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Ekaterina Royal
In this project, learners will get a refresher of introductory statistics, learn about different python libraries that can be used to run statistical analysis, and create visualizations to represent the results. By the end of the project, the learners will import a real world data set, run statistical analysis to find means, medians , standard deviations, correlations, and other information of the data. The learners will also create distinct graphs and plots to represent the data.
Read more
In this project, learners will get a refresher of introductory statistics, learn about different python libraries that can be used to run statistical analysis, and create visualizations to represent the results. By the end of the project, the learners will import a real world data set, run statistical analysis to find means, medians , standard deviations, correlations, and other information of the data. The learners will also create distinct graphs and plots to represent the data.
Read more
In this project, learners will get a refresher of introductory statistics, learn about different python libraries that can be used to run statistical analysis, and create visualizations to represent the results. By the end of the project, the learners will import a real world data set, run statistical analysis to find means, medians , standard deviations, correlations, and other information of the data. The learners will also create distinct graphs and plots to represent the data.
Along the way, the learners will not only learn the frequently used statistics functions, but also learn to navigate documentations for different python libraries in order to find assistance in the implementation of those functions, and find other relevant functions as well. This will help the learners to understand the material and implement more complex functions down the road instead of simply memorizing the syntax of one solution.
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/yl538h/introduction
What's inside
Syllabus
Introduction to Statistics in Python
By the end of the project, the learners will import a real world data set in Python, run statistical analysis to find means, medians , standard deviations, correlations, and other information of the data. The learners will also create distinct graphs and plots to represent the data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores introductory statistics in Python, which is standard in industry
Taught by Ekaterina Royal, which is recognized for their work in statistics
Develops statistical analysis skills through hands-on labs and interacive materials
Recommended for learners based in North America
Save this course
Save Introduction to Statistics in Python to your list so you can find it easily later:
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Reviews summary
Informative statistics in python course
Learners say this course is a very solid overview of some important Python libraries, particularly those that are important for summarizing and graphically visualizing data. Key Features include lectures, readings, exams, quizzes, homework assignments, and a final project.
Provides a strong foundation in statistics and data visualization.
"A very solid overview of some important Python libraries."
"Provides a strong foundation in statistics and data visualization."
"The course has been very helpful in giving me a strong foundation in statistics and data visualization."
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 Introduction to Statistics in Python with these
activities:
Revisit Python Basics and Statistical Concepts
Show steps
This will provide a solid foundation by reviewing key concepts in Python and statistics.
Browse courses on
Python
Show steps
-
Review core Python concepts such as data types, variables, and control flow.
-
Examine the fundamentals of statistics, including descriptive statistics, probability, and hypothesis testing.
Review 'Python for Data Analysis' by Wes McKinney
Show steps
This book provides a comprehensive overview of Python's capabilities for statistical analysis and data manipulation.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Read chapters relevant to the course topics, focusing on statistical techniques using Python.
Seek Mentorship from Experienced Data Scientists
Show steps
Guidance from experts will accelerate your learning and broaden your understanding of statistical analysis applications.
Browse courses on
Mentorship
Show steps
-
Reach out to professionals in the field through platforms like LinkedIn or industry events.
-
Ask for guidance on statistical techniques, project ideas, and career development.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Python Libraries for Statistical Analysis
Show steps
These tutorials will equip you with the practical knowledge to utilize Python libraries for statistical analysis.
Browse courses on
Pandas
Show steps
-
Locate tutorials on using libraries like Pandas for data manipulation and NumPy for numerical operations.
-
Follow along with the tutorials, practicing the implementation of these libraries in code.
Collaborate on Statistical Analysis Problems
Show steps
Working with peers will enhance your problem-solving skills and provide alternative perspectives.
Browse courses on
Statistics
Show steps
-
Form study groups with classmates for regular discussions.
-
Present statistical problems to the group and work together to find solutions.
-
Share knowledge, clarify concepts, and provide constructive feedback.
Practice Statistical Analysis with Python Code
Show steps
This will solidify your understanding of statistical techniques and their implementation in Python.
Browse courses on
Data Analysis
Show steps
-
Find practice problems or exercises involving statistical analysis with Python.
-
Attempt to solve these problems on your own, referencing course materials and documentation.
-
Review your solutions against provided answers or consult the instructor for feedback.
Create a Data Visualization Project
Show steps
This project will enable you to apply your statistical knowledge to create meaningful data visualizations.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and identify the key insights you want to convey.
-
Select appropriate visualization techniques and create interactive dashboards.
-
Present your project, explaining the insights and the statistical techniques used.
Participate in Kaggle Competitions
Show steps
Test your skills against others and gain valuable experience in real-world statistical analysis challenges.
Browse courses on
Kaggle
Show steps
-
Identify Kaggle competitions aligned with the topics covered in this course.
-
Develop and submit statistical models to solve the competition tasks.
Revisit Python Basics and Statistical Concepts
Show steps
This will provide a solid foundation by reviewing key concepts in Python and statistics.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review core Python concepts such as data types, variables, and control flow.
- Examine the fundamentals of statistics, including descriptive statistics, probability, and hypothesis testing.
Review 'Python for Data Analysis' by Wes McKinney
Show steps
This book provides a comprehensive overview of Python's capabilities for statistical analysis and data manipulation.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to the course topics, focusing on statistical techniques using Python.
