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Laura Gemmell
By the end of this project, you will learn how to use Python for basic statistics (including t-tests and correlations). We will learn all the important steps of analysis, including loading, sorting and cleaning data. In this course, we will use exploratory data analysis to understand our data and plot boxplots to visualize the data. Boxplots also allow us to investigate any outliers in our datasets. We will then learn how to examine relationships between the different data using correlations and scatter plots. Finally, we will compare data using t-tests. Throughout this course we will analyse a dataset on Science and Technology from World Bank. The measures in this dataset are numeric, therefore you will learn how to handle and compare numeric data.
Read more
By the end of this project, you will learn how to use Python for basic statistics (including t-tests and correlations). We will learn all the important steps of analysis, including loading, sorting and cleaning data. In this course, we will use exploratory data analysis to understand our data and plot boxplots to visualize the data. Boxplots also allow us to investigate any outliers in our datasets. We will then learn how to examine relationships between the different data using correlations and scatter plots. Finally, we will compare data using t-tests. Throughout this course we will analyse a dataset on Science and Technology from World Bank. The measures in this dataset are numeric, therefore you will learn how to handle and compare numeric data.
Read more
By the end of this project, you will learn how to use Python for basic statistics (including t-tests and correlations). We will learn all the important steps of analysis, including loading, sorting and cleaning data. In this course, we will use exploratory data analysis to understand our data and plot boxplots to visualize the data. Boxplots also allow us to investigate any outliers in our datasets. We will then learn how to examine relationships between the different data using correlations and scatter plots. Finally, we will compare data using t-tests. Throughout this course we will analyse a dataset on Science and Technology from World Bank. The measures in this dataset are numeric, therefore you will learn how to handle and compare numeric data.
This guided project is for anyone with an interest in performing statistical analysis using Python. This could be someone from a social science background with statistics knowledge who wants to advance their analysis, or anyone interested in analysing data.
Register for this course and see more details by visiting:
OpenCourser.com/course/yn0t0y/basic
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What's inside
Syllabus
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Develops foundational statistical concepts, such as t-tests and correlations, for social scientists
Suits beginners with no prior Python knowledge or programming experience
Uses real-world dataset analysis to demonstrate Python's capabilities
Covers handling missing values and exploring outliers in data
Provides hands-on guidance through guided project format
Focuses on visualizing numeric data with boxplots and scatter plots
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Reviews summary
Practical python statistics for data analysis
According to learners, this course offers a largely positive experience, particularly for those looking to apply statistical concepts using Python. Students frequently highlight the clear explanations provided, which effectively demystify basic statistics like t-tests and correlations. The hands-on labs and practical application using a real-world dataset are consistently praised as highly valuable. While the course is a solid introduction, some suggest the pace can feel rushed in later sections, and it assumes a strong understanding of statistical theory. Learners also recommend having a basic Python understanding despite no prior programming being listed as a prerequisite, to get the most out of the coding environment.
Concepts are explained very clearly.
"The instructor's explanations were incredibly clear, especially on t-tests and correlations."
"It demystified basic stats in Python for me; the instructor walks you through each step clearly."
"I found the course concise and to the point, covering essentials effectively."
Excellent for applying stats with Python.
"This guided project was exactly what I needed to bridge my statistics knowledge with practical Python application."
"I really appreciated the hands-on labs that helped me understand the data analysis workflow from start to finish."
"It's a perfect guided project for quick practical learning, providing useful methods."
Potential for some coding environment issues.
"I found several issues with the coding environment not matching the video instructions. This made it frustrating to complete the labs."
"The technical glitches overshadowed my learning experience, and it needs urgent updates to the environment."
"I struggled with the provided coding environment, as it often didn't align with the course content."
Basic Python skills enhance learning.
"While it says 'no prior Python needed', I think a very basic understanding would be beneficial for me."
"I found some Python code snippets difficult to follow at times, even with basic prior exposure."
"It's easier to follow if you're already somewhat comfortable with Python syntax and environment."
Best for those with prior stats theory.
"The statistical explanations were quite brief; it assumes I already know the theory well."
"I wished for slightly more depth on interpreting the statistical output beyond just running the code."
"The course assumes I have a good grasp of statistical theory before diving into the Python implementation."
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 Basic Statistics in Python (Correlations and T-tests) with these
activities:
Connect with Experienced Data Analysts
Show steps
Seeking guidance and support from experienced professionals can accelerate learning and provide valuable insights.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through online platforms or professional networks
-
Reach out to potential mentors and express interest in their guidance
-
Establish regular communication and schedule meetings or calls
Review Descriptive Statistics and Scatter Plots
Show steps
This activity can strengthen foundational statistical knowledge, especially in preparing and understanding data visualization and analysis.
Browse courses on
Descriptive Statistics
Show steps
-
Study definitions and concepts of descriptive statistics
-
Examine examples and practice interpreting scatter plots
-
Consider hypothetical datasets and apply statistical concepts
Practice Calculating Correlation Coefficients
Show steps
Regular practice in calculating correlation coefficients can sharpen analytical skills and deepen understanding of statistical concepts.
