This specialization provides a comprehensive understanding of advanced statistical techniques used in data science. Learners will explore linear regression, model diagnostics, variable selection, Bayesian statistics, and data preparation. The courses are designed to equip data scientists and researchers with the skills needed to analyze complex data sets and draw meaningful conclusions.
Advanced Statistical Techniques for Data Science Specialization
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Advanced Statistical Techniques for Data Science Specialization
Master Advanced Statistical Techniques for Data. Gain deep insights into data through advanced statistical methods and practical applications.
Instructors: Ming-Long Lam
Included with 
Get in-depth knowledge of a subject
4.6
(7 reviews)
Intermediate level
Recommended experience
4 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
4.6
(7 reviews)
Intermediate level
Recommended experience
4 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
How to perform linear and nonlinear regression analysis using R.
Techniques for diagnosing and remediating issues in regression models.
Bayesian statistical methods for data analysis and inference.
Overview
Skills you'll gain
- Exploratory Data Analysis
- Bayesian Statistics
- Data Cleansing
- Applied Machine Learning
- Regression Analysis
- Data Manipulation
- Data Validation
- Analytics
- Machine Learning
- Data Presentation
- Statistical Modeling
- Machine Learning Algorithms
- Statistical Methods
- Data Analysis
- Statistical Analysis
- Statistical Inference
- Probability & Statistics
- Data Science
- Data Visualization
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
May 2025
102 practice exercises
Advance your subject-matter expertise
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Specialization - 5 course series
What you'll learn
Describe the assumptions of the linear regression models.
Use R to fit a linear regression model to a given data set.
Interpret and draw conclusions on the linear regression model.
Skills you'll gain
Category: Regression Analysis
Category: R Programming
Category: Statistical Inference
Category: Predictive Modeling
Category: Linear Algebra
Category: Statistical Analysis
Category: Probability & Statistics
Category: Data Analysis
What you'll learn
Describe the assumptions of the linear regression models.
Use diagnostic plots to detect violations of the assumptions of a linear regression model.
Perform variable selections and model validations.
Skills you'll gain
Category: Statistical Modeling
Category: Data Transformation
Category: Exploratory Data Analysis
Category: Correlation Analysis
Category: Data Validation
Category: Scatter Plots
Category: R Programming
Category: Linear Algebra
Category: Statistical Analysis
Category: Regression Analysis
Category: Statistical Hypothesis Testing
Category: Statistical Inference
What you'll learn
Skills you'll gain
Category: Regression Analysis
Category: R Programming
Category: Data Validation
Category: Mathematical Modeling
Category: Statistical Methods
Category: Statistical Modeling
Category: Advanced Analytics
Category: Statistical Hypothesis Testing
Category: Statistical Analysis
Category: Probability
Category: Statistical Inference
Category: Predictive Modeling
Category: Probability & Statistics
What you'll learn
Skills you'll gain
Category: Bayesian Statistics
Category: Statistical Modeling
Category: Statistical Inference
Category: Regression Analysis
Category: Simulations
Category: Statistical Software
Category: Markov Model
Category: Statistical Programming
Category: Data Analysis
Category: R Programming
Category: Probability
Category: Probability Distribution
Category: Statistical Analysis
Category: Statistical Methods
What you'll learn
1. Apply appropriate techniques for generating insights from data.
2. Present actionable solutions with confidence to the business stakeholders.
Skills you'll gain
Category: Data Analysis
Category: Data Mining
Category: Feature Engineering
Category: Decision Tree Learning
Category: Unsupervised Learning
Category: Regression Analysis
Category: Analytics
Category: Data Cleansing
Category: Machine Learning
Category: Data Visualization
Category: Machine Learning Algorithms
Category: Data Manipulation
Category: Scikit Learn (Machine Learning Library)
Category: Applied Machine Learning
Category: Statistical Analysis
Category: Data Presentation
Category: Statistical Modeling
Category: Exploratory Data Analysis
Category: Python Programming
Category: Data Science
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Build toward a degree
This Specialization is part of the following degree program(s) offered by Illinois Tech. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
Instructors
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Frequently asked questions
No, it’s not required—but it is recommended that you follow the course sequence listed on the specialization landing page. This order is designed to help you build your skills progressively, from foundational concepts to more advanced topics.
No, this specialization is open to all MOOC learners. You do not need to be an admitted degree-seeking student to complete the specialization and receive a Coursera–IIT co-branded non-credit certificate.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
More questions
Financial aid available,