Data Science: Statistics and Machine Learning Specialization

What you will learn from the Data Science: Statistics and Machine Learning Specialization

• Perform regression analysis, least squares and inference using regression models.
• Build and apply prediction functions
• Develop public data products
• Understand the process of drawing conclusions about populations or scientific truths from data
• Make informed data analysis decisions
• Use p-values, confidence intervals, and permutation tests
• Describe variability, distributions, limits, and confidence intervals
• Fundamentals of Probability, random variables, expectations and more
• T confidence intervals
• Power, Bootstrapping & Permutation Tests
• 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
• Linear Regression & Multivariable Regression
• Logistic Regression and Poisson Regression
• Residuals, Diagnostics, variance inflation, and model comparison
• Basic components of building and applying prediction functions
• Understand concepts such as training and tests sets, overfitting, and error rates
• Machine learning methods such as regression or classification trees
• Complete process of building prediction functions
• Prediction relative importance of steps, Errors, and Cross Validation
• Regularized Regression and Combining Predictors
• 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
• How to develop basic applications and interactive graphics in shiny
• How to create R Markdown files and embed R code in an Rmd
• Create a useful data product for the public
• Build an efficient and accurate prediction model

Data Science: Statistics and Machine Learning Specialization includes 5 Courses they are

1. Statistical Inference
2. Regression Models
3. Practical Machine Learning
4. Developing Data Products
5. Data Science Capstone

Course Instructors Roger D. Peng, Jeff Leek, Brian Caffo & Offered by Johns Hopkins University from Coursera

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