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Using Python for Research

Jukka-Pekka "JP" Onnela

3.7

Based on 12 ratings

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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.

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.

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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.

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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

Practical python for research essentials

According to learners, this course provides a solid bridge between introductory Python and its application in research settings. Students particularly value the focus on practical tools like NumPy, SciPy, and scikit-learn, which are essential for scientific computation and statistical learning. The case studies are frequently highlighted as a major strength, offering hands-on opportunities to apply concepts learned in the lectures. While many find the course well-structured and clear, some note that the pace can be quite fast and that having prior Python experience is highly recommended despite the basic review provided. Overall, it's seen as a valuable starting point for applying Python to research tasks.

Provides a good starting point for research applications.

"This course gave me a great foundation for using Python in my own research."

"It's a valuable first step towards applying Python to scientific problems."

"I feel much more confident about using Python tools for research tasks now."

Real-world examples enhance learning and application.

"The case studies were incredibly helpful in practicing the skills and seeing how they apply."

"Working through the diverse case studies made the concepts stick."

"I found the practical case studies to be the most beneficial part of the course."

Covers essential Python libraries for research.

"The focus on tools like NumPy and SciPy is exactly what I needed for my research work."

"I appreciated learning about scikit-learn and its practical application in the case studies."

"Gained hands-on experience with key Python libraries used in scientific computing."

Content moves quickly, demanding attention.

"The pace is quite fast, especially when covering more advanced topics."

"Felt the need to pause and rewatch lectures frequently to keep up."

"Covers a significant amount of material in a limited time, requires dedicated study."

Requires more than basic Python familiarity.

"Despite the review of basics, you really need solid Python knowledge coming in."

"Found it challenging without a strong prior background in Python programming."

"This course is best suited for those already comfortable with Python fundamentals."

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

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Reinforces understanding by encouraging you to actively engage with course materials you have already covered.

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Attempt practice quizzes
Go through lecture notes and slides
Identify areas where further clarification is needed

Review Python Basics

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Refreshes your understanding of the fundamentals of Python programming, which is essential for this course.

Browse courses on Python

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Review syntax and data types
Practice writing simple Python programs
Complete online tutorials or exercises

Practice Using Python Modules

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Provides hands-on experience in using Python modules commonly used in research, which is a key focus of this course.

Browse courses on NumPy

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Install and configure Python modules
Complete exercises or projects that involve using these modules
Seek help from online forums or documentation

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Join Study Groups or Discussion Forums

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Fosters collaboration and peer support, allowing you to exchange ideas, ask questions, and learn from others.

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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

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Challenges you to apply Python skills to a practical problem, fostering a deeper understanding of the course material.

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Identify a research question or problem
Design and implement a Python solution
Document and present your findings

Attend Python Workshops or Conferences

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Provides opportunities to connect with experts, learn about the latest trends in Python, and expand your knowledge.

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Identify relevant workshops or conferences
Register and attend
Engage actively in discussions and networking

Explore Statistical Learning with scikit-learn

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Provides additional guidance in statistical learning, a topic covered in the later part of the course.

Browse courses on Statistical Learning

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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:

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Featured in The Course Notes

This course is mentioned in our blog, The Course Notes. Read one article that features Using Python for Research:

Transform Your Career with Python: A Complete Guide for Professionals

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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.

Practical Statistics for Data Scientists

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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.

Using Python for Research

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Practical python for research essentials

Activities

Career center

Featured in The Course Notes

Reading list

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