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【2023年5月改訂版】実践 Python データサイエンス

Shingo Tsuji and Pierian Data International by Jose Portilla

4.1

Based on 8,075 ratings

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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。

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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。

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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

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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

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Understanding python programming basics will help you learn the libraries covered in this course more effectively.

Browse courses on Python

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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'

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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

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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

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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

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Find tutorials covering advanced NumPy features, such as broadcasting and linear algebra.
Complete practice exercises to apply newly learned NumPy techniques.

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Complete additional pandas practice problems

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Practice is crucial for mastering pandas. Additional practice will enhance your ability to efficiently manipulate and analyze data.

Browse courses on Pandas

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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.

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Mentoring will solidify your understanding of concepts and provide opportunities to refine your communication skills.

Browse courses on Python

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Find a peer mentoring group or organize one with classmates.
Take turns presenting concepts, answering questions, and providing feedback.

Develop a data visualization portfolio

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Creating a portfolio of data visualizations will demonstrate your proficiency in using Matplotlib and Seaborn.

Browse courses on Data Visualization

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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

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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

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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

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Contributing to scikit-learn will deepen your understanding of machine learning algorithms and open-source development.

Browse courses on Machine Learning

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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

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Machine Learning 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Systems 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.

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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.

See salaries and explore the career path for Forensic Analyst

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.

Python Data Science Handbook

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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。

(English) Python Data Science Handbook: Essential Tools for...

【2023年5月改訂版】実践 Python データサイエンス

What's inside

Syllabus

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Save this course

Reviews summary

Pythonデータサイエンス実践入門

Activities

Career center

Reading list

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