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David Dalsveen
In the field of data science, the volumes of data can be enormous, hence the term Big Data. It is essential that algorithms operating on these data sets operate as efficiently as possible. One measure used is called Big-O time complexity. It is often expressed not in terms of clock time, but rather in terms of the size of the data it is operating on. For example, in terms of an array of size N, an algorithm may take N^2 operations to complete. Knowing how to calculate Big-O gives the developer another tool to make software as good as it can be and provides a means to communicate performance when reviewing code with others.
Read more
In the field of data science, the volumes of data can be enormous, hence the term Big Data. It is essential that algorithms operating on these data sets operate as efficiently as possible. One measure used is called Big-O time complexity. It is often expressed not in terms of clock time, but rather in terms of the size of the data it is operating on. For example, in terms of an array of size N, an algorithm may take N^2 operations to complete. Knowing how to calculate Big-O gives the developer another tool to make software as good as it can be and provides a means to communicate performance when reviewing code with others.
Read more
In the field of data science, the volumes of data can be enormous, hence the term Big Data. It is essential that algorithms operating on these data sets operate as efficiently as possible. One measure used is called Big-O time complexity. It is often expressed not in terms of clock time, but rather in terms of the size of the data it is operating on. For example, in terms of an array of size N, an algorithm may take N^2 operations to complete. Knowing how to calculate Big-O gives the developer another tool to make software as good as it can be and provides a means to communicate performance when reviewing code with others.
In this course, you will analyze several algorithms to determine Big-O performance. You will learn how to visualize the performance using the graphing module pyplot.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/yubgp4/big
What's inside
Syllabus
Big-O Time Complexity in Python Code
In the field of data science, the volumes of data can be enormous, hence the term Big Data. It is essential that algorithms operating on these data sets operate as efficiently as possible. One measure used is called Big-O time complexity. It is often expressed not in terms of clock time, but rather in terms of the size of the data it is operating on. For example, in terms of an array of size N, an algorithm may take N^2 operations to complete. Knowing how to calculate Big-O gives the developer another tool to make software as good as it can be and provides a means to communicate performance when reviewing code with others.
In this course, you will analyze several algorithms to determine Big-O performance. You will learn how to visualize the performance using the graphing module pyplot.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines Big-O Time Complexity, which is standard in data science, software development, and the broader field of computer science
Taught by David Dalsveen, who are recognized for their work in Big-O Time Complexity
Teaches skills, knowledge, and tools that are highly relevant to data science
Develops Big-O performance analysis skills, which are core skills for software developers and data scientists
Offers hands-on labs and interactive materials that enhance the learning experience
Requires students to come in with Python programming knowledge as a prerequisite
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Save Big-O Time Complexity in Python Code to your list so you can find it easily later:
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Reviews summary
Big o for beginners
Learners say this guided project is good for beginners and helps them understand Big O notation in Python. However, some students who have already taken the course have complained that they have been asked to pay again to review the material.
The course is simple and straightforward, making it easy for beginners to understand the basics of Big O notation.
"A little too simple for most programmers, but a good course for beginners."
"This guided project has helped me understanding Big O notation in python, it's helpful to gain a fundamental understanding about this concept."
Students have complained about being asked to pay again to review the material after already taking the course.
"I do not recommend taking this type of course, take one and pass it, however after a few days I have tried to review the material, and my surprise is that it asks me to pay again to be able to review the material."
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 Big-O Time Complexity in Python Code with these
activities:
Review basic Python coding
Show steps
Review the basics of Python coding to ensure you have a strong foundation for this course and its programming concepts.
Browse courses on
Python
Show steps
-
Go over notes from an introductory Python course
-
Review Python data types, variables, operators, and control flow
-
Practice writing simple Python scripts
Participate in peer-to-peer study sessions
Show steps
Engage with fellow learners in peer-to-peer study sessions to discuss course concepts, solve problems, and share perspectives.
Show steps
-
Find or create a study group with other students taking this course
-
Meet regularly to discuss assignments, practice problems, and prepare for assessments
-
Collaborate on projects and share resources
Follow guided tutorials on Big-O time complexity
Show steps
Supplement your understanding of Big-O time complexity by following guided tutorials that provide step-by-step explanations and examples.
