Published 10/2025
Created by IT Phd Rocio Chavez
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English + subtitle | Duration: 114 Lectures ( 8h 6m) | Size: 2.32 GB
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 beginningThroughout this course, you will learn—simply and step by step—the basics of Python and everything necessary to clean and preprocess data like a professionalWhat will you learn?Fundamentals of Python and data structuresHow to install and work with Jupyter Lab and Anaconda PromptTechniques for importing, exploring, and transforming CSV, Excel, JSON files, and moreWorking with key data structures such as lists, dictionaries, arrays, series, and DataFramesCleaning null, duplicate, and incorrect dataTo detect outliers using different techniquesPreprocessing to leave your data ready for models and analysisIn addition, each video includes downloadable scripts and example files so you can practice directly in your own environment, without needing to type everything from scratchThis course is ideal for:People who are just starting in data scienceStudents in tech- or business-related fieldsProfessionals who need to process data but don’t come from a technical backgroundThroughout 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 codeWe will cover both basic data cleaning techniques and more sophisticated methods used in machine learning__________________________________________________________________________________¿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 datosCómo instalar y trabajar con Jupyter Lab y Anaconda PromptTécnicas para importar, explorar y transformar archivos CSV, Excel, JSON y másTrabajo con estructuras de datos clave como listas, diccionarios, arreglos, series y DataFramesLimpieza de datos nulos, duplicados e incorrectosDetección de valores atípicos utilizando diferentes técnicasPreprocesamiento para dejar tus datos listos para modelos y análisisAdemás, 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 datosEstudiantes de áreas relacionadas con tecnología o negociosProfesionales que necesitan procesar datos pero no provienen de un trasfondo técnicoA 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.
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