Statistics with Python Specialization by University of Michigan – Coursera

Statistics with Python Specialization by University of Michigan ranks 7th in the list of best online statistics courses. This course is offered through Coursera. The specialization teaches learners how to use the Python programming language to master various statistical concepts. Learners will comprehend data sources, types of data, and the data collection, analysis, and management process. Furthermore, the students will be familiar with data exploration and visualization approaches.

Furthermore, the students will delve into data assessment theories, build confidence interval concepts, and interpret inferential outcomes. Furthermore, sophisticated statistical modeling processes are thoroughly explored, with actual hands-on workshops to help students learn the abilities. Finally, the students will be able to connect research issues to statistical and data analytic methodologies in order to solve complicated problems in a real-world setting.

The course modules are:

• Understanding and visualizing data with Python
• This module introduces the field of statistics, data sources, study design, and data management aspects of a data science problem.
• In addition, the learners will explore the data and communicate the findings with data visualization techniques. The learners will have a solid understanding of various data types and interpret univariate and multivariate data summaries.
• The module also covers important concepts on probability and non-probability sampling of large populations and learns how each sample varies and how inference can be applied based on probability sampling.
• Finally, the learners will apply the statistical concepts using Python during lab sessions and implement various libraries to perform data analysis.

Inferential statistical analysis with Python

• The second module focuses on the basic principles behind data for estimation and assessment. First, the learners will analyze categorical and quantitative data, population techniques, and expand to handle two populations. Next, the learners will understand how to use confidence intervals and work with sample data to assess whether specific parameters are consistent within the data set.
• Finally, the learners will learn about interpreting inferential results and work on numerous case studies to solidify their skills by implementing statistical concepts using Python.

Fitting statistical models to data with Python

• The course’s final module explores the advanced concepts on statistical inference techniques and learns to fit statistical models to data correctly. In addition, the learners will work on various models and understand the relationship between variables for predictions.
• This module also introduces and explores several statistical modeling techniques such as linear regression, logistic regression, generalized linear models, Bayesian techniques, and hierarchical and mixed-effects models. All of the concepts covered in the module are demonstrated with the help of practical examples using real data sets. Besides, the learners will understand the types of modeling approaches available for different data types based on the underlying study design.
• Finally, the learners will explore data visualization using Python and various libraries such as Statsmodels, Pandas, and Seaborn for advanced statistical analysis.

Instructor: Brenda Gunderson, Kerby Shedden, and Brady T. West

Level: Beginner

Duration: 3 months

User Review: 4.6/5

No. of Reviews: 2220

Price: Free Enrollment (Additional charges for certification may apply)

Enroll here: coursera.org/specializations/statistics-with-python