Show steps
-
Find tutorials on platforms like Coursera, edX, or YouTube
-
Watch video lectures and complete practice exercises
-
Discuss concepts with other learners in online forums
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete practice drills on Big-O time complexity
Show steps
Reinforce your understanding of Big-O time complexity through repetitive practice drills that test your ability to calculate and analyze algorithm performance.
Show steps
-
Find practice problems on websites like LeetCode or HackerRank
-
Solve problems by calculating Big-O time complexity
-
Compare your solutions with others and learn from different approaches
Create a visual representation of Big-O time complexity
Show steps
Deepen your understanding of Big-O time complexity by creating a visual representation, such as a graph or chart, that illustrates the asymptotic behavior of different algorithms.
Show steps
-
Choose a data structure or algorithm to visualize
-
Calculate Big-O time complexity for different input sizes
-
Create a graph or chart using tools like matplotlib or plotly
-
Analyze the visual representation and draw conclusions
Participate in coding competitions or hackathons related to Big-O time complexity
Show steps
Challenge yourself and test your skills in a competitive environment by participating in coding competitions or hackathons that focus on Big-O time complexity.
Show steps
-
Identify coding competitions or hackathons that align with course topics
-
Register and participate as an individual or team
-
Solve problems under time constraints and optimize code for efficiency
Contribute to open-source projects related to Big-O time complexity
Show steps
Gain practical experience and enhance your understanding by contributing to open-source projects that involve Big-O time complexity analysis or optimization.
Show steps
-
Find open-source projects on platforms like GitHub or SourceForge
-
Identify areas where you can contribute based on your skills and interests
-
Propose changes or improvements, write code, and submit pull requests
Review basic Python coding
Show steps
Review the basics of Python coding to ensure you have a strong foundation for this course and its programming concepts.
Browse courses on
Python
Show steps
Show steps
Show steps
- Go over notes from an introductory Python course
- Review Python data types, variables, operators, and control flow
- Practice writing simple Python scripts
Participate in peer-to-peer study sessions
Show steps
Engage with fellow learners in peer-to-peer study sessions to discuss course concepts, solve problems, and share perspectives.
Show steps
Show steps
Show steps
- Find or create a study group with other students taking this course
- Meet regularly to discuss assignments, practice problems, and prepare for assessments
- Collaborate on projects and share resources
Follow guided tutorials on Big-O time complexity
Show steps
Supplement your understanding of Big-O time complexity by following guided tutorials that provide step-by-step explanations and examples.
Show steps
Show steps
Show steps
- Find tutorials on platforms like Coursera, edX, or YouTube
- Watch video lectures and complete practice exercises
- Discuss concepts with other learners in online forums
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete practice drills on Big-O time complexity
Show steps
Reinforce your understanding of Big-O time complexity through repetitive practice drills that test your ability to calculate and analyze algorithm performance.
Show steps
Show steps
Show steps
- Find practice problems on websites like LeetCode or HackerRank
- Solve problems by calculating Big-O time complexity
- Compare your solutions with others and learn from different approaches
Create a visual representation of Big-O time complexity
Show steps
Deepen your understanding of Big-O time complexity by creating a visual representation, such as a graph or chart, that illustrates the asymptotic behavior of different algorithms.
Show steps
Show steps
Show steps
- Choose a data structure or algorithm to visualize
- Calculate Big-O time complexity for different input sizes
- Create a graph or chart using tools like matplotlib or plotly
- Analyze the visual representation and draw conclusions
Participate in coding competitions or hackathons related to Big-O time complexity
Show steps
Challenge yourself and test your skills in a competitive environment by participating in coding competitions or hackathons that focus on Big-O time complexity.
Show steps
Show steps
Show steps
- Identify coding competitions or hackathons that align with course topics
- Register and participate as an individual or team
- Solve problems under time constraints and optimize code for efficiency
Contribute to open-source projects related to Big-O time complexity
Show steps
Gain practical experience and enhance your understanding by contributing to open-source projects that involve Big-O time complexity analysis or optimization.