Seek Mentorship from Experienced Data Scientists
Show steps
Guidance from experts will accelerate your learning and broaden your understanding of statistical analysis applications.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Reach out to professionals in the field through platforms like LinkedIn or industry events.
- Ask for guidance on statistical techniques, project ideas, and career development.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Python Libraries for Statistical Analysis
Show steps
These tutorials will equip you with the practical knowledge to utilize Python libraries for statistical analysis.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Locate tutorials on using libraries like Pandas for data manipulation and NumPy for numerical operations.
- Follow along with the tutorials, practicing the implementation of these libraries in code.
Collaborate on Statistical Analysis Problems
Show steps
Working with peers will enhance your problem-solving skills and provide alternative perspectives.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Form study groups with classmates for regular discussions.
- Present statistical problems to the group and work together to find solutions.
- Share knowledge, clarify concepts, and provide constructive feedback.
Practice Statistical Analysis with Python Code
Show steps
This will solidify your understanding of statistical techniques and their implementation in Python.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find practice problems or exercises involving statistical analysis with Python.
- Attempt to solve these problems on your own, referencing course materials and documentation.
- Review your solutions against provided answers or consult the instructor for feedback.
Create a Data Visualization Project
Show steps
This project will enable you to apply your statistical knowledge to create meaningful data visualizations.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and identify the key insights you want to convey.
- Select appropriate visualization techniques and create interactive dashboards.
- Present your project, explaining the insights and the statistical techniques used.
Participate in Kaggle Competitions
Show steps
Test your skills against others and gain valuable experience in real-world statistical analysis challenges.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify Kaggle competitions aligned with the topics covered in this course.
- Develop and submit statistical models to solve the competition tasks.
Career center
Learners who complete Introduction to Statistics in Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. They use statistical techniques and machine learning algorithms to build models that can predict future outcomes or identify trends. The Introduction to Statistics in Python course can provide Data Scientists with a solid foundation in statistical concepts and Python programming, which are essential skills for success in this role.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then communicate their findings to stakeholders in a clear and concise way. The Introduction to Statistics in Python course can help Data Analysts develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use statistical methods to draw conclusions about populations based on samples. The Introduction to Statistics in Python course can help Statisticians develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They use statistical techniques and machine learning algorithms to create models that can predict future outcomes or identify trends. The Introduction to Statistics in Python course can help Machine Learning Engineers develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions. They use statistical techniques and machine learning algorithms to build models that can predict future outcomes or identify trends. The Introduction to Statistics in Python course can help Quantitative Analysts develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. They analyze financial statements, conduct research, and make recommendations to clients. The Introduction to Statistics in Python course can help Financial Analysts develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data about consumer behavior. They use statistical methods to identify trends and patterns. The Introduction to Statistics in Python course can help Market Researchers develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to solve business problems. They use statistical techniques and machine learning algorithms to build models that can predict future outcomes or identify trends. The Introduction to Statistics in Python course can help Operations Research Analysts develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then communicate their findings to stakeholders in a clear and concise way. The Introduction to Statistics in Python course can help Business Analysts develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. They use statistical techniques and machine learning algorithms to build models that can predict future outcomes or identify trends. The Introduction to Statistics in Python course can help Actuaries develop the skills they need to succeed in this role, including statistical analysis, Python programming, and data visualization.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. The Introduction to Statistics in Python course may be useful for Software Engineers who want to develop data-driven applications.
Data Architect
Data Architect
Data Architects design and manage data systems. The Introduction to Statistics in Python course may be useful for Data Architects who want to understand how to use statistical methods to analyze data.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. The Introduction to Statistics in Python course may be useful for Database Administrators who want to understand how to use statistical methods to analyze data.
Computer Programmer
Computer Programmer
Computer Programmers write and maintain computer programs. The Introduction to Statistics in Python course may be useful for Computer Programmers who want to develop data-driven applications.
System Analyst
System Analyst
System Analysts design and implement computer systems. The Introduction to Statistics in Python course may be useful for System Analysts who want to understand how to use statistical methods to analyze data.
For more career information including salaries, visit:
OpenCourser.com/course/yl538h/introduction
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
Introduction to Statistics in Python.
Provides a comprehensive introduction to reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and reinforcement learning algorithms.
Save
Provides a comprehensive introduction to deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Classic in the field of statistical learning. It provides a clear and concise introduction to the fundamental concepts and algorithms of statistical learning.
Provides a comprehensive introduction to natural language processing using Python. It covers a wide range of topics, including text classification, text clustering, and sentiment analysis.
Provides a comprehensive introduction to Python for data analysis. It covers a wide range of topics, including data manipulation, visualization, and statistical modeling.
Provides a Bayesian perspective on statistical modeling. It covers a wide range of topics, including Bayesian inference, model selection, and prediction.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to statistical learning, covering a wide range of topics from basic concepts to advanced techniques. It valuable resource for both beginners and experienced practitioners.
Provides a practical introduction to Bayesian data analysis. It covers a wide range of topics, including Bayesian inference, model selection, and prediction.
Provides a hands-on introduction to data science. It covers a wide range of topics, including data wrangling, machine learning, and data visualization.
Provides a gentle introduction to machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yl538h/introduction
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ekaterina Royal
Language
English
Good to know
Explores introductory statistics in Python, which is standard in industry
Taught by Ekaterina Royal, which is recognized for their work in statistics
Develops statistical analysis skills through hands-on labs and interacive materials
Recommended for learners based in North America
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