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Adwith Malpe
and
Steven Osburn
In this course, you will learn how to read and write data from and to a file. You will also examine how to manipulate and analyze the data using lists, tuples, dictionaries, sets, and the pandas and Matplot libraries.
As a developer, it's important to understand how to deal with issues that could cause an application to crash. You will learn how to implement exceptions to handle these issues.
Read more
In this course, you will learn how to read and write data from and to a file. You will also examine how to manipulate and analyze the data using lists, tuples, dictionaries, sets, and the pandas and Matplot libraries.
As a developer, it's important to understand how to deal with issues that could cause an application to crash. You will learn how to implement exceptions to handle these issues.
Read more
In this course, you will learn how to read and write data from and to a file. You will also examine how to manipulate and analyze the data using lists, tuples, dictionaries, sets, and the pandas and Matplot libraries.
As a developer, it's important to understand how to deal with issues that could cause an application to crash. You will learn how to implement exceptions to handle these issues.
You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!
Register for this course and see more details by visiting:
OpenCourser.com/course/nj877o/data
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What's inside
Syllabus
Module 1: Sequences
In this module, we will discuss lists, tuples, dictionaries and sets.
Module 2: File Input and Output
In this module, you will explore how to read in data from a file, store information to a file, and modify a file.
Read more
Syllabus
Module 1: Sequences
In this module, we will discuss lists, tuples, dictionaries and sets.
Module 2: File Input and Output
In this module, you will explore how to read in data from a file, store information to a file, and modify a file.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides a solid foundation in programming concepts and data manipulation for novice learners
Taught by instructors with expertise in their field, ensuring high-quality content
Students will learn essential data analysis skills using industry-standard libraries and tools
Covers a wide range of data structures, providing a comprehensive understanding of data organization
Introduces exception handling for managing errors and ensuring code robustness
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Reviews summary
Foundational python data analysis
According to learners, this course is an excellent starting point for those new to programming or data analysis with Python. Many would find it exceptionally clear and accessible, appreciating the step-by-step approach to core concepts like Python data structures, file input/output, and error handling. The emphasis on practical application through libraries like pandas and Matplotlib would be a major strength, equipping students with foundational skills for manipulating and visualizing data. While it provides a solid introduction, some learners with prior programming experience might find the pace slow or the depth of coverage limited, as it focuses strictly on beginner-level concepts and might not delve into advanced data science techniques.
Emphasizes hands-on application of concepts and libraries.
"I'd appreciate the practical examples and the opportunities to apply what I learned immediately."
"The course would help me understand how to actually use Python for data manipulation, not just theoretical concepts."
"I would feel confident in applying Python for basic data analysis tasks after completing the course's exercises."
Provides a strong base in Python for data analysis.
"I would gain a solid understanding of manipulating data using lists, dictionaries, and file I/O operations."
"This course would give me practical skills in using the pandas and Matplotlib libraries for basic data tasks and visualization."
"It would provide an excellent starting point for learning how to analyze and visualize data effectively with Python."
Exceptionally clear and accessible for new programmers.
"This course would be perfect for me as someone entirely new to Python and coding!"
"I had no programming background and found the explanations of core concepts incredibly clear and easy to follow."
"It's truly open to anyone, making complex topics like data structures and exceptions very approachable for a beginner."
May be too basic or slow for learners with prior experience.
"For someone with existing Python knowledge, this course might feel a bit too slow or foundational."
"I might hope for more advanced topics or complex projects than what is covered in this introductory course."
"It's clearly a foundational course, so don't expect deep dives into highly advanced data science techniques or niche applications."
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 Data Analysis and Visualization with Python with these
activities:
Review Python Basics
Show steps
Refreshes understanding of Python fundamentals, ensuring a strong foundation for the course.
Browse courses on
Python
Show steps
-
Review online resources or tutorials on Python basics.
-
Take practice quizzes or exercises to test your knowledge.
Review 'Python Crash Course' by Eric Matthes
Show steps
Provides a comprehensive review of Python fundamentals, complementing the course content.
View
Python Crash Course, 3rd Edition: A Hands-On,...
on Amazon
Show steps
-
Read the book.
-
Take notes and summarize key concepts.
-
Complete the practice exercises in the book.
Join a Study Group
Show steps
Facilitates collaboration and discussion, fostering a deeper understanding of course concepts.
Show steps
-
Find fellow students interested in forming a study group.
-
Set regular meeting times.
-
Discuss course material, share insights, and work on assignments together.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Reading and Writing Files
Show steps
Reinforces understanding of file operations by providing practical exercises.
Browse courses on
File Handling
Show steps
-
Open a file for reading or writing.
-
Read data from the file line by line.
