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Jukka-Pekka "JP" Onnela
This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. This version of the course includes a new module on statistical learning.
Read more
This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. This version of the course includes a new module on statistical learning.
Read more
This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. This version of the course includes a new module on statistical learning.
Using a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.
Register for this course and see more details by visiting:
OpenCourser.com/course/tu6pnz/using
What's inside
Learning objectives
- Python 3 programming basics (a review)
- Python tools (e.g., numpy and scipy modules) for research applications
- How to apply python research tools in practical settings
- Python 3 programming basics (a review)
- Python tools (e.g., numpy and scipy modules) for research applications
- How to apply python research tools in practical settings
Syllabus
Week 1: Python BasicsReview of basic Python 3 language concepts and syntax.
Week 2: Python Research ToolsIntroduction to Python modules commonly used in scientific computation, such as NumPy.
Read more
Syllabus
Week 1: Python BasicsReview of basic Python 3 language concepts and syntax.
Week 2: Python Research ToolsIntroduction to Python modules commonly used in scientific computation, such as NumPy.
Read more
Weeks 3 & 4: Case StudiesThis collection of six case studies from different disciplines provides opportunities to practice Python research skills.
Week 5: Statistical Learning Exploration of statistical learning using the scikit-learn library followed by a two-part case study that allows you to further practice your coding skills.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a comprehensive overview of Python programming basics, research tools, and case studies to practice skills
Suitable for learners with some prior Python experience seeking to enhance their skills for research projects
Incorporates case studies from various disciplines to illustrate practical applications of Python research tools
Introduces statistical learning techniques using the scikit-learn library, expanding the scope of Python research applications
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Reviews summary
Python research skills
According to students, Using Python for Research is a helpful course for building a strong foundation on how to use Python for research. This course requires strong Python fundamentals, and students without a good grasp of the language may have a difficult time with the assignments. However, learners with a good understanding of Python will find the engaging case studies and excellent instruction that helps learners gain much from this course.
Exceptional instructor.
"I would like to look at the course from two aspects:
1. Instructor: He is really awesome, explains the concepts clearly and concisely. He is one of the best i have seen in programming, almost on the same level as the legendary Eric Grimson, just not that cool :)"
Engaging case studies.
"This isn't an introductory courses, but gives you a first-week overview of Python."
"It's useful for intermediate-to-advanced levels, in order to try out case studies related do classification and data analysis of various datasets and areas, such as biology and DNA sequencing or natural language processing."
"Week 3 and Week 4 consist of many case studies which I liked a lot."
Comprehension tests are simple and not reflective.
"Comprehension tests are mostly boring and ridiculously simple only checking if a student was sleeping during the video."
"Nothing to reflect about, no questions to evoke some deeper understanding."
Exercises can be dull and not creative.
"The check exercises are relatively easy and relevant to check the understanding of the concepts explained."
"However, the assignments are way harder than the materials covered in the course, at least in the first two weeks."
"the exercises are sometimes dull and not really creative"
Some assignments are challenging and not always relevant
"The homework assignments are poorly worded, which sometimes leads to inconclusive results."
"Some exercises are missing with the next ones relying on the missing ones."
"The questions are often ambiguous."
"However, the assignments are way harder than the materials covered in the course, at least in the first two weeks."
Assumes a solid Python foundation.
"The course is not for everyone and not sort of "cool", but if you aspire to have a good grasp of creative problem solving and computational thinking, it is the place to start because it doesn't just ask you to carry out tasks like a robot."
"Also, those who wanna take this course need to have some strong foundations in python, and 2-4 hours a week studying is misleading."
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 Using Python for Research with these
activities:
Review Course Notes and Quizzes
Show steps
Reinforces understanding by encouraging you to actively engage with course materials you have already covered.
Browse courses on
Quizzes
Show steps
-
Attempt practice quizzes
-
Go through lecture notes and slides
-
Identify areas where further clarification is needed
Review Python Basics
Show steps
Refreshes your understanding of the fundamentals of Python programming, which is essential for this course.
