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Data Science

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Data Analysis

Introduction to Data Science with Silicon Valley Engineers

How to Read the World with Data: Explore Your Own Data Science! Dive deep and practically into the tools and techniques that form the core of modern data science. In particular, you will learn how to analyze data and implement algorithms using Anaconda, Numpy, Pandas, and Scikit-learn, essential components of data science.

(5.0) 5 reviews

120 learners

  • altoformula
이론 실습 모두
파이썬데이터
데이터프레임
Pandas
Scikit-Learn
Numpy
Algorithm

Reviews from Early Learners

What you will learn!

  • Scikit-Learn

  • Pandas

  • data science

Data science that captures both theory and practice,
From basics to analysis + machine learning!

Data science, did it feel difficult?

Anaconda, NumPy, Pandas, Scikit-learn

✅ Students interested in data science
✅ Anyone who wants to learn the basics of data science!

Learn how to analyze data and implement algorithms using Anaconda, Numpy, Pandas, and Scikit-Learn , essential components of data science .

From basics to advanced techniques

  • Beginners who lack understanding of the basic concepts and tools of data science can learn systematically.
  • You can acquire knowledge from basics to advanced step by step while learning how to use essential tools such as Anaconda, Numpy, Pandas, and Scikit-learn.

Solving the difficulties of practical application

  • We provide real-world case studies and project-based learning for those who have difficulty applying data analysis or machine learning models to real-world work.
  • You can learn data science skills that can be immediately applied in practice.

Complex data processing and analysis are also OK

  • We teach you how to efficiently process and analyze large amounts of data.
  • You can learn data preprocessing, analysis, and visualization techniques through Numpy and Pandas.

Various machine learning models built directly

  • Learn how to build and optimize various machine learning models using Scikit-learn.
  • This is especially helpful if you've been struggling to understand and develop machine learning algorithms on your own.

Key strengths of this course

We start with setting up the Anaconda environment and then go through the ins and outs of using Numpy and Pandas, which are the foundations of data processing and analysis.

This will allow you to effectively handle large datasets and become proficient in data preprocessing and transformation processes.

We will also learn how to implement machine learning algorithms using Scikit-learn through hands-on training.

Gain experience building various machine learning models and applying them to real datasets to derive insights!

Introduction and Installation of Anaconda

Understanding Pandas Data Structures

Getting to know Scikit-Learn

Step by step in theory, make it clear in practice!

💡 This course combines theory and practice, with each module featuring real-world case studies and project work designed to help you develop skills that can be applied immediately in the workplace. This course will be a great guide to start your journey into the world of data science.


Knowledge sharer for this course

We will pass on the know-how of current Silicon Valley engineers !
I am a current software engineer who runs the YouTube channel " American Engineer " and the Brunch channel " Silicon Valley News and Life ."

History

Portfolio/Personal Videos


Q&A 💬

Q. Why should I take this course?

This course starts from the basic concepts of data science and is structured to allow you to learn core tools including Anaconda, Numpy, Pandas, and Scikit-learn through hands-on practice. It is ideal for those who want to learn theory and practice in an integrated manner, as you can directly experience practical data analysis and machine learning techniques .

Q. What can I do after taking this course?

By utilizing the skills learned in the lecture, you can perform data analysis, data preprocessing, visualization, and building and evaluating basic machine learning models. These are essential capabilities for deriving business insights or making data-based decisions in various industries.

Q. Can non-majors also take this course?

Yes, you can. This course starts from the basic concepts of data science and gradually advances to more advanced content, so even non-majors can follow along with basic computer skills and a basic understanding of mathematics. However, if you have basic knowledge of the Python programming language , you can take the course more effectively. Mathematical background knowledge, especially statistics and linear algebra, is also helpful.

If you are new to Python, learn the basics of Python through YouTube or take the lecture below first! Even if you only watch the basics, you will have no trouble following the entire lecture.

