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Shingo Tsuji
and
Pierian Data International by Jose Portilla
このコースは、Pythonを使ってデータを解析し可視化するために必要なスキルを網羅しています。Pythonと科学計算のためのライブラリの使い方が完璧に理解できるようになっています。
このコースを習得すれば、次のような事ができるようになります。
- Pythonプログラミングへの知識が深まります。
- NumPyを使って、アレイを使った数値計算ができるようになります。
- pandasを使った効果的なデータ解析ができるようになります。
- Matplotlibとseabornを使って、出版にも使えるほど綺麗なデータの可視化が可能になります。
- Pythonを使って実際にデータを解析する方法論が身につきます。
- 機械学習への理解が相当高まります。
2023年5月にコースの大幅改訂を行いました。ほとんどすべての動画と資料が更新されています。
17時間以上、100本を超えるビデオと、すぐに使えるPythonコードがまとまった資料が用意されていますので、データサイエンスに関する知識が飛躍的に高まります。
Register for this course and see more details by visiting:
OpenCourser.com/course/zo62h0/[(Shi
What's inside
Syllabus
コースの内容と学習のための指針が掴めます
コースの概要
学習を進めるために
Pythonの導入と、様々なモジュールのセットアップが完了します。
Read more
Syllabus
コースの内容と学習のための指針が掴めます
コースの概要
学習を進めるために
Pythonの導入と、様々なモジュールのセットアップが完了します。
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
The use of Python, NumPy, pandas, Matplotlib, and seaborn to explore data and create visualizations
Teaches developers the basics of data analysis and visualization
Instructed by experts in the field with extensive experience with Python and data analysis
Delivers a thorough understanding of data science concepts and techniques
Provides hands-on practice through practical data analysis projects
Regularly updated with new content and materials
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Pythonデータサイエンス実践入門
受講生によると、このコースはPythonを使ったデータサイエンスの基礎を学ぶ上で非常に分かりやすく、特にNumPyやpandasといったライブラリの使い方について実践的な解説がされているとのことです。理論だけでなく、タイタニックや株式市場などの具体的なデータ解析事例を通して学べる点が高く評価されています。2023年5月の大幅改訂により、内容が最新化され、よりスムーズに学習できるようになったという声もあります。ただし、機械学習パートはあくまで入門レベルであり、より深い知識を得るには別途学習が必要な点には注意が必要です。
内容が新しくなり学習しやすくなりました。
"2023年改訂版ということで、内容が最新になっており、安心して学習できました。"
"改訂後の内容は以前よりさらに分かりやすく、動画の質も向上していると感じました。"
"以前のバージョンを知りませんが、現在の内容は非常にスムーズに学べるように構成されていると思います。"
具体的なデータ解析事例を通して学べます。
"タイタニックや株価データの分析など、具体的なケーススタディが多く、実践的なスキルが身につきました。"
"実際にデータを分析するパートがあり、学んだ知識をどう活用するのかイメージしやすかったです。"
"単なるライブラリの使い方だけでなく、実際のデータを使った分析手法が学べて良かったです。"
複雑な内容も分かりやすく解説されています。
"初心者にも分かりやすいように、一つ一つの概念を丁寧に解説してくれています。"
"先生の説明が非常に丁寧で、スライドやコード例も豊富で理解しやすかったです。"
"データ分析の考え方やコードの書き方など、疑問点を残さずに次に進めました。"
データ解析に必要な基本を網羅しています。
"Pythonでのデータ分析に必要なライブラリ（numpy, pandas, matplotlib, seaborn）の基礎が網羅されていて、とても勉強になりました。"
"データサイエンスの基礎であるnumpy、pandas、matplotlibなどを一通り学ぶことができます。"
"データ処理や可視化といったデータサイエンスの基本的な流れを掴むのに役立ちました。"
機械学習は基礎的な紹介に留まります。
"機械学習についても触れられていますが、こちらはかなり基本的な内容なので、本格的に学ぶなら別の教材が必要です。"
"scikit-learnの使い方の簡単な紹介といった感じで、機械学習アルゴリズム自体の深掘りはありませんでした。"
"データ分析の応用として機械学習の触りを知るには良いですが、このコースだけで実務に活かせるレベルにはなりません。"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in 【2023年5月改訂版】実践 Python データサイエンス with these
activities:
Review basic Python programming concepts
Show steps
Understanding python programming basics will help you learn the libraries covered in this course more effectively.