-
Write data to the file.
-
Close the file.
Create a New Pandas DataFrame from Scratch
Show steps
Provides hands-on experience in creating and manipulating data structures in Python.
Browse courses on
Pandas
Show steps
-
Import the Pandas library.
-
Create a new DataFrame using the DataFrame() constructor.
-
Specify the column names and data types.
-
Populate the DataFrame with data.
-
Print the DataFrame to console to verify its contents.
Follow Matplotlib Tutorials
Show steps
Enhances understanding of data visualization techniques through guided tutorials.
Browse courses on
Data Visualization
Show steps
-
Find online tutorials or documentation for Matplotlib.
-
Follow the tutorials to create different types of visualizations.
-
Experiment with different options and parameters.
-
Apply the learned techniques to real-world data.
Attend a Python Workshop
Show steps
Provides hands-on experience and exposure to advanced Python techniques.
Browse courses on
Python
Show steps
-
Find and register for a Python workshop.
-
Attend the workshop and actively participate.
-
Practice the concepts learned in the workshop.
Build a Simple Data Analysis Dashboard
Show steps
Enhances data analysis skills by applying the concepts learned in the course to a practical project.
Browse courses on
Data Visualization
Show steps
-
Gather and prepare the data.
-
Choose appropriate visualizations (e.g., charts, graphs).
-
Create the dashboard using a visualization tool (e.g., Tableau, Power BI).
-
Share the dashboard and gather feedback.
Review Python Basics
Show steps
Refreshes understanding of Python fundamentals, ensuring a strong foundation for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review online resources or tutorials on Python basics.
- Take practice quizzes or exercises to test your knowledge.
Review 'Python Crash Course' by Eric Matthes
Show steps
Provides a comprehensive review of Python fundamentals, complementing the course content.
View
Python Crash Course, 3rd Edition: A Hands-On,...
on Amazon
Show steps
Show steps
Show steps
- Read the book.
- Take notes and summarize key concepts.
- Complete the practice exercises in the book.
Join a Study Group
Show steps
Facilitates collaboration and discussion, fostering a deeper understanding of course concepts.
Show steps
Show steps
Show steps
- Find fellow students interested in forming a study group.
- Set regular meeting times.
- Discuss course material, share insights, and work on assignments together.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Reading and Writing Files
Show steps
Reinforces understanding of file operations by providing practical exercises.
Browse courses on
File Handling
Show steps
Show steps
Show steps
- Open a file for reading or writing.
- Read data from the file line by line.
- Write data to the file.
- Close the file.
Create a New Pandas DataFrame from Scratch
Show steps
Provides hands-on experience in creating and manipulating data structures in Python.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Import the Pandas library.
- Create a new DataFrame using the DataFrame() constructor.
- Specify the column names and data types.
- Populate the DataFrame with data.
- Print the DataFrame to console to verify its contents.
Follow Matplotlib Tutorials
Show steps
Enhances understanding of data visualization techniques through guided tutorials.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find online tutorials or documentation for Matplotlib.
- Follow the tutorials to create different types of visualizations.
- Experiment with different options and parameters.
- Apply the learned techniques to real-world data.
Attend a Python Workshop
Show steps
Provides hands-on experience and exposure to advanced Python techniques.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find and register for a Python workshop.
- Attend the workshop and actively participate.
- Practice the concepts learned in the workshop.
Build a Simple Data Analysis Dashboard
Show steps
Enhances data analysis skills by applying the concepts learned in the course to a practical project.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather and prepare the data.
- Choose appropriate visualizations (e.g., charts, graphs).
- Create the dashboard using a visualization tool (e.g., Tableau, Power BI).
- Share the dashboard and gather feedback.
Career center
Learners who complete Data Analysis and Visualization with Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst collects, organizes, and interprets data to help businesses make informed decisions. This course provides a solid foundation in data manipulation and analysis techniques, which are essential skills for Data Analysts. The course covers topics such as data cleaning, data transformation, data visualization, and statistical analysis, which are all commonly used by Data Analysts in their daily work.
Data Scientist
Data Scientist
A Data Scientist uses data to build models that can predict future outcomes or identify trends. This course provides a strong foundation in data analysis and visualization techniques, which are essential skills for Data Scientists. The course covers topics such as data mining, machine learning, and deep learning, which are all commonly used by Data Scientists in their daily work.
