This Asset we are sharing with you the Coursera – Statistics with Python Specialization free download links. This is a premium product and you will get it free on here. PSDLY made to help people like graphic designers, video creators, web developers, freelancers, filmmakers, etc. On our website, you will find lots of premium assets free like Free-course/tutorials, Lightroom Preset, PS action, Mockups, Videohive Items, Premium Sounds, Web Templates, and much more.
Free download — Coursera – Statistics with Python Specialization
Practical and Modern Statistical Thinking For All. Use Python for statistical visualization, inference, and modeling
Understanding and Visualizing Data with Python
In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.
At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera.
Inferential Statistical Analysis with Python
In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.
At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.
Fitting Statistical Models to Data with Python
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations.
This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python).
During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.
About: Coursera – Statistics with Python Specialization
| File Name: | Coursera – Statistics with Python Specialization |
| Content Source: | N/A |
| Genre / Category: | Programming |
| File Size : | |
| Publisher: | N/A |
| Updated and Published: | August,01, 2021 |
DOWNLOAD LINK : Coursera – Statistics with Python Specialization
FILEAXA.COM – is our main file storage service. We host all files there. You can join FILEAXA.COM premium service to get access to our all files with unlimited download speed.
Before extracting our files updates your WinRAR and download all parts in the same locations then unzip it. if it protected with passwords then join our telegram group and ask there for the password.