Browse courses on
Python
Show steps
-
Review syntax and data types
-
Practice writing simple Python programs
-
Complete online tutorials or exercises
Practice Using Python Modules
Show steps
Provides hands-on experience in using Python modules commonly used in research, which is a key focus of this course.
Browse courses on
NumPy
Show steps
-
Install and configure Python modules
-
Complete exercises or projects that involve using these modules
-
Seek help from online forums or documentation
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join Study Groups or Discussion Forums
Show steps
Fosters collaboration and peer support, allowing you to exchange ideas, ask questions, and learn from others.
Show steps
-
Join online forums or discussion groups related to the course
-
Participate in study sessions with classmates
-
Contribute to discussions and help others
Develop a Python Project
Show steps
Challenges you to apply Python skills to a practical problem, fostering a deeper understanding of the course material.
Show steps
-
Identify a research question or problem
-
Design and implement a Python solution
-
Document and present your findings
Attend Python Workshops or Conferences
Show steps
Provides opportunities to connect with experts, learn about the latest trends in Python, and expand your knowledge.
Show steps
-
Identify relevant workshops or conferences
-
Register and attend
-
Engage actively in discussions and networking
Explore Statistical Learning with scikit-learn
Show steps
Provides additional guidance in statistical learning, a topic covered in the later part of the course.
Browse courses on
Statistical Learning
Show steps
-
Follow online tutorials on scikit-learn
-
Experiment with different statistical learning algorithms
-
Apply statistical learning to real-world datasets
Review Course Notes and Quizzes
Show steps
Reinforces understanding by encouraging you to actively engage with course materials you have already covered.
Browse courses on
Quizzes
Show steps
Show steps
Show steps
- Attempt practice quizzes
- Go through lecture notes and slides
- Identify areas where further clarification is needed
Review Python Basics
Show steps
Refreshes your understanding of the fundamentals of Python programming, which is essential for this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review syntax and data types
- Practice writing simple Python programs
- Complete online tutorials or exercises
Practice Using Python Modules
Show steps
Provides hands-on experience in using Python modules commonly used in research, which is a key focus of this course.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Install and configure Python modules
- Complete exercises or projects that involve using these modules
- Seek help from online forums or documentation
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join Study Groups or Discussion Forums
Show steps
Fosters collaboration and peer support, allowing you to exchange ideas, ask questions, and learn from others.
Show steps
Show steps
Show steps
- Join online forums or discussion groups related to the course
- Participate in study sessions with classmates
- Contribute to discussions and help others
Develop a Python Project
Show steps
Challenges you to apply Python skills to a practical problem, fostering a deeper understanding of the course material.
Show steps
Show steps
Show steps
- Identify a research question or problem
- Design and implement a Python solution
- Document and present your findings
Attend Python Workshops or Conferences
Show steps
Provides opportunities to connect with experts, learn about the latest trends in Python, and expand your knowledge.
Show steps
Show steps
Show steps
- Identify relevant workshops or conferences
- Register and attend
- Engage actively in discussions and networking
Explore Statistical Learning with scikit-learn
Show steps
Provides additional guidance in statistical learning, a topic covered in the later part of the course.
Browse courses on
Statistical Learning
Show steps
Show steps
Show steps
- Follow online tutorials on scikit-learn
- Experiment with different statistical learning algorithms
- Apply statistical learning to real-world datasets
Career center
Learners who complete Using Python for Research will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models and systems. They work closely with Data Scientists to translate algorithms into production-ready code. This course in Python for Research provides a strong foundation for a career as a Machine Learning Engineer. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, Machine Learning Engineers can become more effective in developing and deploying machine learning models.
Data Scientist
Data Scientist
Data Scientists design and build systems that extract knowledge and insights from data. They use advanced statistical and machine learning techniques to uncover hidden patterns and trends in data. This course in Python for Research can help build a foundation for a career as a Data Scientist. The course covers essential Python programming concepts and tools, as well as statistical learning techniques commonly used in research settings. By developing proficiency in Python, aspiring Data Scientists can become more effective in working with data and developing data-driven solutions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They develop and implement trading strategies, and manage risk. This course in Python for Research provides a solid foundation for a career as a Quantitative Analyst. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, Quantitative Analysts can become more effective in developing and implementing financial models.
