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Using Python for Research

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.

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

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

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

Browse courses on Quizzes

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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

Four other activities

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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:

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Research Scientist

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Consultant

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.

See salaries and explore the career path for User Experience Researcher

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.

See salaries and explore the career path for Financial Analyst

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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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

Three deals to help you save

What's inside

Learning objectives

Syllabus

Good to know

Save this course

Reviews summary

Python research skills

Activities

Career center

Reading list

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