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Charles Russell Severance

In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.

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What's inside

Syllabus

Welcome to the Capstone
Congratulations to everyone for making it this far. Before you begin, please view the Introduction video and read the Capstone Overview. The Course Resources section contains additional course-wide material that you may want to refer to in future weeks.
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Explores the fundamentals of data visualization with Python, focusing on data retrieval, processing, and visualization techniques
Provides hands-on experience through projects that involve building a search engine, exploring data sources, and visualizing email data
Suitable for learners with prior experience in Python as covered in the earlier courses of the specialization
Taught by Charles Russell Severance, an expert in data science and visualization
Requires familiarity with Python 3 for effective participation
Utilizes an optional project for learners to apply their skills to a data source of their choice, fostering independent exploration

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Reviews summary

Python capstone: data applications

According to learners, this capstone course is a highly rewarding culmination of the Python for Everybody specialization. Many found the hands-on projects and practical application of Python for data tasks like retrieval, processing, and visualization to be extremely valuable. The course structure, including optional projects and Honors assignments, is praised for providing flexibility and deeper engagement for those seeking it. While the material is considered a challenging yet fulfilling wrap-up, most students feel it effectively solidifies concepts from previous courses, providing actionable skills.
Offers flexibility for deeper study.
"The optional Honors assignments provide a chance to dig much deeper if you have time."
"Appreciate the flexibility of the personal project being optional but highly recommended."
"The course structure allows you to choose your level of engagement."
"Great having the option to do the extra work for more learning."
Demanding but provides significant learning.
"The course is challenging, especially the projects, but incredibly rewarding."
"Some parts felt difficult, requiring significant effort, but mastering them was satisfying."
"Definitely not easy, but the learning gained is substantial."
"Requires dedication, but the outcome is worth the hard work put in."
Effectively ties together prior learning.
"This course brings together all the knowledge from the previous courses very well."
"It's a perfect culmination of the Python for Everybody specialization."
"Really helped solidify my understanding of the concepts learned previously and apply them."
"Finally, seeing how all the pieces fit together for real applications was great."
Focuses on applicable data skills.
"I learned how to use practical tools and strategies that I could apply immediately to my work."
"Getting experience with APIs, databases, and visualization libraries is very relevant."
"The skills taught here are directly useful for data analysis tasks."
"This course equips you with actionable skills for retrieving, processing, and visualizing data."
Provides practical, real-world experience.
"The hands-on coding and projects are the strongest part of the course for me."
"I loved the project where I could find my own data set and work with it."
"Building a search engine and working with email data felt very practical and insightful."
"Learned a lot by actually building something end-to-end."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Capstone: Retrieving, Processing, and Visualizing Data with Python with these activities:
Review and Organize Course Resources
Ensure a solid foundation for your learning by organizing and reviewing course materials before the course commences.
Show steps
  • Review the course syllabus and objectives
  • Download and skim through the assigned readings and resources
  • Organize your notes and study materials in a systematic manner
Review Statistics Concepts
Revisit the fundamental principles of statistics, which are essential for understanding data analysis techniques.
Browse courses on Statistics
Show steps
  • Review lecture notes or online resources on basic statistics
  • Solve practice problems to test your understanding
  • Engage in discussion forums or study groups to clarify concepts
Review Python 3 Basics
Reinforce your understanding of Python syntax, variables, and data types, which are essential for success in this course.
Browse courses on Python Syntax
Show steps
  • Review online resources for Python 3 fundamentals
  • Complete practice exercises on variables, data types, and operators
  • Take a practice quiz to assess your understanding
Five other activities
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Show all eight activities
Discussion on Data Sources
Engage in peer discussions to broaden your understanding of data sources and their relevance to data analysis.
Browse courses on Data Sources
Show steps
  • Participate in online forums or discussion groups related to data sources
  • Organize a study group with peers to discuss different types of data sources and their applications
  • Share your own experiences and insights on data source identification and selection
Coding Exercises on Building Search Engines
Enhance your coding proficiency in web crawling and scraping techniques, which are crucial for building search engines.
Browse courses on Web Crawling
Show steps
  • Find online coding challenges or platforms that offer exercises on building search engines
  • Attend a coding session or workshop on search engine development
  • Implement a mini-search engine using Python and compare its performance with existing search algorithms
Leveraging Data Visualization Tools
Gain practical experience with data visualization techniques using Matplotlib and Seaborn, which are essential for presenting data insights.
Browse courses on Data Visualization
Show steps
  • Follow interactive tutorials on Matplotlib and Seaborn to learn the basics of data visualization
  • Explore online resources and documentation to discover advanced data visualization techniques
  • Create your own interactive data visualizations using real-world datasets
Visualizing Email Data
Develop practical skills in using Python for data analysis and visualization, including text extraction and processing.
Browse courses on Data Visualization
Show steps
  • Collect and clean email data using Python and the provided scripts
  • Explore the data to identify patterns and trends
  • Visualize the data using techniques covered in the course
Contribute to Python Data Analysis Projects
Gain hands-on experience in practical data analysis by contributing to open-source projects on GitHub.
Browse courses on Open Source
Show steps
  • Identify open-source projects related to Python data analysis
  • Review the project's documentation and codebase
  • Contribute to the project by fixing bugs, adding features, or improving documentation

