Go to Course: https://www.coursera.org/learn/python-plotting
### Course Review: Applied Plotting, Charting & Data Representation in Python If you are looking to enhance your data visualization skills and gain a solid foundation in using Python's powerful matplotlib library, "Applied Plotting, Charting & Data Representation in Python" on Coursera is an invaluable course. With a practical and hands-on approach, this course deftly combines theory and application, making it suitable for beginners and intermediate learners alike. #### Overview The course offers a comprehensive introduction to information visualization principles, tailored specifically for data representation using Python. It focuses on effective reporting and charting techniques, paving the way for learners to create meaningful visualizations. Throughout the course, especially in the initial weeks, students explore both the artistic and scientific aspects of visual communication, discussing what makes visual data compelling and informative. #### Syllabus Breakdown **Module 1: Principles of Information Visualization** This module sets the stage by introducing key concepts in information visualization. It emphasizes design literacy and the importance of graphical heuristics. Learners engage with various principles of effective visualization, creating a strong foundation essential for the subsequent modules. The inclusion of grading, prerequisites, and course expectations in the syllabus helps learners navigate their journey ahead. **Module 2: Basic Charting** In the second week, students dive into the world of basic charting. The hands-on assignment utilizing real-world CSV weather data is particularly engaging, as it allows learners to manipulate and visualize data effectively. By creating line graphs to represent temperature fluctuations and overlaying scatter plots of record-breaking data, students can see firsthand the power of visual storytelling using matplotlib. **Module 3: Charting Fundamentals** This module accentuates charting fundamentals, where students are encouraged to innovate. The assignment allows for flexibility in choosing complexity; whether it’s a straightforward static image or an interactive chart, this module nurtures creativity and critical thinking. By implementing techniques based on academic research, learners develop a deeper understanding of visualization dynamics and best practices. **Module 4: Applied Visualizations** The final module synthesizes all the skills acquired throughout the course. In a culminating project titled "Becoming a Data Scientist," students identify publicly accessible datasets and formulate a research question. This assignment challenges learners to apply their knowledge practically by creating a relevant visualization with matplotlib. The requirement to justify how their visualizations address the research question sets a strong emphasis on critical analysis and reinforces the importance of data-driven storytelling. ### Why You Should Take This Course 1. **Practical Application**: The hands-on assignments using real-world data ensure that learners not only understand theoretical concepts but also apply them in practical scenarios. 2. **Skill Development**: The course equips you with vital skills in using one of the most popular libraries for data visualization, establishing a solid foundation for further exploration of data analysis. 3. **Flexible Learning**: With a mix of static and interactive visualizations, this course respects diverse learning styles and encourages creativity. 4. **Capstone Project**: The final project serves as a perfect opportunity to showcase the skills acquired, making it an excellent addition to your professional portfolio. 5. **Comprehensive Structure**: The thoughtfully designed modules guide learners from fundamental principles to applied visualizations, ensuring a coherent learning experience. ### Conclusion "Applied Plotting, Charting & Data Representation in Python" is an exceptional course for anyone looking to delve into the world of data visualization. Whether you are a data scientist in training or a professional seeking to enhance your reporting skills, this course provides critical insights into effective data visualization practices using Python. Highly recommended for anyone eager to turn complex datasets into meaningful visuals!
Module 1: Principles of Information Visualization
In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.
Module 2: Basic ChartingIn this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.
Module 3: Charting FundamentalsIn this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.
Module 4: Applied VisualizationsIn this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic c
Inspires you to create attractive visualisations with a balanced representation, while creating something what you really want, while actively suggesting to explore the API to get to that result.
Very helpful to understand what it takes to make a scientific and sensible visual. Recommended for someone who is interested in learning data visualization and does not have a background.
Loved the course! This course teaches you details about matplotlib and enables you to produce beautiful and accurate graphs.. Assignments are challanging, and helps to build a solid foundation.
Great course with lots of learning. The lectures were crisp and the course inspired us to look at materials beyond the course and in the internet which is a important skill for any data scientist
Each week for this course is fantastic, but where it really shines is in the final project, which gives you the freedom to apply the techniques you have learned to your interests/passions.