260€
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The demand for data analysis skills is soaring, and Python stands at the forefront as a powerful tool for unlocking insights from complex datasets. Our Course on Data Analysis with Python is designed to equip you with the essential skills needed to excel in this thriving field. You'll learn to harness Python's capabilities to manipulate, analyse, and visualise data effectively. With the increasing reliance on data-driven decisions across industries, employers are actively seeking professionals who can transform raw data into actionable insights. By enrolling in this course, you position yourself at the leading edge of a booming industry, ready to meet the demands of a data-centric world. Our engaging online format ensures you can learn at your own pace, making it easier than ever to acquire these in-demand skills and enhance your career prospects. Join us to become a sought-after expert in data analysis with Python.
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Para qué te prepara
The Course on Data Analysis with Python prepares you to tackle complex data challenges with confidence. You will learn to manipulate and analyse large datasets, draw meaningful insights, and make data-driven decisions. By mastering Python libraries like Pandas, NumPy, and Matplotlib, you will enhance your ability to create visualisations and interpret statistical results, equipping you to effectively contribute to data-driven projects in any professional context.
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.
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.
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
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
Titulación
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