この専門講座について

80,316 最近の表示

This is an action-packed specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. It appeals to anyone interested in pursuing a career in Data Science, and already has foundational skills (or has completed the Introduction to Applied Data Science specialization). You will learn Python - no prior programming knowledge necessary. You will then learn data visualization and data analysis. Through our guided lectures, labs, and projects you’ll get hands-on experience tackling interesting data problems. Make sure to take this specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.

Upon completing all courses in the specialization and receiving the Specialization certificate, you will also receive an IBM Badge recognizing you as a Specialist in Applied Data Science.

LIMITED TIME OFFER: Subscription is only $39 USD per month and gives you access to graded materials and a certificate.

受講生の就業成果
45%
この専門講座終了後に新しいキャリアをスタートしました
11%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンラインコース
自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール
柔軟性のある期限の設定および維持
初級レベル
約5か月で修了
推奨5時間/週
英語
字幕:英語, 韓国語, ベトナム語, ドイツ語, アラビア語, トルコ語...
受講生の就業成果
45%
この専門講座終了後に新しいキャリアをスタートしました
11%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンラインコース
自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール
柔軟性のある期限の設定および維持
初級レベル
約5か月で修了
推奨5時間/週
英語
字幕:英語, 韓国語, ベトナム語, ドイツ語, アラビア語, トルコ語...

この専門講座には4コースあります。

コース1

コース 1

Python for Data Science and AI

4.6
17,166件の評価
2,637件のレビュー
コース2

コース 2

Pythonによるデータ分析

4.7
11,098件の評価
1,592件のレビュー
コース3

コース 3

Data Visualization with Python

4.6
7,648件の評価
1,022件のレビュー
コース4

コース 4

Applied Data Science Capstone

4.7
4,260件の評価
499件のレビュー

提供:

IBM ロゴ

IBM

よくある質問

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • The specialization consists of 4 courses. Suggested time to complete each course is 3-4 weeks. If you follow recommended timelines it would take 3 to 4 months to complete the entire specialization.

  • No prior experience in data science or programming is required. However it is recommended that you have some foundational knowledge about data science which can be developed by taking the the Introduction to Applied Data Science specialization by IBM.

  • It is strongly recommended that you take the Python for Data Science course first. Then you can take either the Visualization or the Data Science course - whichever you prefer. And end with the Captsone course.

  • No

  • You will be able to learn practical Python skills, and apply them to interesting Data Visualization and Data Analysis problems.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。