Career center

Learners who complete Capstone: Retrieving, Processing, and Visualizing Data with Python will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists analyze large amounts of structured and unstructured data to extract meaningful insights, develop predictive models, and make data-driven decisions. This course provides a foundation in Python programming, data retrieval, processing, and visualization, all of which are essential skills for Data Scientists. By completing this course, you will gain a competitive edge in the job market and be well-equipped to contribute to data-driven decision-making in various industries.
Data Analyst
Data Analysts collect, clean, and interpret data to identify patterns, trends, and insights that can inform business decisions. This course provides a comprehensive overview of data retrieval, processing, and visualization techniques, which are fundamental skills for Data Analysts. By taking this course, you will develop the analytical and technical skills necessary to succeed in this role.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve complex business problems. This course provides a solid foundation in Python programming, data processing, and visualization, which are essential skills for Machine Learning Engineers. By completing this course, you will gain the technical expertise and practical experience needed to build and implement machine learning solutions.
Software Engineer, Data
Software Engineers (Data) specialize in developing software solutions for data-intensive applications. This course provides a comprehensive overview of data retrieval, processing, and visualization techniques, which are essential for building data-driven applications. By taking this course, you will gain the technical skills and knowledge necessary to succeed in this role.
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to communicate insights and trends. This course provides a thorough foundation in data visualization techniques, using Python and other tools. By completing this course, you will develop the skills and knowledge necessary to effectively convey data-driven insights to a wide range of audiences.
Data Journalist
Data Journalists use data to tell stories and inform the public. This course provides a comprehensive overview of data retrieval, processing, and visualization techniques, which are essential for Data Journalists. By taking this course, you will gain the skills and knowledge necessary to uncover and communicate data-driven insights in a compelling and engaging way.
Business Analyst
Business Analysts use data to identify and solve business problems. This course provides a foundation in data retrieval, processing, and visualization, which are essential skills for Business Analysts. By completing this course, you will gain the analytical and technical skills necessary to succeed in this role.
Market Researcher
Market Researchers collect and analyze data to understand market trends and consumer behavior. This course provides a foundation in data retrieval, processing, and visualization, which are essential skills for Market Researchers. By completing this course, you will gain the analytical and technical skills necessary to succeed in this role.
Financial Analyst
Financial Analysts use data to evaluate and make recommendations on financial investments. This course provides a foundation in data retrieval, processing, and visualization, which are essential skills for Financial Analysts. By completing this course, you will gain the analytical and technical skills necessary to succeed in this role.
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency and effectiveness of operations. This course provides a foundation in data retrieval, processing, and visualization, which are essential skills for Operations Research Analysts. By completing this course, you will gain the analytical and technical skills necessary to succeed in this role.
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions about populations. This course provides a foundation in data retrieval, processing, and visualization, which are essential skills for Statisticians. By completing this course, you will gain the analytical and technical skills necessary to succeed in this role.
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. This course provides a foundation in data retrieval, processing, and visualization, which are essential skills for Epidemiologists. By completing this course, you will gain the analytical and technical skills necessary to succeed in this role.
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. This course provides a foundation in data retrieval, processing, and visualization, which are essential skills for Biostatisticians. By completing this course, you will gain the analytical and technical skills necessary to succeed in this role.
Computer Scientist
Computer Scientists design and develop computer systems and applications. This course provides a foundation in Python programming, data retrieval, processing, and visualization, which are essential skills for Computer Scientists. By completing this course, you will gain the technical skills and knowledge necessary to succeed in this role.
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in Python programming, data retrieval, processing, and visualization, which are essential skills for Software Engineers. By completing this course, you will gain the technical skills and knowledge necessary to succeed in this role.