Show steps
-
Solve practice problems involving correlation coefficient calculations
-
Analyze real-world datasets and compute correlation coefficients
Three other activities
Expand to see all activities and additional details
Show all six activities
Visualize Data Using Boxplots
Show steps
Creating boxplots improves practical data visualization skills and reinforces insights gained from statistical analysis.
Browse courses on
Boxplots
Show steps
-
Select appropriate datasets for boxplot visualization
-
Use statistical software or tools to generate boxplots
-
Interpret and analyze the results, identifying patterns and outliers
Write a Report on Data Analysis Findings
Show steps
Writing a report consolidates learning and provides a tangible demonstration of analytical and communication skills.
Browse courses on
Data Analysis
Show steps
-
Summarize and interpret statistical findings
-
Structure the report using appropriate sections and headings
-
Present results clearly and concisely, using tables and graphs
-
Proofread and ensure the report is well-written and error-free
Analyze a Dataset Using Python
Show steps
A hands-on project allows students to apply statistical concepts and programming skills, fostering deeper understanding and retention.
Browse courses on
Data Analysis
Show steps
-
Identify a dataset of interest
-
Explore and clean the data using Python
-
Perform statistical analysis, including t-tests and correlations
-
Visualize the results using appropriate plots and charts
-
Write a report or presentation to showcase findings
Connect with Experienced Data Analysts
Show steps
Seeking guidance and support from experienced professionals can accelerate learning and provide valuable insights.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through online platforms or professional networks
- Reach out to potential mentors and express interest in their guidance
- Establish regular communication and schedule meetings or calls
Review Descriptive Statistics and Scatter Plots
Show steps
This activity can strengthen foundational statistical knowledge, especially in preparing and understanding data visualization and analysis.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Study definitions and concepts of descriptive statistics
- Examine examples and practice interpreting scatter plots
- Consider hypothetical datasets and apply statistical concepts
Practice Calculating Correlation Coefficients
Show steps
Regular practice in calculating correlation coefficients can sharpen analytical skills and deepen understanding of statistical concepts.
Show steps
Show steps
Show steps
- Solve practice problems involving correlation coefficient calculations
- Analyze real-world datasets and compute correlation coefficients
Three other activities
Expand to see all activities and additional details
Show all six activities
Visualize Data Using Boxplots
Show steps
Creating boxplots improves practical data visualization skills and reinforces insights gained from statistical analysis.
Browse courses on
Boxplots
Show steps
Show steps
Show steps
- Select appropriate datasets for boxplot visualization
- Use statistical software or tools to generate boxplots
- Interpret and analyze the results, identifying patterns and outliers
Write a Report on Data Analysis Findings
Show steps
Writing a report consolidates learning and provides a tangible demonstration of analytical and communication skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Summarize and interpret statistical findings
- Structure the report using appropriate sections and headings
- Present results clearly and concisely, using tables and graphs
- Proofread and ensure the report is well-written and error-free
Analyze a Dataset Using Python
Show steps
A hands-on project allows students to apply statistical concepts and programming skills, fostering deeper understanding and retention.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a dataset of interest
- Explore and clean the data using Python
- Perform statistical analysis, including t-tests and correlations
- Visualize the results using appropriate plots and charts
- Write a report or presentation to showcase findings
Career center
Learners who complete Basic Statistics in Python (Correlations and T-tests) will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians apply statistical methods to collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and market research. The knowledge and skills gained in this Basic Statistics in Python course will provide a strong foundation for individuals interested in pursuing a career as a Statistician. The course covers essential statistical concepts such as correlations and t-tests, which are widely used in statistical analysis.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. They work in a variety of industries, including technology, healthcare, and finance. This course in Basic Statistics in Python provides a solid foundation for aspiring Data Scientists, as it covers essential statistical concepts and techniques used in data analysis. By learning how to handle and analyze numeric data, participants will gain valuable skills that are highly sought after in the field of data science.
Data Analyst
Data Analyst
Data Analysts use their knowledge of statistics and data analysis techniques to investigate data, identify patterns and trends, and communicate insights to stakeholders. This course in Basic Statistics in Python, which covers topics such as exploratory data analysis, correlations, and t-tests, provides a solid foundation for aspiring Data Analysts. By learning how to handle and analyze numeric data, participants will gain valuable skills that are highly sought after in the field of data science.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They play a crucial role in the financial industry, helping investment firms and hedge funds make informed decisions. This course in Basic Statistics in Python provides a strong foundation for individuals interested in becoming Quantitative Analysts. The course covers essential statistical concepts and techniques, such as correlations and t-tests, which are widely used in financial analysis and modeling.