Show steps
Show steps
Show steps
- Find open-source projects on platforms like GitHub or SourceForge
- Identify areas where you can contribute based on your skills and interests
- Propose changes or improvements, write code, and submit pull requests
Career center
Learners who complete Big-O Time Complexity in Python Code will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help organizations make informed decisions. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize data processing algorithms and ensure efficient data analysis.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable data science solutions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models to solve real-world problems. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize machine learning algorithms and ensure efficient model training and deployment.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable machine learning solutions.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure and pipelines. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize data processing algorithms and ensure efficient data pipelines.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable data engineering solutions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize algorithms and data structures to improve the performance and efficiency of your software applications.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable software solutions.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help organizations understand their customers, products, and operations. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize data processing algorithms and ensure efficient data analysis.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable data analysis solutions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize financial modeling algorithms and ensure efficient data analysis.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable financial modeling solutions.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks for organizations. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize risk analysis algorithms and ensure efficient risk assessment.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable risk analysis solutions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage financial risk. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize actuarial modeling algorithms and ensure efficient risk assessment.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable actuarial solutions.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions and advise clients. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize financial modeling algorithms and ensure efficient data analysis.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable financial analysis solutions.
Business Analyst
Business Analyst
Business Analysts use data to understand business problems and develop solutions. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize data analysis algorithms and ensure efficient data-driven decision-making.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable business analysis solutions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize optimization algorithms and ensure efficient problem-solving.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable operations research solutions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help organizations make informed decisions. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize statistical analysis algorithms and ensure efficient data analysis.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your code, enabling you to develop more efficient and scalable statistical analysis solutions.
Data Science Manager
Data Science Manager
Data Science Managers lead teams of data scientists and are responsible for the overall success of data science projects. This course can help you develop the skills needed to succeed in this role, such as:
* Understanding Big-O time complexity can help you optimize data science pipelines and ensure efficient resource allocation.
* Visualizing performance using pyplot can help you identify and address performance bottlenecks in your team's code, enabling you to develop more efficient and scalable data science solutions.
Product Manager
Product Manager
Product Managers are responsible for the development and success of products. This course may be useful for Product Managers who want to understand the technical aspects of data science and how it can be used to improve products.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and completing projects. This course may be useful for Project Managers who want to understand the technical aspects of data science and how it can be used to improve project outcomes.
For more career information including salaries, visit:
OpenCourser.com/course/yubgp4/big
Reading list
We've selected 11 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
Big-O Time Complexity in Python Code.
Comprehensive and detailed treatment of computer algorithms. It valuable resource for researchers, scholars, and experienced software engineers. For beginners or learners who are new to programming, it is recommended to consult other resources first.
Is helpful as both a textbook and a reference manual. It provides information on algorithm analysis and design and could serve well as a more in-depth exploration of these topics than the course offers.
Save
Offers a deep dive into Python algorithms and is useful for people who want a greater understanding of algorithm design and analysis. It could be used as a supplement to the course material and for helping to improve programming skills.
This classic and highly respected algorithms textbook would be especially useful for learners who want to pursue advanced studies in computer science and software development. It is more detailed and advanced than what is covered in the course and could supplement the material well.
Is useful as a textbook or a reference manual. It covers algorithm analysis and design, making it a great resource for learning how to solve complex problems efficiently. While it uses Java as its programming language, the concepts and techniques presented are applicable to Python as well.
Collection of algorithmic problems and solutions. It valuable resource for learners who want to improve their problem-solving skills and learn how to apply algorithms to real-world problems.
Presents a collection of algorithmic solutions to real-world problems. It useful reference book for programmers who want to learn efficient ways to solve complex problems.
Presents a collection of algorithm problems and solutions. It valuable resource for learners who want to practice solving algorithmic problems and improve their skills in algorithm analysis and design.
Is helpful in providing background knowledge as it makes complex code easy to learn and explains algorithms in an intuitive, accessible way. It would be good for someone who is new to programming or who wants to brush up on the basics of computer science.
Covers specialized data structures and algorithms for handling massive datasets. It valuable resource for learners interested in big data processing and analysis.
Save
Covers a variety of programming topics including data structures and algorithms. It is helpful for learners who are new to programming or who want to broaden their understanding of computer science and engineering.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yubgp4/big
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
David Dalsveen
Language
English
Good to know
Examines Big-O Time Complexity, which is standard in data science, software development, and the broader field of computer science
Taught by David Dalsveen, who are recognized for their work in Big-O Time Complexity
Teaches skills, knowledge, and tools that are highly relevant to data science
Develops Big-O performance analysis skills, which are core skills for software developers and data scientists
Offers hands-on labs and interactive materials that enhance the learning experience
Requires students to come in with Python programming knowledge as a prerequisite
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