3 courses in this series are designed to teach students the basic and intermediate concepts of statistical analysis using the Python programming language.
Students will learn:
Where does the data come from?
What types of data can be collected?
Data design
Data Management
Effective data exploration and visualization
They will be able to:
Use data for calculations and theoretical evaluations
Build confidence intervals
Interpret inferential results
Apply more advanced statistical modeling procedures
Finally, they will learn about the importance of research questions and be able to connect them to statistical analysis methods and data studied.
Learning project is underway
The courses in this series include a variety of tasks that test students’ knowledge and ability to apply the material.
Tasks include:
Concept testing
Written analyses
Python Programming Assessments
These tasks are performed using:
Exams
Submitting written assignments
Jupyter Notebook environment
Details of the courses that make up the specialization
Understanding and Visualizing Data with Python
Course 1
19 hours
4.7 (2,632 ratings)
Course Details
What you’ll learn:
Properly identify different data types and understand the different uses for each.
Create data visualizations and numerical summaries with Python.
Communicate statistical ideas clearly and concisely to a wide audience.
Identify appropriate analysis techniques for probability and non-probability samples.
Skills you will acquire:
Category: Statistics
statistics
Category: Data Analysis
Data Analytics
Category: Python Programming
Python programming
Category: Data similarity
Data similarity
Inferential statistical analysis with Python
Course 2
21 hours
4.6 (896 ratings)
Course Details
What you’ll learn:
Determine the assumptions required to calculate the confidence intervals for the relevant population parameters.
Create confidence intervals in Python and interpret the results.
Examine how inference procedures are produced and interpreted step by step when analyzing real data.
Run hypothesis tests in Python and interpret the findings.
Skills you will acquire:
Category: Confidence Profits
Confidence gains
Category: Python Programming
Python programming
Category: Statistical Inference
Statistical inference
Category: Statistical hypothesis testing
Statistical hypothesis testing
Fitting statistical models to data with Python
Course 3
14 hours
4.4 (689 ratings)
Course Details
What you’ll learn:
Deepen your understanding of statistical inferential techniques by mastering the art of fitting statistical models to data.
Link research questions to data analysis methods, emphasizing goals, relationships between variables, and making predictions.
Explore various statistical modeling techniques such as linear regression, logistic regression, and Bayesian inference using real data.
Work on practical cases in Python with libraries like Statsmodels, Pandas, and Seaborn in the Jupyter Notebook environment.
