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人工知能プロジェクトのデータ分析認定(上級) (Advanced)

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Overview

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Learning outcomes

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Course content

1

データ前処理とクレンジング

2

特徴量エンジニアリング

3

探索的データ分析

4

統計的モデリング

5

機械学習アルゴリズム選定

6

深層学習モデル構築

7

モデル評価指標

8

ハイパーパラメータ最適化

9

データ可視化技法

10

ビジネスインパクト評価

11

データパイプライン設計

12

スケーラブルaiシステム

13

倫理と公平性評価

14

Aiプロジェクト管理

15

成果物ドキュメンテーション

16

パフォーマンスモニタリング

17

継続的学習とモデル更新

18

クラウドaiサービス活用

19

データガバナンスとコンプライアンス

20

プレゼンテーションとステークホルダーコミュニケーション

Career Path

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Key facts

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Why this course

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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
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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
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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 States
MC
Michael Carter
US · Course completed

The Advanced AI Project Data Analysis certification exceeded my expectations. The curriculum was tightly aligned with my goal of leading AI‑driven product teams. I especially appreciated the module on time‑series forecasting with Prophet, which I immediately applied to our sales‑prediction pipeline, cutting forecast errors by 12%. The case studies were current and the downloadable Jupyter notebooks made it easy to replicate every technique. Overall, the course material was high‑quality, the instructors were responsive, and I feel fully equipped to design end‑to‑end data workflows for AI projects.

LS
Lucas Silva
BR · Course completed

Fiquei muito satisfeito com o curso! Ele ajudou a alcançar meu objetivo de melhorar a análise de dados nos projetos de IA da minha startup. Aprendi a montar pipelines de dados usando Apache Airflow e a aplicar técnicas de feature engineering que aumentaram a acurácia dos nossos modelos em 8%. O material didático foi bem organizado e os vídeos curtos facilitam o estudo. A experiência de aprendizado foi prática e divertida, e já estou usando o que aprendi no dia a dia.

FW
Felix Wagner
DE · Course completed

Wow, what an energizing experience! This course gave me exactly the tools I needed to turn raw data into AI‑ready datasets. The hands‑on labs on clustering with t‑SNE and model explainability with SHAP were eye‑opening, and I could immediately implement them in a recent fraud‑detection project, reducing false positives by 15%. The course materials are top‑notch—clear slides, real‑world datasets, and up‑to‑date references. I left feeling confident and truly inspired to tackle bigger AI challenges.

HT
Haruki Tanaka
JP · Course completed

この上級コースは、私の学習目標であったAIプロジェクトのデータ前処理と評価指標の深い理解に直結しました。特に、データ品質チェックリストと欠損値補完のベストプラクティスを学び、社内の画像認識プロジェクトでデータクリーニング時間を30%短縮できました。教材は豊富なコード例と最新の研究論文へのリンクがあり、実務にすぐ活かせます。全体的に、実践的で満足度の高い学習体験でした。





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Recently updated!

May 2026