📢 Guide to practice environment and materials

It doesn't matter what PC operating system you use, whether it's Windows, macOS, Linux, or Ubuntu, but the lecture will focus on macOS. Detailed PC specifications are as follows.

  • Processor (CPU): At least a dual-core processor is recommended, but a processor with more cores will help speed up data processing.
  • Memory: A minimum of 4GB of RAM is required, but 8GB or more is recommended . Data science tasks often require loading large amounts of data into memory, so more RAM is beneficial.
  • Storage space: You will need enough hard drive or SSD space. Scikit-learn itself does not take up much space, but depending on the datasets and project files you will be using, it may require significant storage space.
  • Python version: Python 3.6 or later is required to run Scikit-learn. We recommend using a more recent version of Python.

We share lecture notes in PDF format and source code via Github with students.

Recommended for
these people

Who is this course right for?

  • If you want to become a data scientist

  • Those who want to study the basics of data science

Need to know before starting?

  • Python

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  • 🧭 실리콘 밸리의 혁신 현장에서 직접 배운 기술과 노하우를 온라인 강의를 통해 이제 여러분과 함께 나누고자 합니다.

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  • 🫡 똑똑하지는 않지만, 포기하지 않고 꾸준히 하면 뭐든지 이룰수 있다는 점을 꼭 말씀드리고 싶습니다. 항상 좋은 자료로 옆에서 도움을 드리겠습니다

 

Curriculum

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26 lectures ∙ (5hr 29min)

Course Materials:

Lecture resources
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Reviews

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5 reviews

5.0

5 reviews

  • 불뚝님의 프로필 이미지
    불뚝

    Reviews 13

    Average Rating 5.0

    5

    100% enrolled

    잘 들었습니다.

    • 미쿡엔지니어
      Instructor

      안녕하세요 불뚝님, 시간내서 좋은 평가해주셔서 감사합니다!

  • JE님의 프로필 이미지
    JE

    Reviews 2

    Average Rating 5.0

    5

    15% enrolled

    도움 많이 되었습니다!

    • 안녕하세요 JE님, 시간내서 좋은 리뷰 남겨주셔서 감사합니다!

  • 동그리님의 프로필 이미지
    동그리

    Reviews 1

    Average Rating 5.0

    5

    12% enrolled

    데이터 사이언스 기초를 복습하기에 좋은 강의였어요! 개인적으로 매우 만족했어요!

    • 안녕하세요 동그리님! 시간내어서 좋은 리뷰 남겨주셔서 감사합니다.

  • water_bottle님의 프로필 이미지
    water_bottle

    Reviews 3

    Average Rating 5.0

    5

    54% enrolled

    안녕하세요 백엔드 개발자로 일하고 있습니다. 데이터 분석, 사이언스가 어떤건지 느낌을 알고 싶어서 수강 했는데 다른 분들은 어떨지 모르겠지만 저처럼 데이터 사이언스 관련해서 무지한 사람 기준으로는 수강이 조금 어려운 것 같아요. ㅠㅠ

    • 안녕하세요 water_bottle님, 일단 데이터 사이언스라는 분야가 수학을 기본으로 하고, 확률을 높이는 쪽이라 기존에 백엔드 같이 100%값이 나오는 분야가 아니라 어려울 수 있는데, 어떤 점이 가장 이해가 되지 않으시나요? 그래도 좋은 리뷰 감사드립니다.

    • 앗 답변이 늦었네요. 저에겐 섹션3, 4가 어려웠습니다. 유튜브 다른 강의는 잘 보고 있습니다. 감사합니다 :)

  • DE rocks님의 프로필 이미지
    DE rocks

    Reviews 1

    Average Rating 5.0

    5

    27% enrolled

    현업 데이터 엔지니어입니다. 강의 설명이 명쾌하며 구성도 잘 짜여져있어서 다시 한번 개념잡기 좋았습니다. 강추입니다

    • 안녕하세요 metacret님, 좋은 리뷰 감사드립니다!

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