Data Analyst
Data Analyst
Data Analysts collect, process, and analyze data to identify trends and patterns. They use statistical and machine learning techniques to extract insights from data. This course in Python for Research can help build a foundation for a career as a Data Analyst. The course covers essential Python programming concepts and tools, as well as statistical learning techniques commonly used in research settings. By developing proficiency in Python, aspiring Data Analysts can become more effective in working with data and developing data-driven solutions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies. This course in Python for Research provides a strong foundation for a career as a Software Engineer. The course covers Python programming basics and tools. By gaining proficiency in Python, aspiring Software Engineers can become more effective in developing and maintaining software applications.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use statistical techniques to draw conclusions from data and make predictions. This course in Python for Research can help build a foundation for a career as a Statistician. The course covers statistical learning techniques commonly used in research settings. By developing proficiency in Python, aspiring Statisticians can become more effective in working with data and developing statistical models.
Research Scientist
Research Scientist
Research Scientists conduct scientific research and develop new technologies. They work in a variety of fields, including medicine, engineering, and computer science. This course in Python for Research provides a strong foundation for a career as a Research Scientist. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, aspiring Research Scientists can become more effective in conducting research and developing new technologies.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. They work with large datasets and use a variety of programming languages and technologies. This course in Python for Research provides a strong foundation for a career as a Data Engineer. The course covers Python programming basics and tools. By gaining proficiency in Python, aspiring Data Engineers can become more effective in developing and maintaining data pipelines and infrastructure.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. They use data analysis and modeling techniques to make recommendations to businesses. This course in Python for Research provides a strong foundation for a career as a Business Analyst. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, aspiring Business Analysts can become more effective in analyzing data and making recommendations to businesses.
Product Manager
Product Manager
Product Managers lead the development and launch of new products. They work with engineers, designers, and marketers to bring products to market. This course in Python for Research may be useful for Product Managers who want to gain a deeper understanding of data analysis and modeling techniques. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, Product Managers can become more effective in understanding and using data to make decisions.
Consultant
Consultant
Consultants provide advice and guidance to businesses and organizations. They use their expertise in a particular field to help businesses solve problems and improve performance. This course in Python for Research may be useful for Consultants who want to gain a deeper understanding of data analysis and modeling techniques. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, Consultants can become more effective in understanding and using data to make recommendations to clients.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with products and services. They use a variety of research methods, including user testing, surveys, and interviews. This course in Python for Research may be useful for User Experience Researchers who want to gain a deeper understanding of data analysis and modeling techniques. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, User Experience Researchers can become more effective in analyzing research data and making recommendations to businesses.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. They work with businesses to improve efficiency and productivity. This course in Python for Research provides a strong foundation for a career as an Operations Research Analyst. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, aspiring Operations Research Analysts can become more effective in solving business problems using mathematical and analytical techniques.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations to investors. They use a variety of financial modeling techniques to assess the risk and return of investments. This course in Python for Research may be useful for Financial Analysts who want to gain a deeper understanding of data analysis and modeling techniques. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, Financial Analysts can become more effective in analyzing financial data and making recommendations to investors.
Market Researcher
Market Researcher
Market Researchers conduct research to understand the needs and wants of consumers. They use a variety of research methods, including surveys, interviews, and focus groups. This course in Python for Research may be useful for Market Researchers who want to gain a deeper understanding of data analysis and modeling techniques. The course covers Python programming basics and tools, as well as statistical learning techniques. By gaining proficiency in Python, Market Researchers can become more effective in analyzing research data and making recommendations to businesses.
For more career information including salaries, visit:
OpenCourser.com/course/tu6pnz/using
Reading list
We've selected 35 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
Using Python for Research.
Combines theory, applications, and discussion of best practices in data science. It will allow learners to supplement their learning about Python tools for data science by providing a deeper dive into statistical learning.
Covers how to use Python's scientific computing capabilities to perform data analysis, including how to clean and munge data, analyze and visualize it, and model it
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Valuable resource for Python programmers of all levels. It offers practical solutions to common Python programming problems and serves as a helpful reference guide for everyday use.
