Overview

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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. In this comprehensive Data Science with Python course, you will master essential libraries such as NumPy, Pandas, Matplotlib, and PyTorch to solve real-world data science challenges. Starting with NumPy, you’ll learn how to work with arrays, perform linear algebra, and manipulate large datasets. You’ll then explore Pandas to filter, analyze, and visualize data efficiently, followed by Matplotlib for creating informative plots and visualizations that uncover patterns in data. As you progress, you will dive into advanced image processing techniques with Matplotlib, build interactive plots using Plotly, and gain hands-on experience with PyTorch fundamentals. The course will guide you through essential concepts like tensors, GPU acceleration, broadcasting, and model training, offering a solid foundation for machine learning and deep learning tasks. Designed for individuals eager to advance their data science skills, this course is ideal for beginners and intermediate learners. With practical exercises, real-world applications, and interactive lessons, you'll be prepared to tackle any data science project. Upon completion, you'll be ready to take your skills further in the field of machine learning and artificial intelligence. By the end of the course, you will be able to manipulate data with NumPy and Pandas, visualize data using Matplotlib and Plotly, process images, and implement machine learning models using PyTorch.

Syllabus

NumPy

In this module, we will explore NumPy, a powerful library for numerical computing in Python. You'll learn how to create and reshape arrays, perform element-wise operations, and dive into linear algebra applications. Advanced topics such as solving linear systems and logical filtering will also be covered.

Pandas

In this module, we will work with Pandas to process and analyze real-world datasets. You’ll learn how to filter data, manage missing values, and manipulate columns. The Titanic dataset will serve as an example to practice essential Pandas operations and data wrangling techniques.

Matplotlib, Graphing, and Statistics

In this module, we will cover data visualization using Matplotlib. You will create a wide range of plots, from simple bar charts to complex 3D surface plots, and learn how to enhance them with annotations, colors, and advanced graphing techniques for statistical analysis.

Matplotlib and Image Processing

In this module, we will combine Matplotlib and NumPy to process images. You’ll learn how to manipulate image channels, apply basic transformations like grayscale conversion and thresholding, and experiment with advanced techniques like image compression and tiling for creative results.

Plotly and Interactive Plots

In this module, we will explore Plotly to create interactive and visually engaging plots. You’ll build line and scatter plots with tooltips, experiment with 3D visualizations, and learn how to customize plots using advanced techniques like graph objects and dictionary-based figure construction.

PyTorch Fundamentals

In this module, we will introduce you to PyTorch, a powerful deep learning framework. You will learn how to work with tensors, perform various tensor operations, and explore the concepts of broadcasting. Additionally, you will discover how to use GPU acceleration to optimize performance for large-scale computations.