This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.
Statistics with Python Specialization
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Statistics with Python Specialization
Practical and Modern Statistical Thinking For All. Use Python for statistical visualization, inference, and modeling
Instructors: Brenda Gunderson
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4.6
(2,883 reviews)
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
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Share your expertise with employers
Get in-depth knowledge of a subject
4.6
(2,883 reviews)
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Create and interpret data visualizations using the Python programming language and associated packages & libraries
Apply and interpret inferential procedures when analyzing real data
Apply statistical modeling techniques to data (ie. linear and logistic regression, linear models, multilevel models, Bayesian inference techniques)
Understand importance of connecting research questions to data analysis methods.
Overview
Skills you'll gain
- Histogram
- Statistics
- Bayesian Statistics
- Descriptive Statistics
- Matplotlib
- Statistical Modeling
- Statistical Methods
- Data Visualization Software
- Data Analysis
- Statistical Programming
- Statistical Hypothesis Testing
- Statistical Visualization
- Statistical Analysis
- Box Plots
- Statistical Inference
- Probability & Statistics
- Data Visualization
- Sampling (Statistics)
Tools you'll learn
What’s included
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Taught in English
23 practice exercises
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Specialization - 3 course series
What you'll learn
Properly identify various 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 broad audience
Identify appropriate analytic techniques for probability and non-probability samples
Skills you'll gain
Category: Descriptive Statistics
Category: Histogram
Category: Sampling (Statistics)
Category: Probability & Statistics
Category: Box Plots
Category: Statistical Inference
Category: Statistics
Category: Statistical Visualization
Category: Data Visualization Software
Category: Data Management
Category: Statistical Programming
Category: Exploratory Data Analysis
Category: Data Visualization
Category: Statistical Methods
Category: Matplotlib
Category: Jupyter
Category: Python Programming
Category: Data Analysis
What you'll learn
Determine assumptions needed to calculate confidence intervals for their respective population parameters.
Create confidence intervals in Python and interpret the results.
Review how inferential procedures are applied and interpreted step by step when analyzing real data.
Run hypothesis tests in Python and interpret the results.
Skills you'll gain
Category: Statistical Inference
Category: Statistical Hypothesis Testing
Category: Statistical Analysis
Category: Python Programming
Category: NumPy
Category: Statistical Methods
Category: Matplotlib
Category: Data Analysis
Category: Probability & Statistics
Category: Jupyter
Category: Statistics
Category: Bayesian Statistics
What you'll learn
Deepen your understanding of statistical inference techniques by mastering the art of fitting statistical models to data.
Connect research questions with data analysis methods, emphasizing objectives, relationships between variables, and making predictions.
Explore various statistical modeling techniques like linear regression, logistic regression, and Bayesian inference using real data sets.
Work through hands-on case studies in Python with libraries like Statsmodels, Pandas, and Seaborn in the Jupyter Notebook environment.
Skills you'll gain
Category: Regression Analysis
Category: Statistical Modeling
Category: Correlation Analysis
Category: Statistical Hypothesis Testing
Category: Bayesian Statistics
Category: Jupyter
Category: Statistical Inference
Category: Statistical Methods
Category: Predictive Modeling
Category: Python Programming
Category: Statistical Programming
Category: Statistical Analysis
Category: Probability Distribution
Category: Exploratory Data Analysis
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Frequently asked questions
This specialization is made up of three courses, each with four weeks/modules. Each week in a course requires a commitment of roughly 3-6 hours, which will vary by learner.
High school-level algebra is the only background knowledge mandatory for the first course in the series. A basic Python and/or coding background is recommended.
It is definitely recommended to take this specialization in order.
More questions
Financial aid available,