Business Analyst
Business Analyst
A Business Analyst uses data to help businesses improve their operations. This course provides a solid foundation in data analysis and visualization techniques, which are essential skills for Business Analysts. The course covers topics such as data mining, data modeling, and data presentation, which are all commonly used by Business Analysts in their daily work.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data to help businesses understand their customers. This course provides a strong foundation in data analysis and visualization techniques, which are essential skills for Market Researchers. The course covers topics such as survey design, data analysis, and data presentation, which are all commonly used by Market Researchers in their daily work.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. This course provides a strong foundation in data analysis and visualization techniques, which are essential skills for Financial Analysts. The course covers topics such as financial modeling, data analysis, and data presentation, which are all commonly used by Financial Analysts in their daily work.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for Software Engineers who want to learn more about data analysis and visualization techniques. The course covers topics such as data structures, algorithms, and software design, which are all commonly used by Software Engineers in their daily work.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data pipelines. This course may be useful for Data Engineers who want to learn more about data analysis and visualization techniques. The course covers topics such as data warehousing, data mining, and data visualization, which are all commonly used by Data Engineers in their daily work.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course may be useful for Database Administrators who want to learn more about data analysis and visualization techniques. The course covers topics such as database design, data optimization, and data security, which are all commonly used by Database Administrators in their daily work.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. This course may be useful for Statisticians who want to learn more about data visualization techniques. The course covers topics such as statistical modeling, data analysis, and data presentation, which are all commonly used by Statisticians in their daily work.
Actuary
Actuary
An Actuary uses data to assess risk and uncertainty. This course may be useful for Actuaries who want to learn more about data analysis and visualization techniques. The course covers topics such as probability, statistics, and financial modeling, which are all commonly used by Actuaries in their daily work.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes of disease and other health problems. This course may be useful for Epidemiologists who want to learn more about data analysis and visualization techniques. The course covers topics such as data collection, data analysis, and data presentation, which are all commonly used by Epidemiologists in their daily work.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to medical research. This course may be useful for Biostatisticians who want to learn more about data analysis and visualization techniques. The course covers topics such as clinical trials, data analysis, and data presentation, which are all commonly used by Biostatisticians in their daily work.
Economist
Economist
An Economist studies the production, distribution, and consumption of goods and services. This course may be useful for Economists who want to learn more about data analysis and visualization techniques. The course covers topics such as econometrics, data analysis, and data presentation, which are all commonly used by Economists in their daily work.
Sociologist
Sociologist
A Sociologist studies human society and social behavior. This course may be useful for Sociologists who want to learn more about data analysis and visualization techniques. The course covers topics such as social research, data analysis, and data presentation, which are all commonly used by Sociologists in their daily work.
Psychologist
Psychologist
A Psychologist studies the mind and behavior. This course may be useful for Psychologists who want to learn more about data analysis and visualization techniques. The course covers topics such as psychological research, data analysis, and data presentation, which are all commonly used by Psychologists in their daily work.
For more career information including salaries, visit:
OpenCourser.com/course/nj877o/data
Reading list
We've selected nine 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
Data Analysis and Visualization with Python.
Provides a comprehensive guide to data visualization with Python and Matplotlib, a popular Python library for data visualization. It is particularly useful for those who want to learn how to create high-quality data visualizations.
Provides a comprehensive guide to data analysis with Python, including data manipulation, analysis, and visualization. It is particularly useful for those who want to learn the fundamentals of data analysis with Python.
Provides a comprehensive overview of machine learning with Python, including supervised learning, unsupervised learning, and deep learning. It is particularly useful for those who want to learn the fundamentals of machine learning with Python.
Provides a comprehensive overview of natural language processing with Python, including text preprocessing, text classification, and text generation. It is particularly useful for those who want to learn the fundamentals of natural language processing with Python.
Provides a comprehensive overview of deep learning with Python, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is particularly useful for those who want to learn the fundamentals of deep learning with Python.
Provides a comprehensive overview of Python for finance, including financial data analysis, financial modeling, and financial risk management. It is particularly useful for those who want to learn the fundamentals of finance with Python.
Provides a comprehensive overview of Python for web development, including web frameworks, web servers, and web security. It is particularly useful for those who want to learn the fundamentals of web development with Python.
Save
Provides a comprehensive overview of Python for software development, including software design, software testing, and software deployment. It is particularly useful for those who want to learn the fundamentals of software development with Python.
Provides a comprehensive overview of Python for big data, including data storage, data processing, and data analysis. It is particularly useful for those who want to learn the fundamentals of big data with Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nj877o/data
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Effort
16 hours to complete
Level
Introductory
Via
Coursera
Institution
Arizona State University
Instructors
Adwith Malpe
Steven Osburn
Language
English
This course belongs to a
collection
called
Python: A Guided Journey from Introduction to Application. It's comprised of three courses:
- Data Analysis and Visualization with Python (viewing)
- Introduction to Machine Learning with Python
- Object-Oriented Programming and GUI with Python
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