Data Analysis and Visualisation with Python

Learn data analysis with Python

Data analysis is at the heart of data science, helping organisations uncover insights that drive better decisions. On this three-week course, you’ll learn how to collect, prepare, and analyse data using Python while developing practical skills used by data professionals across industries.

You’ll begin by exploring web scraping techniques to collect data from online sources before learning how to prepare datasets for analysis. Along the way, you’ll gain experience with the workflows and tools used to turn raw data into meaningful business insights.

Prepare data for analysis with confidence

High-quality analysis starts with well-prepared data. You’ll explore essential data preprocessing techniques, including handling missing values, scaling features, and encoding categorical data to create clean, reliable datasets.

These practical skills will help you understand how data is prepared for analysis and modelling, giving you the confidence to work with real-world datasets.

Create compelling data visualisations with Python

Learn how to communicate insights through clear and engaging data visualisation using two of Python’s most widely used libraries: Matplotlib and Seaborn.

You’ll create a range of visualisations before applying data storytelling principles to combine multiple visuals into audience-ready reports. You’ll also conduct exploratory data analysis to identify trends, patterns, and relationships that support data-driven decision-making.

Gain career-ready skills from experienced data science professionals at Edureka

Throughout the course, you’ll learn from the experts at Edureka to develop the data analysis and data visualisation skills employers value. With this knowledge, you’ll be prepared for future roles in data science, data analytics, and business intelligence.

This course is designed for aspiring data professionals, software developers, analysts, engineers, and business professionals who want to build practical data science skills with Python. No prior data science or machine learning experience is required, though basic computer literacy and comfort with logical problem-solving will be helpful.

It is ideal for career changers moving into data roles, working professionals looking to add analytics capabilities to their current role, and graduates preparing for data analyst, data scientist, or machine learning engineer positions. It is also valuable for managers and domain experts who want to understand how data-driven decisions are made.

This course is designed for aspiring data professionals, software developers, analysts, engineers, and business professionals who want to build practical data science skills with Python. No prior data science or machine learning experience is required, though basic computer literacy and comfort with logical problem-solving will be helpful.

It is ideal for career changers moving into data roles, working professionals looking to add analytics capabilities to their current role, and graduates preparing for data analyst, data scientist, or machine learning engineer positions. It is also valuable for managers and domain experts who want to understand how data-driven decisions are made.

• Explain key principles of responsible data collection, preprocessing, visualisation, and exploratory data analysis.
• Apply Python techniques to collect, clean, scale, encode, and prepare data for analysis.
• Investigate datasets using exploratory analysis and statistical visualisation to identify patterns, relationships, and data-quality issues.
• Evaluate appropriate charts, visualisation techniques, and reporting approaches for different analytical questions and audiences.
• Create clear, audience-ready visual reports and data stories that communicate evidence-based insights effectively.