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データサイエンス専門能力証明 (Advanced)

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Overview

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Learning outcomes

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Course content

1

高度データ前処理

2

大規模データストレージ設計

3

ディープラーニングアーキテクチャ

4

強化学習応用

5

時系列予測モデリング

6

自然言語処理高度技術

7

画像認識と生成モデル

8

異常検知アルゴリズム

9

モデル解釈と説明可能ai

10

データパイプライン自動化

11

クラウドベース機械学習運用

12

エッジデバイスai実装

13

バイアスと公平性評価

14

高性能計算とgpu活用

15

データプライバシーと匿名化

16

ビジネスインテリジェンス統合

17

Aiプロジェクトマネジメント

18

実験設計とabテスト

19

モデルデプロイと継続的学習

20

データサイエンス倫理と法規制

Career Path

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Key facts

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Why this course

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  • Instant access to all course materials upon payment
  • Flexibility to begin at your convenience

This self-paced approach allows you to begin your professional development journey immediately, fitting your learning around your existing commitments.

We offer two flexible learning paths to suit your schedule:

  • Fast Track: Complete in 1 month with 3-4 hours of study per week
  • Standard Mode: Complete in 2 months with 2-3 hours of study per week

You can progress at your own pace and access the materials 24/7.

There are no formal entry requirements for this course. You just need:

  • A good command of English language
  • Access to a computer/laptop with internet
  • Basic computer skills
  • Dedication to complete the course
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Assessment is done through:

  • Multiple-choice questions at the end of each unit
  • You need to score at least 60% to pass each unit
  • You can retake quizzes if needed
  • All assessments are online

Upon successful completion, you will receive:

  • A digital certificate from London School of Business and Administration
  • Option to request a physical certificate
  • Transcript of completed units
  • Certification is included in the course fee
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Why people choose us for their career

Trusted by professionals worldwide

Verified outcomes from learners who finished the course and put it to work.

4.5
Based on 4 learner reviews · 4 countries
98%
Would recommend
100%
Verified learners
2026
Cohort active
Completed from United Kingdom
ST
Sarah Thompson
GB · Course completed

I took the Advanced Data Science certification because I wanted to move from basic reporting to building AI solutions. The course was surprisingly practical – the week‑long sprint on time‑series forecasting using Prophet helped me automate monthly sales predictions at my firm. The video lessons were clear and the downloadable notebooks made it easy to follow along. I especially appreciated the industry‑focused examples, like the churn‑prediction project for a telecom client. While the workload was intense, the support from tutors kept me on track. All in all, it was a solid investment in my career and I can now speak confidently about deep‑learning pipelines.

MC
Michael Carter
US · Course completed

The データサイエンス専門能力証明 (Advanced) course at Stanmore School of Business exceeded my expectations. I enrolled to deepen my machine‑learning expertise for a senior analyst role, and the curriculum delivered exactly that. The modules on ensemble methods and neural network optimization gave me the confidence to redesign our predictive models, resulting in a 12% increase in forecast accuracy. The hands‑on labs using Python's scikit‑learn and TensorFlow were exceptionally well‑structured, and the accompanying case studies mirrored real‑world business problems. The instructors provided prompt, detailed feedback on every project, which helped me refine my code and presentation skills. Overall, the course material was current, the platform was reliable, and I feel fully prepared to lead data‑driven initiatives.

HT
Haruki Tanaka
JP · Course completed

このコースは本当に最高でした!スタンモア・スクールのデータサイエンス専門能力証明(Advanced)で、実務にすぐ使えるスキルが身につきました。特に、SQLとPythonを組み合わせたデータ前処理の実践演習は、現在のプロジェクトで大活躍しています。Kaggleコンペ形式の課題で、画像分類モデルを自分で構築し、精度を90%以上に向上させたことが自信になりました。教材は最新の研究成果を反映していて、実例が日本のビジネスシーンに合わせて解説されている点も嬉しいです。講師陣のフィードバックが迅速で丁寧なので、学びのスピードがとても速く感じました。

ZD
Zanele Dlamini
ZA · Course completed

I approached the Advanced Data Science program with a clear goal: to lead a data‑analytics team in my consultancy. The course delivered a detailed roadmap from data wrangling to model deployment. The section on feature engineering using pandas gave me concrete techniques that I applied to a client’s customer segmentation project, cutting the analysis time in half. The video lectures were comprehensive, and the supplementary reading list included recent papers on explainable AI, which enriched my understanding of model interpretability. The capstone project, which required deploying a Flask API for a predictive model on AWS, was challenging but immensely rewarding. The only minor drawback was the pacing of the live Q&A sessions, which sometimes conflicted with my time zone, but overall the learning experience was thorough and highly relevant.





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Recently updated!

May 2026