to 2019 be reflected in the apartment sales price? What kind of parks are there in my neighborhood? How can I utilize the data in the public data portal? The goal is to become familiar with Python and various data analysis libraries by handling various types of data through public data.

(4.9) 336 reviews

6,305 learners

  • todaycode
Python
Pandas
Numpy

Reviews from Early Learners

What you will gain after the course

  • Data Analysis and Visualization with Python

  • Practice using public data

  • Data preprocessing and statistical analysis

  • Map visualization and text data processing



I have collected valuable feedback from running the course for a year.
In 2020, "Getting Started with Python Data Analysis with Public Data" has been completely revamped!

✍🏻 I rewrote both the code and the video .

• A wider variety of graphs (heat maps, histograms, distributions, scatter plots, regression graphs, etc.) than before have been covered, and content has been added to make it easier to draw subplots.

📝 We provide both practice code and result code .

Please use the practice file ( 01-apt-price-input.ipynb ) that provides a simple guide so that you can follow the code while watching the video, and the file (01-apt-price-output.ipynb) that displays the results .


Related Roadmap

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A data science roadmap you can use in your real-world work!
Including this lecture

Recommended for
these people

Who is this course right for?

  • Beginners who want to learn Python

  • People interested in data analysis

  • Researchers who want to utilize public data

  • Students who want to practice by handling real data

Need to know before starting?

  • Python Basic Grammar

Hello
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19,387

Learners

812

Reviews

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Answers

4.9

Rating

7

Courses

Curriculum

All

84 lectures ∙ (14hr 10min)

Course Materials:

Lecture resources
Published: 
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Reviews

All

336 reviews

4.9

336 reviews

  • mudcook1083님의 프로필 이미지
    mudcook1083

    Reviews 1

    Average Rating 5.0

    5

    20% enrolled

    I am a student who is taking on a new challenge at a fairly young age. I used to do similar data analysis at my previous company, but if there were such convenient and good tools, I would have been able to increase productivity at my company. Through the instructor's lecture, I was able to learn that data analysis can be done easily, broadly, and deeply through Python notebooks. I am so grateful that it gave me a new perspective on approaching data. The lecture is so easy to understand and informative that I would like to recommend it to others.

    • hsw4000847님의 프로필 이미지
      hsw4000847

      Reviews 5

      Average Rating 5.0

      5

      36% enrolled

      I am studying in the US. It is more informative than the lectures by famous professors at school.

      • daehynk3548님의 프로필 이미지
        daehynk3548

        Reviews 8

        Average Rating 5.0

        5

        24% enrolled

        I think this is the best lecture in terms of data analysis (loading, preprocessing, EDA, visualization). While studying Python data analysis methods and coding examples, I think, "What can I do with this?" I think this lecture provides answers and clues to that. Also, many of the methods used in parts are very useful. In addition, it was very good that it was renewed by supplementing recent data and explanations. Conclusion: If you want pandas, seaborn, matplotlib + @, just listen. If you are a beginner, you will never regret it.

        • todaycode
          Instructor

          Thank you for your thoughtful review! Thanks to you, it has been a great help in updating all the courses up to Chapter 5. In particular, Chapter 5 has added content on analyzing and visualizing text data, such as extracting frequency from existing structured data, and implementing information masking for personal information protection using regular expressions using email, phone number, and car registration number. We will continue to update the content through feedback in the future :)

      • wonseok님의 프로필 이미지
        wonseok

        Reviews 15

        Average Rating 4.7

        5

        100% enrolled

        Hello? This is Sebastian Junior 3rd. I have been looking for various lectures, but when it comes to learning Python preprocessing and visualization, Professor Park Jo-eun's lecture seems to be the best. I sincerely thank you for making such a great lecture! My personal wish is that you make lectures by grouping Kaggle practice by topic so that it can be applied in practice..! ㅎㅎㅎ Thank you again!

        • chadeng842490님의 프로필 이미지
          chadeng842490

          Reviews 6

          Average Rating 5.0

          5

          98% enrolled

          Hello This lecture is a really good lecture that gave me a rough idea of Python. This lecture may not cover 100%, but it taught me the basics so that I could search and find things through this lecture. Thank you so much. It's the best.

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