この専門講座について

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With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.

This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy.

This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work.

To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.

受講生の就業成果
50%
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
20%
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
中級レベル
約10か月で修了
推奨2時間/週
英語
字幕:英語, アラビア語
受講生の就業成果
50%
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
20%
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
中級レベル
約10か月で修了
推奨2時間/週
英語
字幕:英語, アラビア語

この専門講座には8コースあります。

コース1

コース 1

Introduction to Genomic Technologies

4.6
2,597件の評価
411件のレビュー
コース2

コース 2

Genomic Data Science with Galaxy

3.7
725件の評価
210件のレビュー
コース3

コース 3

Python for Genomic Data Science

4.3
1,019件の評価
184件のレビュー
コース4

コース 4

Algorithms for DNA Sequencing

4.8
575件の評価
133件のレビュー

提供:

ジョンズ・ホプキンズ大学(Johns Hopkins University) ロゴ

ジョンズ・ホプキンズ大学(Johns Hopkins University)

業界パートナーのいずれかのロゴ

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.

  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 6-9 months.

  • Each course in the specialization is offered monthly.

  • Some background in statistics, computer science, and biology is helpful but not required.

  • The sequence shown above is strongly recommended.

  • No, you will not earn credit at Johns Hopkins University by completing this specialization.

  • Upon completion, you will be able to use a variety of tools to conduct analysis of data generated by next generation sequencing experiments.

  • Pre-enrollment will be available on January 11, 2016.

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。