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Columbus, United States · Study online with LSBA

Certificado En Análisis De Datos De Proyectos De Inteligencia Artificial

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Overview

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Learning outcomes

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Course content

1

Fundamentos De Inteligencia Artificial

2

Estadística Descriptiva Para Ia

3

Minería De Datos En Proyectos De Ia

4

Visualización De Datos Con Herramientas Ia

5

Modelado Predictivo Y Aprendizaje Automático

6

Evaluación De Modelos De Ia

7

Ética Y Gobernanza De Datos En Ia

8

Implementación De Pipelines De Datos

9

Optimización De Rendimiento De Modelos

10

Presentación De Resultados Y Toma De Decisiones

Career Path

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Key facts

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Why this course

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Everything you need to know before you start

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We offer immediate access to our course materials through our open enrollment system. This means:

  • The course starts as soon as you pay the course fee, instantly
  • No waiting periods or fixed start dates
  • 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
Open enrolment · Start today

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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.

4.8
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 Certificado En Análisis De Datos De Proyectos De Inteligencia Artificial perfectly aligned with my goal of leading AI‑driven projects at my firm. The modules on data preprocessing and model evaluation gave me hands‑on experience with Python’s Pandas and Scikit‑learn libraries, which I immediately applied to a client’s predictive maintenance project. The course materials are up‑to‑date, well‑structured, and the case studies reflect real‑world challenges. I finished the program feeling confident and fully prepared for my new role.

CS
Carlos Silva
BR · Course completed

Adorei o curso! Eu queria entender como transformar dados de projetos de IA em insights acionáveis, e o conteúdo entregou exatamente isso. As aulas práticas de visualização com Tableau me ajudaram a criar dashboards que já usei para apresentar resultados ao meu time. O material didático é claro e os exercícios são bem focados. Saí do programa com novas habilidades que já estou usando no dia a dia.

FW
Felix Wagner
DE · Course completed

Wow, what an inspiring experience! This certification gave me the tools to design end‑to‑end AI data pipelines—from data cleaning to model deployment. I especially loved the hands‑on labs where we built a recommendation system using TensorFlow, which I later showcased in a hackathon and won first place. The lectures were engaging, the resources were top‑notch, and the community support was fantastic. I feel completely equipped to drive AI projects at my company.

RK
Rahul Kapoor
IN · Course completed

The course provided a comprehensive, step‑by‑step roadmap for analyzing data in AI projects. I was able to master advanced techniques such as feature engineering and hyper‑parameter tuning, which I applied to optimize a churn‑prediction model for my startup, improving accuracy by 12%. The reference materials, including the detailed slide decks and supplemental Python notebooks, were extremely thorough. Overall, the learning experience was rigorous yet accessible, and I am extremely satisfied with the outcomes.





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Recently updated!

May 2026