# Statistics with Python Specialization

Product is rated as #31 in category Data Science
 Learning experience 9.2 9.1

Learners will be introduced to the field of statistics and will explore basic principles behind using data for estimation and for assessing theories. This course also focuses on various modeling objectives, including making inferences about relationships between variables and generating predictions for future observations.

Last updated on November 13, 2021 6:20 pm

The Statistics with Python specialization has been developed to teach students the beginning and intermediate concepts of statistical analysis utilizing the Python programming language. Learners will discover where data originate from, what kinds of data can be gathered, research study data style, data management, and how to efficiently perform information expedition and visualization. They will have the ability to use data for estimation and assessing theories, construct confidence intervals, analyze inferential outcomes, and use advanced analytical modeling treatments. Finally, they will discover the significance of and have the ability to link research study concerns to the statistical and data analysis techniques taught to them.

### What you will discover

• Create and analyze data visualizations utilizing the Python programming language and associated packages & libraries

• Apply and analyze inferential procedures when evaluating real data

• Apply analytical modeling methods to data (ie. linear and logistic regression, linear models, multilevel models, Bayesian reasoning methods)
• Understand the significance of linking research questions to data analysis approaches.

## Syllabus

There are 3 Courses in this Specialization organized by the University of Michigan.

### Course 1. Understanding and Visualizing Data with Python

In this course, students will be presented to the field of statistics, consisting of where information originates from, research study style, data management, and exploring and visualizing data. Learners will determine various kinds of data, and discover how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will likewise be presented with the distinctions between probability and non-probability sampling from bigger populations, the concept of how sample estimates differ, and how reasonings can be made about bigger populations based upon probability sampling.

At the end of every week, students will apply the analytical ideas they’ve discovered utilizing Python within the course environment. During these lab-based sessions, students will find the various usages of Python as a tool, consisting of the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are supplied to stroll students through the development of visualizations and data management, all within Python. This course makes use of the Jupyter Notebook environment within Coursera.

### Course 2. Inferential Statistical Analysis with Python

In this course, we will check out the fundamental concepts behind utilizing data for evaluation and for assessing theories. We will examine both categorical data and quantitative data, beginning with one population strategy and broadening to deal with comparisons of 2 populations. Also, we will discover how to build self-confidence periods. We will likewise utilize sample information to examine whether a theory about the worth of a specification corresponds with the data. A significant focus will be on translating inferential outcomes properly.

At the completion of each week, students will apply what they’ve discovered utilizing Python within the course environment. During these lab-based sessions, students will go through tutorials concentrating on particular case studies to assist strengthen the week’s analytical concepts, which will include even more deep dives into Python libraries consisting of Statsmodels, Pandas, and Seaborn. This course makes use of the Jupyter Notebook environment within Coursera.

## Description

The Statistics with Python specialization has been developed to teach students the beginning and intermediate concepts of statistical analysis utilizing the Python programming language. Learners will discover where data originate from, what kinds of data can be gathered, research study data style, data management, and how to efficiently perform information expedition and visualization. They will have the ability to use data for estimation and assessing theories, construct confidence intervals, analyze inferential outcomes, and use advanced analytical modeling treatments. Finally, they will discover the significance of and have the ability to link research study concerns to the statistical and data analysis techniques taught to them.

### What you will discover

• Create and analyze data visualizations utilizing the Python programming language and associated packages & libraries

• Apply and analyze inferential procedures when evaluating real data

• Apply analytical modeling methods to data (ie. linear and logistic regression, linear models, multilevel models, Bayesian reasoning methods)
• Understand the significance of linking research questions to data analysis approaches.

## Syllabus

There are 3 Courses in this Specialization organized by the University of Michigan.

### Course 1. Understanding and Visualizing Data with Python

In this course, students will be presented to the field of statistics, consisting of where information originates from, research study style, data management, and exploring and visualizing data. Learners will determine various kinds of data, and discover how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will likewise be presented with the distinctions between probability and non-probability sampling from bigger populations, the concept of how sample estimates differ, and how reasonings can be made about bigger populations based upon probability sampling.

At the end of every week, students will apply the analytical ideas they’ve discovered utilizing Python within the course environment. During these lab-based sessions, students will find the various usages of Python as a tool, consisting of the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are supplied to stroll students through the development of visualizations and data management, all within Python. This course makes use of the Jupyter Notebook environment within Coursera.

### Course 2. Inferential Statistical Analysis with Python

In this course, we will check out the fundamental concepts behind utilizing data for evaluation and for assessing theories. We will examine both categorical data and quantitative data, beginning with one population strategy and broadening to deal with comparisons of 2 populations. Also, we will discover how to build self-confidence periods. We will likewise utilize sample information to examine whether a theory about the worth of a specification corresponds with the data. A significant focus will be on translating inferential outcomes properly.

At the completion of each week, students will apply what they’ve discovered utilizing Python within the course environment. During these lab-based sessions, students will go through tutorials concentrating on particular case studies to assist strengthen the week’s analytical concepts, which will include even more deep dives into Python libraries consisting of Statsmodels, Pandas, and Seaborn. This course makes use of the Jupyter Notebook environment within Coursera.

## Specification:

• Coursera
• University of Michigan
• Microdegree
• Self-paced
• Beginner
• 1-3 Months
• Free Trial (Paid Course & Certificate)
• English
• Python
• High School-level Algebra
• Data Analysis Data Science with 'Python' Data Visualization Practical Statistics

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