Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the foundational skills needed to excel in data science by mastering Python and popular libraries like NumPy, Pandas, Matplotlib, and Seaborn. This course provides hands-on experience with Python basics, data manipulation, and visualization techniques, all essential for building a strong foundation in data science. Whether you're a beginner or looking to refine your skills, you will gain the confidence to perform advanced data handling and visualization tasks. The journey begins with an introduction to Python programming, covering essential concepts such as variables, conditionals, loops, and functions. Next, dive into data handling with NumPy, learning to manipulate arrays, perform mathematical operations, and reshape data efficiently. Explore Pandas for advanced data manipulation, including Series and DataFrames, and learn how to clean and transform data to make informed decisions. Finally, you will immerse yourself in data visualization, using Matplotlib and Seaborn to create compelling visual representations of data, from simple line graphs to complex heatmaps. By the end of the course, you'll have a robust understanding of Python's data science ecosystem, empowering you to tackle real-world problems with data. This course is ideal for beginners in data science or anyone looking to gain a practical understanding of Python for data analysis. No prior programming experience is required. If you're curious about the world of data and want to get started with Python, this course will be a valuable resource to kickstart your learning journey.

Syllabus

BONUS - Python Crash Course

In this module, we will explore the fundamental concepts of Python programming, focusing on variables, conditional statements, loops, and functions. You will also dive into containers like lists, tuples, sets, and dictionaries, understanding their features and practical applications in Python. This crash course provides a solid foundation to get started with Python programming, enhancing your coding skills for data science.

Data Handling using NumPy

In this module, we will focus on the powerful NumPy library to handle and manipulate large datasets. You will learn to create, modify, and perform various mathematical operations on arrays. Additionally, you'll explore techniques for reshaping arrays and generating random and identity matrices, laying the groundwork for advanced data manipulation.

Data Handling using Pandas

In this module, we will dive into the Pandas library, one of the most crucial tools for data analysis. You will explore Series and DataFrames, the key data structures, and learn how to perform statistical operations, modify data, and filter data effectively. Additionally, you will get hands-on experience with advanced features such as concatenation and boolean indexing.

Data Visualization using Matplotlib in Python

In this module, we will explore Matplotlib, a comprehensive library for data visualization. You will learn to create various types of plots such as line, bar, scatter, and pie charts. Additionally, you'll explore advanced visualization methods like 3D plotting, focusing on how to present your data visually to uncover meaningful insights.

Data Visualization using Seaborn in Python

In this module, we will introduce Seaborn, a powerful statistical visualization library built on top of Matplotlib. You will learn how to create intricate plots, such as swarm plots and violin plots, to better understand data distributions and relationships. Additionally, you will discover the power of facet grids and heatmaps for multi-variable analysis.