Data Understanding and Data Visualization with Python

Packt via Coursera Specialization

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Overview

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This specialization 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 specialization. You will begin by mastering Python fundamentals, including data structures, strings, and key programming concepts essential for data manipulation and analysis. Next, you’ll gain expertise in data handling with NumPy and Pandas. The specialization then focuses on data visualization, teaching you how to present data effectively using Matplotlib, Seaborn, Bokeh, Plotly, and Folium. You’ll create everything from simple plots to interactive geographical maps, reinforcing your learning through quizzes, challenges, and projects. This specialization is ideal for beginners starting their career in data science or enhancing their skills. While prior programming experience is helpful, it’s not required. By the end, you'll be able to manipulate data with Pandas, visualize datasets using various plotting techniques, work with complex numerical operations in NumPy, and create interactive visualizations using modern tools. You'll be prepared to analyze and interpret data, adding value to any data-driven field.

Syllabus

Course 1: Python Foundations for Data Handling
Course 2: Data Processing and Exploration with NumPy & Pandas
Course 3: Data Visualization and Storytelling with Python

Courses

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9 hours 28 minutes
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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. Dive into the world of data manipulation and numerical analysis with NumPy and Pandas, two of the most essential tools in the data science toolkit. Learn how to manipulate, process, and analyze large datasets with ease and efficiency. Whether you're working with numerical data, handling missing values, or exploring data through visualization, this course will equip you with the skills you need to tackle a wide range of data tasks. The course is divided into two major sections—NumPy and Pandas—each offering focused lessons on core concepts. You’ll start by learning the fundamentals of NumPy for numerical computing, including array creation, reshaping, and advanced operations such as broadcasting and universal functions. Then, you will delve into Pandas, exploring Series and DataFrame objects, handling missing data, and mastering powerful functions like groupby, merge, and pivot tables. This course is perfect for anyone looking to enhance their data processing and analysis skills. Whether you're an aspiring data scientist, business analyst, or just someone who works with data regularly, this course will provide you with the tools to work efficiently and effectively. No prior programming experience is required, but basic familiarity with Python will be helpful. The course is suitable for beginners to intermediate learners, with a focus on practical applications and hands-on exercises. By the end of the course, you will be able to work with large datasets, perform complex data manipulations, use NumPy for numerical operations, and confidently analyze data with Pandas. You’ll also gain expertise in data cleaning, reshaping, merging, and creating advanced data visualizations to inform data-driven decisions.
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8 hours 20 minutes
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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. Dive into the world of data visualization and learn to effectively communicate data insights using popular Python libraries. You will explore a wide range of tools such as Matplotlib, Seaborn, Bokeh, Plotly, and Folium to craft stunning visualizations and interactive plots. This course will equip you with the skills needed to visualize data across different domains—from simple 2D plots to complex 3D and geographic visualizations. Throughout the course, you will start by learning the basics of Matplotlib, including customizing plots, adding markers, and managing axis limits. Then, you will move into advanced topics like creating subplots, contour plots, and even 3D plots. You will also explore Seaborn for high-level visualization, including relational and categorical plots, heatmaps, and more. As you advance, you'll interact with tools like Bokeh and Plotly to make your plots interactive, and finally, you’ll learn to create geographic maps with Folium to visualize real-world data such as COVID-19 statistics. This course offers an interactive journey where each concept builds upon the last. You'll apply what you learn in quizzes and real-world projects, allowing you to strengthen your skills progressively. You'll also benefit from hands-on projects, such as working with 3D surface plots and geographical data, which will allow you to test your skills and deepen your understanding. This course is designed for individuals who are looking to enhance their data visualization skills with Python. It is ideal for beginners to intermediate learners in data science, computer science, or anyone looking to better present and interpret data insights. Familiarity with Python programming basics is recommended but not required. By the end of the course, you will be able to create various types of visualizations—from simple line plots to complex interactive 3D scatter plots and geographical maps—and understand how to choose the right type of plot for your data and audience.
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5 hours 8 minutes
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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 course, you will explore the foundations of Python, focusing on key data handling techniques essential for real-world applications. By learning how to work with Python’s powerful libraries, you will become proficient in handling, manipulating, and visualizing data. You will gain a deep understanding of Python data structures, including lists, dictionaries, and strings, and how to apply them in data-related tasks. The course is structured to start with the basics, introducing Python strings and methods before moving into more advanced topics like data structures and object manipulation. You will get hands-on experience with data operations in Python, including insertion, deletion, and slicing, followed by quizzes to reinforce the concepts learned. Throughout the course, you will practice problem-solving techniques and explore abstract concepts that enhance your ability to work with complex data structures in Python. This course is ideal for beginners in Python programming who are interested in data analysis and handling. No prior programming experience is required, but a basic understanding of mathematics and logic will be helpful. The difficulty level is beginner, making it accessible to anyone new to programming or Python. By the end of the course, you will be able to manipulate and handle data structures efficiently, apply string operations, and utilize Python libraries to create data visualizations. You will also gain the ability to solve complex data handling problems using Python.