Browse courses on
Python
Show steps
-
Review basic syntax, data types, and control flow.
-
Complete beginner-level coding exercises to practice syntax.
Read and summarize 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Reading this book will complement the course content and provide a more comprehensive understanding of machine learning.
View
Aprende Machine Learning con Scikit-Learn,...
on Amazon
Show steps
-
Read the book in sections and take notes on key concepts.
-
Summarize each section in your own words, focusing on the main takeaways.
Follow additional NumPy tutorials
Show steps
NumPy is essential for numerical operations in this course. By exploring additional tutorials, you will expand your knowledge of its capabilities.
Browse courses on
NumPy
Show steps
-
Find tutorials covering advanced NumPy features, such as broadcasting and linear algebra.
-
Complete practice exercises to apply newly learned NumPy techniques.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete additional pandas practice problems
Show steps
Practice is crucial for mastering pandas. Additional practice will enhance your ability to efficiently manipulate and analyze data.
Browse courses on
Pandas
Show steps
-
Find online resources or books with pandas practice problems.
-
Dedicate time to solving a variety of pandas problems.
Join a peer-to-peer mentoring group for Python data science.
Show steps
Mentoring will solidify your understanding of concepts and provide opportunities to refine your communication skills.
Browse courses on
Python
Show steps
-
Find a peer mentoring group or organize one with classmates.
-
Take turns presenting concepts, answering questions, and providing feedback.
Develop a data visualization portfolio
Show steps
Creating a portfolio of data visualizations will demonstrate your proficiency in using Matplotlib and Seaborn.
Browse courses on
Data Visualization
Show steps
-
Collect and prepare a dataset.
-
Create various types of visualizations, such as bar charts, line charts, and scatterplots.
-
Refine your visualizations based on feedback.
-
Present your portfolio in a professional manner.
Build a data analysis project using real-world data
Show steps
Working on a real-world data analysis project will give you practical experience in applying the techniques learned in this course.
Browse courses on
Data Analysis
Show steps
-
Identify a dataset and research a specific problem or opportunity.
-
Clean and prepare the data.
-
Apply appropriate data analysis techniques to extract insights.
-
Develop a solution or recommendation based on your analysis.
-
Present your findings to stakeholders.
Contribute to scikit-learn
Show steps
Contributing to scikit-learn will deepen your understanding of machine learning algorithms and open-source development.
Browse courses on
Machine Learning
Show steps
-
Familiarize yourself with the scikit-learn codebase.
-
Identify an area where you can make a meaningful contribution.
-
Submit your code changes as a pull request.
-
Collaborate with maintainers to refine your contribution.
-
Get your contribution merged into the main scikit-learn repository.
Review basic Python programming concepts
Show steps
Understanding python programming basics will help you learn the libraries covered in this course more effectively.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic syntax, data types, and control flow.
- Complete beginner-level coding exercises to practice syntax.
Read and summarize 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Reading this book will complement the course content and provide a more comprehensive understanding of machine learning.
View
Aprende Machine Learning con Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the book in sections and take notes on key concepts.
- Summarize each section in your own words, focusing on the main takeaways.
Follow additional NumPy tutorials
Show steps
NumPy is essential for numerical operations in this course. By exploring additional tutorials, you will expand your knowledge of its capabilities.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Find tutorials covering advanced NumPy features, such as broadcasting and linear algebra.
- Complete practice exercises to apply newly learned NumPy techniques.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete additional pandas practice problems
Show steps
Practice is crucial for mastering pandas. Additional practice will enhance your ability to efficiently manipulate and analyze data.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Find online resources or books with pandas practice problems.
- Dedicate time to solving a variety of pandas problems.
Join a peer-to-peer mentoring group for Python data science.
Show steps
Mentoring will solidify your understanding of concepts and provide opportunities to refine your communication skills.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find a peer mentoring group or organize one with classmates.
- Take turns presenting concepts, answering questions, and providing feedback.
Develop a data visualization portfolio
Show steps
Creating a portfolio of data visualizations will demonstrate your proficiency in using Matplotlib and Seaborn.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Collect and prepare a dataset.
- Create various types of visualizations, such as bar charts, line charts, and scatterplots.
- Refine your visualizations based on feedback.
- Present your portfolio in a professional manner.
Build a data analysis project using real-world data
Show steps
Working on a real-world data analysis project will give you practical experience in applying the techniques learned in this course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a dataset and research a specific problem or opportunity.
- Clean and prepare the data.