Research Analyst
Research Analyst
Research Analysts gather, analyze, and interpret data to provide insights and recommendations to businesses and organizations. They may specialize in a particular industry or area of research, such as market research, financial analysis, or public policy. This course in Basic Statistics in Python provides a valuable foundation for aspiring Research Analysts, as it covers essential statistical methods and techniques used in data analysis and interpretation. The skills learned in this course will enable participants to effectively analyze data, draw conclusions, and communicate their findings to stakeholders.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to evaluate and make recommendations on investments. They work in a variety of industries, including banking, insurance, and asset management. This course in Basic Statistics in Python provides a valuable foundation for aspiring Financial Analysts, as it covers essential statistical methods and techniques used in financial analysis. The skills learned in this course will enable participants to effectively analyze data, draw conclusions, and communicate their findings to stakeholders.
Business Analyst
Business Analyst
Business Analysts use data to identify opportunities, solve problems, and improve decision-making within organizations. They work in a variety of industries, including consulting, finance, and healthcare. This course in Basic Statistics in Python provides a valuable foundation for aspiring Business Analysts, as it covers essential statistical methods and techniques used in data analysis. The skills learned in this course will enable participants to effectively analyze data, draw conclusions, and communicate their findings to stakeholders.
Market Researcher
Market Researcher
Market Researchers gather, analyze, and interpret data about consumers, markets, and competitors. They use this information to help businesses make informed marketing decisions. This course in Basic Statistics in Python provides a valuable foundation for aspiring Market Researchers, as it covers essential statistical methods and techniques used in market research. The skills learned in this course will enable participants to effectively analyze data, draw conclusions, and communicate their findings to stakeholders.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of health and disease in populations. They use statistical methods to analyze data and identify risk factors for disease. This course in Basic Statistics in Python provides a valuable foundation for aspiring Epidemiologists, as it covers essential statistical methods and techniques used in epidemiology. The skills learned in this course will enable participants to effectively analyze data, draw conclusions, and communicate their findings to stakeholders.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to data in the field of biology. They work in a variety of settings, including academia, government, and industry. This course in Basic Statistics in Python provides a valuable foundation for aspiring Biostatisticians, as it covers essential statistical methods and techniques used in biostatistics. The skills learned in this course will enable participants to effectively analyze data, draw conclusions, and communicate their findings to stakeholders.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, including technology, finance, and healthcare. While not directly related to the field of statistics, this course in Basic Statistics in Python may be useful for Software Engineers who need to analyze data as part of their work. The course covers essential statistical concepts and techniques that can be applied to a variety of software development tasks.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. They work in a variety of industries, including technology, finance, and healthcare. While not directly related to the field of statistics, this course in Basic Statistics in Python may be useful for Data Engineers who need to analyze data as part of their work. The course covers essential statistical concepts and techniques that can be applied to a variety of data engineering tasks.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in a variety of industries, including logistics, manufacturing, and healthcare. This course in Basic Statistics in Python may be useful for Operations Research Analysts who need to analyze data as part of their work. The course covers essential statistical concepts and techniques that can be applied to a variety of operations research problems.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty in the insurance and finance industries. This course in Basic Statistics in Python may be useful for Actuaries who need to analyze data as part of their work. The course covers essential statistical concepts and techniques that can be applied to a variety of actuarial problems.
Economist
Economist
Economists use statistical methods to analyze economic data and make predictions about the economy. This course in Basic Statistics in Python may be useful for Economists who need to analyze data as part of their work. The course covers essential statistical concepts and techniques that can be applied to a variety of economic problems.
For more career information including salaries, visit:
OpenCourser.com/course/yn0t0y/basic
Reading list
We've selected nine 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
Basic Statistics in Python (Correlations and T-tests).
Save
Provides a comprehensive overview of reinforcement learning using Python. It covers the basics of reinforcement learning and provides examples and exercises that can be used to practice the concepts covered in the course.
Provides a comprehensive overview of machine learning for data science using Python. It covers the basics of machine learning and provides examples and exercises that can be used to practice the concepts covered in the course.
Provides a comprehensive overview of machine learning using Python. It covers the basics of machine learning and provides examples and exercises that can be used to practice the concepts covered in the course.
Provides a comprehensive overview of natural language processing using Python. It covers the basics of natural language processing and provides examples and exercises that can be used to practice the concepts covered in the course.
Provides a comprehensive overview of deep learning using Python. It covers the basics of deep learning and provides examples and exercises that can be used to practice the concepts covered in the course.
Provides a comprehensive overview of the Python programming language and its applications in data analysis. It covers the basics of Python and provides examples and exercises that can be used to practice the concepts covered in the course.
Provides a comprehensive overview of correlation and regression analysis and its applications in various fields. It covers the basics of correlation and regression analysis and provides examples and exercises that can be used to practice the concepts covered in the course.
Provides a comprehensive overview of statistics and its applications in social and behavioral sciences. It covers the basics of statistics and provides examples and exercises that can be used to practice the concepts covered in the course.
Provides a comprehensive overview of statistics and its applications in psychology. It covers the basics of statistics and provides examples and exercises that can be used to practice the concepts covered in the course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yn0t0y/basic
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