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Análise De Dados De Projeto De IA

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Overview

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Learning outcomes

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Course content

1

Fundamentos De Dados Em Ia

2

Preparação E Limpeza De Dados

3

Análise Exploratória De Dados

4

Modelagem Estatística E Machine Learning

5

Visualização E Comunicação De Resultados

Career Path

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Key facts

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Why this course

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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
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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 "Análise De Dados De Projeto De IA" course at Stanmore School of Business exceeded my expectations. The curriculum was perfectly aligned with my goal of mastering data pipelines for AI projects. I especially appreciated the hands‑on labs where we built a complete data‑preprocessing workflow using Python and pandas, which I have already applied to a client project. The lecture slides were clear, up‑to‑date, and included real‑world case studies that made the theory immediately relevant. Overall, the learning experience was professional, well‑structured, and highly satisfying.

LS
Lucas Silva
BR · Course completed

Fiquei muito feliz com o curso "Análise De Dados De Projeto De IA" da Stanmore. Ele me ajudou a alcançar meu objetivo de entender como preparar dados para modelos de aprendizado de máquina. Na prática, aprendi a usar o Power Query para limpar dados de sensores e a criar dashboards interativos no Power BI, o que já estou usando no meu trabalho. O material do curso é bem organizado e cheio de exemplos reais, o que facilita o aprendizado. Saí do curso com confiança e pronto para aplicar tudo no meu dia a dia.

FW
Felix Wagner
DE · Course completed

Wow, what an enthusiastic journey! The "Análise De Dados De Projeto De IA" course blew me away with its dynamic content. I wanted to learn how to turn messy datasets into AI‑ready inputs, and the weekly projects let me do exactly that—like the time I built a real‑time data stream using Kafka and Spark. The videos were crisp, the reading material was spot‑on, and the instructor’s feedback was always encouraging. I left feeling pumped and already recommending the course to all my colleagues.

HT
Haruki Tanaka
JP · Course completed

The "Análise De Dados De Projeto De IA" course offered a detailed and comprehensive exploration of data engineering for AI. My learning goal was to acquire end‑to‑end skills, from data acquisition to model deployment, and the course delivered precisely that. I particularly valued the module on feature engineering, where I learned to implement advanced techniques such as target encoding and dimensionality reduction using scikit‑learn. The provided Jupyter notebooks were meticulously annotated, and the supplementary reading list included recent research papers that kept the content cutting‑edge. The overall experience was rigorous yet rewarding, and I feel fully equipped to lead AI data projects in my organization.





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Recently updated!

May 2026