Featured in The Course Notes

This course is mentioned in our blog, The Course Notes. Read one article that features Capstone: Retrieving, Processing, and Visualizing Data with Python:

Reading list

We've selected 23 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Capstone: Retrieving, Processing, and Visualizing Data with Python.
Is the backbone for the capstone of the course, and covers Python 3.
Provides a comprehensive overview of the data science process, from data collection and cleaning to analysis and visualization. It valuable resource for anyone who wants to learn more about data science.
Provides a comprehensive introduction to data analysis using Python. It covers topics such as data cleaning, exploration, visualization, and modeling.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, from supervised learning to unsupervised learning to deep learning. It valuable resource for anyone who wants to learn more about machine learning.
Focuses on data visualization using Python. It covers topics such as creating charts, graphs, and maps, as well as how to effectively communicate data insights.
Classic textbook on statistical learning. It provides a comprehensive overview of the field, from linear regression to nonlinear regression to Bayesian methods. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a comprehensive overview of data mining. It covers a wide range of topics, from data preprocessing to data visualization to data mining algorithms. It valuable resource for anyone who wants to learn more about data mining.
Provides a comprehensive introduction to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive introduction to deep learning using Python. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks to generative adversarial networks. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of natural language processing. It covers a wide range of topics, from text classification to text generation to text summarization. It valuable resource for anyone who wants to learn more about natural language processing.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, from speech recognition to natural language understanding to speech synthesis. It valuable resource for anyone who wants to learn more about speech and language processing.
Comprehensive reference on natural language processing. It good choice for students who want to learn more about the latest advances in natural language processing.
Provides a gentle introduction to deep learning. It good choice for students who have no prior experience with deep learning.
Provides a gentle introduction to machine learning. It good choice for students who have no prior experience with machine learning.
Provides a comprehensive overview of computer networks. It covers a wide range of topics, from network protocols to network security to network performance. It valuable resource for anyone who wants to learn more about computer networks.
Provides a comprehensive overview of database systems. It covers a wide range of topics, from database design to database implementation to database administration. It valuable resource for anyone who wants to learn more about database systems.
Provides a comprehensive overview of information retrieval. It covers a wide range of topics, from text indexing to query processing to ranking algorithms. It valuable resource for anyone who wants to learn more about information retrieval.
Provides a comprehensive overview of discrete mathematics. It covers a wide range of topics, from set theory to graph theory to number theory. It valuable resource for anyone who wants to learn more about discrete mathematics.
Provides a comprehensive overview of algorithms. It covers a wide range of topics, from sorting algorithms to search algorithms to graph algorithms. It valuable resource for anyone who wants to learn more about algorithms.

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