Johns Hopkins University
Data Science: Statistics and Machine Learning Specialization
Johns Hopkins University

Data Science: Statistics and Machine Learning Specialization

Roger D. Peng, PhD
Brian Caffo, PhD
Jeff Leek, PhD

Instructors: Roger D. Peng, PhD

38,892 already enrolled

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4.5

(620 reviews)

Intermediate level
Some related experience required
3 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.5

(620 reviews)

Intermediate level
Some related experience required
3 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Perform regression analysis, least squares and inference using regression models.

  • Build and apply prediction functions

  • Develop public data products

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Taught in English

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Specialization - 5 course series

Statistical Inference

Statistical Inference

Course 154 hours

What you'll learn

  • Understand the process of drawing conclusions about populations or scientific truths from data

  • Describe variability, distributions, limits, and confidence intervals

  • Use p-values, confidence intervals, and permutation tests

  • Make informed data analysis decisions

Skills you'll gain

Category: Sampling (Statistics)
Category: Data Analysis
Category: Probability
Category: Probability & Statistics
Category: Probability Distribution
Category: Sample Size Determination
Category: Statistical Modeling
Category: Bayesian Statistics
Category: Statistical Inference
Category: Statistical Analysis
Category: Statistical Hypothesis Testing
Category: Statistical Methods
Category: Statistics
Regression Models

Regression Models

Course 253 hours

What you'll learn

  • Use regression analysis, least squares and inference

  • Understand ANOVA and ANCOVA model cases

  • Investigate analysis of residuals and variability

  • Describe novel uses of regression models such as scatterplot smoothing

Skills you'll gain

Category: Supervised Learning
Category: Data Processing
Category: Regression Analysis
Category: Classification And Regression Tree (CART)
Category: Machine Learning
Category: Data Collection
Category: Decision Tree Learning
Category: Predictive Modeling
Category: Feature Engineering
Category: Random Forest Algorithm
Category: Statistical Machine Learning
Category: Applied Machine Learning
Category: Machine Learning Algorithms
Category: R Programming
Practical Machine Learning

Practical Machine Learning

Course 38 hours

What you'll learn

  • Use the basic components of building and applying prediction functions

  • Understand concepts such as training and tests sets, overfitting, and error rates

  • Describe machine learning methods such as regression or classification trees

  • Explain the complete process of building prediction functions

Skills you'll gain

Category: GitHub
Category: User Interface (UI)
Category: Shiny (R Package)
Category: Leaflet (Software)
Category: Hypertext Markup Language (HTML)
Category: Statistical Reporting
Category: Plotly
Category: Web Applications
Category: Rmarkdown
Category: Data Presentation
Category: Package and Software Management
Category: Interactive Data Visualization
Category: Data Visualization
Category: Data Visualization Software
Category: R Programming
Developing Data Products

Developing Data Products

Course 410 hours

What you'll learn

  • Develop basic applications and interactive graphics using GoogleVis

  • Use Leaflet to create interactive annotated maps

  • Build an R Markdown presentation that includes a data visualization

  • Create a data product that tells a story to a mass audience

Skills you'll gain

Category: Data Cleansing
Category: Data Presentation
Category: Exploratory Data Analysis
Category: Data Analysis
Category: Natural Language Processing
Category: Data Science
Category: Data Manipulation
Category: Predictive Modeling
Category: Data Storytelling
Category: Statistical Analysis
Category: Machine Learning
Category: Data Collection
Category: R Programming
Data Science Capstone

Data Science Capstone

Course 55 hours

What you'll learn

  • Create a useful data product for the public

  • Apply your exploratory data analysis skills

  • Build an efficient and accurate prediction model

  • Produce a presentation deck to showcase your findings

Skills you'll gain

Category: Data Science
Category: Predictive Modeling
Category: Regression Analysis
Category: Probability & Statistics
Category: Statistical Modeling
Category: Statistical Inference
Category: Statistical Analysis

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Instructors

Roger D. Peng, PhD
Johns Hopkins University
37 Courses1,641,862 learners
Brian Caffo, PhD
Johns Hopkins University
30 Courses1,669,246 learners

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