- Apply appropriate data analysis techniques to extract insights.
- Develop a solution or recommendation based on your analysis.
- Present your findings to stakeholders.
Contribute to scikit-learn
Show steps
Contributing to scikit-learn will deepen your understanding of machine learning algorithms and open-source development.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Familiarize yourself with the scikit-learn codebase.
- Identify an area where you can make a meaningful contribution.
- Submit your code changes as a pull request.
- Collaborate with maintainers to refine your contribution.
- Get your contribution merged into the main scikit-learn repository.
Career center
Learners who complete 【2023年5月改訂版】実践 Python データサイエンス will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist applies scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. This course is foundational to becoming a data scientist by providing a grounding in core data science concepts like data analysis, data visualization and machine learning.
Data Analyst
Data Analyst
Data Analysts make sense of raw data to uncover patterns in order to make informed decisions. This course can help you become a successful data analyst by providing a comprehensive overview of data science topics, including data analysis, data visualization, and machine learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their knowledge of machine learning and software engineering to design, develop and maintain machine learning models. This course can help you become a machine learning engineer by providing a strong foundation in machine learning concepts, including linear regression, logistic regression, and support vector machines.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure that allows data scientists and data analysts to access and process data. This course can provide you with a strong foundation in data engineering concepts, including data wrangling, data transformation, and data storage.
Business Analyst
Business Analyst
Business Analysts use data to identify opportunities and solve problems for businesses. This course can provide you with the skills you need to become a business analyst, including data analysis, data visualization, and machine learning.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course can help you become a statistician by providing a strong foundation in statistical concepts, including data analysis, data visualization, and machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can provide you with the skills you need to become a quantitative analyst, including data analysis, data visualization, and machine learning.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course can provide you with the skills you need to become an actuary, including data analysis, data visualization, and machine learning.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can provide you with the skills you need to become a software engineer, including data analysis, data visualization, and machine learning.
Web Developer
Web Developer
Web Developers design, develop, and maintain websites. This course can provide you with the skills you need to become a web developer, including data analysis, data visualization, and machine learning.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use visual representations of data to communicate insights and trends. This course can provide you with the skills you need to become a data visualization specialist, including data analysis, data visualization, and machine learning.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. This course can provide you with the skills you need to become a database administrator, including data analysis, data visualization, and machine learning.
Systems Analyst
Systems Analyst
Systems Analysts design, implement, and maintain computer systems. This course can provide you with the skills you need to become a systems analyst, including data analysis, data visualization, and machine learning.
Information Security Analyst
Information Security Analyst
Information Security Analysts design, implement, and maintain security measures to protect information systems. This course can provide you with the skills you need to become an information security analyst, including data analysis, data visualization, and machine learning.
Forensic Analyst
Forensic Analyst
Forensic Analysts collect and analyze digital evidence to solve crimes. This course can provide you with the skills you need to become a forensic analyst, including data analysis, data visualization, and machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/zo62h0/[(Shi
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
【2023年5月改訂版】実践 Python データサイエンス.
この本には、Pythonによるデータサイエンスに役立つ幅広いトピックが網羅されており、NumPy、Pandas、Matplotlib、Scikit-learnなどのライブラリの使い方を学ぶことができます。
この本は、機械学習アルゴリズムとScikit-learn、Keras、TensorFlowなどのライブラリの使い方を学ぶのに役立つ、実践的なガイドです。
この本は、Pandasライブラリを使用して、データの読み込み、クリーニング、変換、視覚化する方法を学ぶのに役立ちます。
この本は、機械学習の基本コンセプトと、Scikit-learnライブラリを使用した実装について学ぶのに役立ちます。
この本は、Kerasライブラリを使用して、ディープラーニングモデルを作成してトレーニングする方法を学ぶのに役立ちます。
この本は、Scikit-learnライブラリを使用して、機械学習アルゴリズムを実装する方法を学ぶのに役立ちます。
この本は、NumPy、Pandas、Matplotlibなどのライブラリを使用せずに、Pythonでデータサイエンスを行う方法を学ぶのに役立ちます。
この本は、Pythonプログラミング言語の基本を学ぶのに役立ちます。
この本は、TensorFlowライブラリを使用して、ディープラーニングモデルを作成してトレーニングする方法を学ぶのに役立ちます。
このドキュメントは、Scikit-learnライブラリの詳細なリファレンスで、機械学習アルゴリズムの実装に役立ちます。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zo62h0/[(Shi
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