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via Udemy |
Go to Course: https://www.udemy.com/course/analisis-de-datos-con-python-y-sqlite/
Certainly! Here's a well-written review and recommendation of the Coursera course based on the provided details: --- **Course Review: Data Analysis with Python and SQLite on Coursera** If you're interested in developing a solid foundation in data analysis using Python and SQLite, this course is an excellent choice. Designed for beginners and intermediate learners, it provides a comprehensive introduction to essential data analysis concepts, practical skills, and tools. **Course Content and Learning Outcomes** Throughout this course, participants will learn: - Core concepts related to data analysis using Python and SQLite. - How to install and set up the Python IDE and the SQLite DB Browser for graphical database management. - Techniques to structure and handle data effectively with Python. - File management skills, including converting CSV files into SQLite tables, which is critical for importing and organizing datasets. - Methods to clean data efficiently using SQL commands like `Replace`. - Using the SQLite DB Browser interface for selecting and managing data visually. - Writing specific SQL queries for column selection and data filtering to perform precise analyses. - Installing and utilizing key libraries such as `sqlite3` and `matplotlib` in Python. - Enhancing data analysis with Python lists for more flexible data manipulation. - Creating diverse visualizations: bar charts, pie charts, and scatter plots to interpret data visually. - Employing SQL clauses in conjunction with Python to optimize data retrieval, filtering, and analysis. The course's main project involves analyzing a CSV file containing footballers' data. Participants will learn how to convert this dataset into a SQLite database, perform selections, clean data, and visualize insights effectively. This hands-on approach ensures that learners gain practical skills they can apply to real-world datasets. **Review & Recommendations** This course is highly recommended for those looking to harness the power of Python combined with SQLite for data analysis. Its step-by-step instruction, combined with practical exercises, makes complex topics accessible. The integration of SQL queries with Python scripting is particularly valuable, enabling learners to write efficient, targeted data analysis workflows. Whether you are a student, data enthusiast, or professional looking to expand your skills, this course offers a robust foundation in data handling, cleaning, and visualization. The use of real-world data (footballers' dataset) makes the learning process engaging and immediately applicable. **Final Verdict:** A highly practical, well-structured course that bridges Python programming, SQL data management, and data visualization. I confidently recommend it for anyone aiming to improve their data analysis skills with a hands-on approach. --- Let me know if you'd like me to tailor the review for a specific audience or add more details!
En este curso aprenderán:Conceptos inherentes al análisis de datos con Python y SQliteInstalar el IDE de programación PythonInstalar el manejador gráfico de base de datos SQlite DB BrowserUsar Python para estructurar los datosManejar y convertir archivos.CSV ("valores separados por coma") a tablas de SQliteLimpiar datos utilizando el método Replace de SQLUsar la interfaz SQlite DB BrowserSeleccionar datos por columnas Instalar y usar las librerías SQlite3 y MatplotlibUsar Listas de Python para ampliar y mejorar el análisis Crear consultas especificas para la selección de datosGraficar usando gráfico tipo barra, circular y dispersiónUtilizar clausulas de SQL para seleccionar los datos necesarios de forma optima En este curso usaremos un archivo.CSV de futbolistas, el cual analizaremos y convertiremos en una tabla de base de datos SQlite, que nos permitirá en conjunto con Python seleccionar los datos por columnas y a su vez poder usar toda la sintaxis SQL y de Python para limpiar y analizar en base a requerimientos nuestro conjunto de datos.No solo usaremos clausulas o comandos de SQL, también usaremos sintaxis Python para maximizar nuestro campo de selección en relación a nuestro conjunto de datos, permitiendo así obtener datos con valor de conclusión y graficando los mismos, para una mejor visualización.