【2023年5月改訂版】実践 Python データサイエンス from Udemy

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。

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。

インストールとセットアップ

Jupyter Notebookの使い方

学習を進めるための資料

numpyを使ったベクトルや行列の操作を学びます。

numpy入門

アレイを作る

アレイを使った計算

アレイの添え字

行と列の入れ替え

アレイと計算のための関数

アレイを使ったデータ処理

アレイのの入出力

pandaを使ったデータ処理の方法を学びます。

Series（1次元のデータ列）

DataFrame（テーブル型のデータ）

indexの基本

indexを変える

行や列を削除する

データを取り出す

形の違うデータの計算

データの並べ替えと順番

データと統計量

欠損値の扱い

indexの階層構造

データを読み込み、様々なフォーマットで保存する方法を学びます。

テキストデータの読み書き

JSON

HTMLからのデータの取り出し

Excel形式のファイルを読み込む

データの変形や具体的な処理方法を学びます。

データのマージ

indexを使ったマージ

データの連結

DataFrameを組み合わせる

SeriesとDataFrameの変換

ピボットテーブルの作り方

重複したデータの処理

マッピングを使った列の追加

置換

indexの変更

ビニング（Binning）

外れ値

Permutation

ばらばらなデータセットを1つのセットにまとめます。

データをまとめるGroupBy

データをまとめるGroupByその2

データのAggregation

Split、Apply、Combining

クロス集計表

データを表現するための可視化手法を学びます。

seabornのインストール

ヒストグラム

カーネル密度推定

分布の可視化

ボックスプロットとバイオリンプロット

回帰とプロット

ヒートマップとクラスタリング

ここまで学んできた技術を実際のデータ解析に生かしていきましょう。

実践データ解析（導入）

実践データ解析（準備）

タイタニックその1

タイタニックその2

タイタニックその3

タイタニックその4

株式市場のデータ解析入門

株式市場その1

株式市場その2

株式市場その3

株式市場その4

株式市場その5

選挙とデータ解析

選挙その1

選挙その2

選挙その3

選挙その4

scikitlearnを使った機械学習アルゴリズムの利用

機械学習入門

線形回帰その1

線形回帰その2

線形回帰その3

線形回帰その4

ロジスティック回帰その1

ロジスティック回帰その2

ロジスティック回帰その3

ロジスティック回帰その4

多クラス分類その1：ロジスティック回帰

多クラス分類その2：k近傍法

サポートベクトルマシン（SVM）その1

サポートベクトルマシン（SVM）その2

ナイーブベイズ分類その1

ナイーブベイズ分類その2

決定木とランダムフォレスト

Pythonの全体像をおおまかに掴みます

Pythonの基礎その1

Pythonの基礎その2

Pythonの基礎その3

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

マーケティング実務に役立つpython基礎

学習者によると、このコースはマーケティング実務の分析に役立つPythonの基礎を習得するのに役立つわかりやすい講座です。

Python初心者でもわかりやすい

"pythonをほぼ初めて触ったのですが、"

マーケティング実務の分析に役立つスキルが身につく

"マーケティング実務の分析に活かせる基礎を着実に身に着けられる講義でした。"

基礎からわかりやすく解説されている

"とてもわかり易かったです。"

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.

Five other activities

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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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Statistician

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.

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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.

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