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Offers a deep dive into the inner workings of Python, providing insights into the language's design and implementation. It valuable resource for those seeking a comprehensive understanding of Python.
Covers data science fundamentals, including data cleaning, analysis, and modeling, and is particularly valuable as additional reading on creating data visualizations and performing statistical analysis
Save
Is an authoritative guide to deep learning theory and practice. It valuable resource for those seeking a comprehensive understanding of the field.
Will enhance the understanding of the scikit-learn library by covering its functionalities and the theory behind them in more detail.
For a more comprehensive and advanced understanding of Python, this book provides an in-depth exploration of the language's features and capabilities. It serves as a valuable reference guide for those seeking to master Python.
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Will supplement the course by offering a practical approach to learning Python, with an emphasis on real-world applications.
Is tailored specifically for scientists using Python. It covers the application of Python in scientific computing and provides practical examples for solving scientific problems.
Provides a comprehensive overview of machine learning algorithms and techniques using Python and is useful as a reference on specific machine learning models
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Will give learners a more comprehensive overview of Python 3, which will serve as a foundation for the course's content.
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Is an excellent resource for those looking to refine their Python programming skills. It provides guidance on best practices, code optimization, and effective coding techniques.
Will be particularly relevant during Week 5 of the course, where learners will delve into statistical learning. It offers practical guidance on applying machine learning algorithms to real-world problems using popular Python libraries like Scikit-learn, Keras, and TensorFlow. It's a valuable resource for gaining hands-on experience with machine learning techniques.
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Will enhance the learner's understanding of Python's core concepts and help them apply them in various domains.
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Serves as an excellent resource for anyone seeking to improve their Python programming skills. It guides learners through the fundamentals of Python and offers a clear explanation of programming concepts.
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Will make learning Python more interactive and engaging, using a visual and humorous approach.
Covers Python-specific libraries and packages for machine learning and is particularly valuable as a reference or supplemental resource on using Python for machine learning.
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Provides a foundation in Python programming, making this course more accessible to learners who may be new to Python.
Covers machine learning concepts and algorithms using Python and popular machine learning libraries, and is particularly valuable as a reference on using these libraries
Will introduce learners who are new to programming to Python's capabilities by focusing on practical examples.
Covers deep learning concepts and algorithms using Python and the Keras library, and is particularly valuable as a reference on using Keras for deep learning
As a comprehensive beginner's guide to Python, this book can be a helpful resource for those who need a quick and thorough introduction to the language. It covers the fundamentals of Python programming, making it suitable for those who have no prior programming experience.
Provides an introduction to Python programming and is particularly valuable for learners who need a refresher on Python basics
Provides an introduction to natural language processing, a subfield of artificial intelligence.
Provides a comprehensive overview of data science using Python and is particularly valuable as a reference on data science concepts and techniques
Provides a comprehensive overview of the Python standard library.
Provides a comprehensive overview of statistical learning methods, including the lasso and other regularization techniques, and is particularly valuable as background reading on statistical learning concepts
Provides a comprehensive overview of statistical learning methods and is particularly valuable as a reference on statistical learning concepts
Provides a theoretical foundation for machine learning from a probabilistic perspective and is particularly valuable as background reading on machine learning concepts
Save
Reference to Python.
Provides a comprehensive overview of Bayesian data analysis and is particularly valuable as background reading on Bayesian statistics
Introduces Python programming and emphasizes its use in application development.
Provides a gentle introduction to Python.
Provides a practical introduction to Bayesian data analysis using R and is particularly valuable as background reading on Bayesian statistics
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tu6pnz/using
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Effort
2 to 4 hours per week for 12 weeks
Level
Intermediate
Via
edX
Institution
Harvard University
Instructor
Jukka-Pekka "JP" Onnela
Language
English
Transcripts
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Good to know
Provides a comprehensive overview of Python programming basics, research tools, and case studies to practice skills
Suitable for learners with some prior Python experience seeking to enhance their skills for research projects
Incorporates case studies from various disciplines to illustrate practical applications of Python research tools
Introduces statistical learning techniques using the scikit-learn library, expanding the scope of Python research applications
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