360€
306€
-15% (hasta el 30/04/2026)
* Becas y descuentos no aplicables a formación programada
- Presentación
- Temario
- Metodología
- Titulación
Descripción
¿A quién va dirigido?
This course is aimed at professionals and graduates in the field who are eager to enhance or refresh their knowledge of data analysis using Python. Ideal for those with a foundational understanding of data science, it provides practical skills and insights into Python's powerful tools and libraries, enabling participants to efficiently analyse and interpret data.
Objetivos
- To understand the basics of Python for data analysis. - To learn to manipulate datasets using Python libraries. - To develop skills in data visualisation techniques. - To master data cleaning and preprocessing methods. - To apply statistical analysis using Python. - To explore data science tools and frameworks. - To gain proficiency in writing Python scripts for analysis.
Salidas Profesionales
- Data analyst in tech companies - Marketing data strategist - Financial analyst using Python - Data visualisation specialist - Python programmer for data insights - Business intelligence consultant - Healthcare data analyst - E-commerce data optimisation expert - Government data researcher - Machine learning assistant in data science projects
Temario del Course on Data Analysis with Python
UNIT 1. Introduction to Data Analysis
- What Is Data Analysis?
UNIT 2. Libraries for Data Analysis: NumPy, Pandas and Matplotlib
- Data Analysis with NumPy
- Pandas
- Matplotlib
UNIT 3. Filtering and Data Mining
- How to Use loc in Pandas
- How to Delete a Column in Pandas
UNIT 4. Pivot Tables
- Pivot Tables in Pandas
UNIT 5. GroupBy and Aggregate Functions
- The Pandas Group
UNIT 6. DataFrame Merge
- Merging DataFrames with Python Pandas
UNIT 7. Data Visualisation with Matplotlib and Seaborn
- Matplotlib
- 2.Seaborn
UNIT 8. Introduction to Machine Learning
- Machine Learning
UNIT 9. Linear Regression and Logistic Regression
- Linear Regression
- Logistic Regression
UNIT 10. Decision Tree
- Tree Structure
UNIT 11. Naive Bayes
- Naive Bayes Algorithm
- Types of Naive Bayes
UNIT 12. Support Vector Machines (SVMs)
- Introduction to Support Vector Machines
- How Do SVMs Work?
- SVM Kernels
- Building a Classifier with Scikit-Learn
UNIT 13. KNN Algorithm
- K-Nearest Neighbours (KNN)
- Python Implementation of KNN Algorithm
UNIT 14. Principal Component Analysis (PCA)
- Principal Component Analysis: Definition and Steps
UNIT 15. Random Forest
- Random Forest Algorithm
Metodología
EDUCA LXP se basa en 6 pilares
Item
Titulación del Course on Data Analysis with Python
Degree Issued and Endorsed by INESEM Business School. “Non-Official Education and Not Leading to the Award of an Official Degree or Certificate of Professionalism”.
INESEM Business School se ocupa también de la gestión de la Apostilla de la Haya, previa demanda del estudiante. Este sello garantiza la autenticidad de la firma del título en los 113 países suscritos al Convenio de la Haya sin necesidad de otra autenticación. El coste de esta gestión es de 65 euros. Si deseas más información contacta con nosotros en el 958 050 205 y resolveremos todas tus dudas.
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Course on Data Analysis with Python
360€
306€
360€
306€