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Curso gratuito English Certificado verificado Beginner #data science #data analysis #sql #python #data #science #work

Data Science Foundations with Python

Learn the basics of data science with Python in a practical, step-by-step way. You will explore data, clean and visualize datasets, apply simple statistics, and build your first machine learning models using real business examples.

4.3 (3 valoraciones)
140 estudiantes inscritos
546 visualizaciones
25 lessons
~45 min en total
A tu ritmo, acceso de por vida

¿De qué trata este curso?

This beginner-friendly course introduces working professionals to the core ideas and workflows of data science using Python. You will learn how to think like a data scientist, work with data in Python, explore and clean datasets, create visualizations, perform basic statistical analysis, and build your first simple machine learning models. The course is practical, hands-on, and centered on real-world business examples so learners can quickly connect concepts to everyday work. Each module begins with a short introduction, and each lesson includes clear takeaways, key terms, and 1-2 practical exercises that can be completed in 5-15 minutes.

¿Cuáles son los datos clave de este curso?

Precio Gratis
Certificado Certificado verificado
Duración 45 min
Lecciones 25
Idioma English
Nivel Beginner
Examen A tu ritmo, acceso de por vida

Programa

5 modules · 25 lessons

1
Module 1: Getting Started with Data Science and Python
5 lessons
  • What Data Science Is and Why It Matters
  • The Data Science Workflow
  • Setting Up Python for Data Work
  • Python Syntax Basics for Data Tasks
  • Working Safely and Effectively in Notebooks
2
Module 2: Working with Data in Python
5 lessons
  • Introducing Pandas and DataFrames
  • Importing and Exporting Data
  • Inspecting Data Structure and Quality
  • Selecting, Filtering, and Sorting Data
  • Basic Data Types and Cleaning Checks
3
Module 3: Cleaning and Preparing Data
5 lessons
  • Handling Missing Data
  • Removing Duplicates and Correcting Inconsistencies
  • Working with Dates and Times
  • Creating New Columns and Simple Features
  • Preparing a Clean Dataset for Analysis
4
Module 4: Exploring Data with Descriptive Analysis and Visualization
5 lessons
  • Descriptive Statistics Made Simple
  • Grouping and Comparing Categories
  • Introduction to Data Visualization
  • Reading Patterns, Trends, and Outliers
  • Communicating Insights Clearly
5
Module 6: Intro to Machine Learning and Next Steps
5 lessons
  • What Machine Learning Is
  • Preparing Data for a Simple Model
  • Building a First Model in Python
  • Evaluating Model Performance
  • Putting It All Together and Continuing the Journey

Certificado de finalización

Al terminar este curso desbloquearás un certificado verificado — descargable en PDF, con número único y enlace de verificación pública.

Cada certificado tiene un enlace verificable único y un código QR.

Preguntas frecuentes

Is this course free to start?

Yes. You can start learning this course for free.

Who is this course for?

This course is designed for learners who want practical, job-relevant skills and a structured path from basics to application.

Do I need prior experience?

No prior experience is required unless specific prerequisites are listed in the course requirements section.

How long does this course take?

The estimated total study time for this course is 45 min.

How does the certificate work?

Tras finalizar este curso podrás descargar un certificado verificado con un número único y enlace público de verificación.

Opiniones de los estudiantes

4.3
3 valoraciones
5
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4
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L
Laura C. Verificado · 30 abr. 2026

Useful Python basics

The Python data cleaning examples were easy to follow. I just wish the stats section went a little slower.

D
Daniel P. Verificado · 10 ene. 2026

I finally got how to use pandas to clean a messy dataset and make a quick chart from it. The step-by-step workflow felt really practical.

J
James W. Verificado · 13 jun. 2025

Hands-on and clear

The part on building a simple machine learning model stood out, especially how it started with exploring the data first. I would've liked a few more practice exercises, but the real-world examples helped a lot.

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