Data Cleaning and Preprocessing using Python

Learn to Clean and Transform Your Data Step by Step

What you'll learn
- Fundamentals of programming in Python — data types, operators, data structures, and control flow structures.
- To detect and handle errors, remove duplicate records and handle missing values
- To adapt variables into suitable formats for analysis, ensuring coherence and compatibility among them
- To combine data from multiple sources into the same dataset
- Data reduction, which involves simplifying the dataset while keeping only the most relevant information
- To organize and structure the dataset correctly in a format compatible with the analysis or model to be developed
- How to apply both simple and advanced techniques to carry out these stages of data cleaning and preprocessing using Python

Requirements
- No programming experience nor statistical knwoledge needed

Description
Have you ever wanted to apply Machine Learning, create impactful visualizations, or generate solid reports… but realized that your data is messy, incomplete, or poorly structured?

This course was designed to help you avoid those obstacles from the very beginning

Throughout this course, you will learn—simply and step by step—the basics of Python and everything necessary to clean and preprocess data like a professional

What will you learn?

Fundamentals of Python and data structures

How to install and work with Jupyter Lab and Anaconda Prompt

Techniques for importing, exploring, and transforming CSV, Excel, JSON files, and more

Working with key data structures such as lists, dictionaries, arrays, series, and DataFrames

Cleaning null, duplicate, and incorrect data

To detect outliers using different techniques

Preprocessing to leave your data ready for models and analysis

In addition, each videoincludes downloadable scripts and example filesso you can practice directly in your own environment, without needing to type everything from scratch

This course is ideal for:

People who are just starting in data science

Students in tech- or business-related fields

Professionals who need to process data but don’t come from a technical background

Throughout the course, we’ll be using a carefully selected set of Python tools and libraries that are widely adopted in the data science industry such as Pandas, Numpy, scikit-learn, Matplotlib and Seaborn,using Jupyter Lab to document the entire process clearly and to debug our code

We will cover both basic data cleaning techniques and more sophisticated methods used in machine learning

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¿Alguna vez has querido aplicar Machine Learning, crear visualizaciones impactantes o generar reportes sólidos… pero te diste cuenta de que tus datos están desordenados, incompletos o mal estructurados?

Este curso fue diseñado para ayudarte a evitar esos obstáculos desde el principio.

A lo largo de este curso, aprenderás—de manera simple y paso a paso—los fundamentos de Python y todo lo necesario para limpiar y preprocesar datos como un profesional.

¿Qué aprenderás?

Fundamentos de Python y estructuras de datos

Cómo instalar y trabajar con Jupyter Lab y Anaconda Prompt

Técnicas para importar, explorar y transformar archivos CSV, Excel, JSON y más

Trabajo con estructuras de datos clave como listas, diccionarios, arreglos, series y DataFrames

Limpieza de datos nulos, duplicados e incorrectos

Detección de valores atípicos utilizando diferentes técnicas

Preprocesamiento para dejar tus datos listos para modelos y análisis

Además, cada video incluye scripts y archivos de ejemplo descargables para que puedas practicar directamente en tu propio entorno, sin necesidad de escribir todo desde cero.

Este curso es ideal para:

Personas que recién comienzan en ciencia de datos

Estudiantes de áreas relacionadas con tecnología o negocios

Profesionales que necesitan procesar datos pero no provienen de un trasfondo técnico

A lo largo del curso, usaremos un conjunto cuidadosamente seleccionado de herramientas y librerías de Python que son ampliamente adoptadas en la industria de la ciencia de datos, como Pandas, Numpy, scikit-learn, Matplotlib y Seaborn, utilizando Jupyter Lab para documentar todo el proceso de forma clara y depurar nuestro código.

Cubriremos tanto técnicas básicas de limpieza de datos como métodos más sofisticados usados en machine learning.

Who this course is for:
- Whether you are a student, a professional changing careers, or a data enthusiast, this course will guide you step by step through key programming concepts and powerful data manipulation techniques using pandas, numpy, scikit-learn, and more
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