Data Science Graduate Certificate

This course introduces the key steps involved in the data mining pipeline, including:

• Data Understanding
• Data Preprocessing
• Data Warehousing
• Data Modeling
• Interpretation and Evaluation
• Real-World Applications

This course introduces the key steps involved in the data mining pipeline, including:

• Data Understanding
• Data Preprocessing
• Data Warehousing
• Data Modeling
• Interpretation and Evaluation
• Real-World Applications

Understand the foundations of probability and its relationship to statistics and data science.

• Learn what it means to calculate a probability, independent and dependent outcomes, and conditional events.
• Study how discrete and continuous random variables fit with data collection.
• Understand the fundamental importance of Gaussian (normal) random variables and the Central Limit Theorem to all statistics and data science.

This course introduces statistical inference, sampling distributions, and confidence intervals.

You will learn:

• How to define and construct good estimators
• Methods of moments estimation
• Maximum likelihood estimation
• Methods of constructing confidence intervals that extend to more general settings

This course will focus on the theory and implementation of hypothesis testing, especially as it relates to applications in data science.

You will learn to use hypothesis tests to make informed decisions from data.

• The general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values.
• The misuse of testing concepts, especially p-values, and the ethical implications of such misuse.

Introduction to Statistical Learning will explore concepts in statistical modeling:

• When to use certain models
• How to tune those models
• Whether other options will provide certain trade-offs

We will cover regression, classification, trees, resampling, unsupervised techniques, and more.

Learn the foundational framework and application of cross-validation, bootstrapping, dimensionality reduction, ridge regression, lasso, GAMs, and splines.

This course consists of the foundational framework and application of tree-based methods, support vector machines, and unsupervised learning.

In this course, you will learn various supervised machine learning algorithms and prediction tasks applied to different data. You will also learn when to use which model and why, and how to improve model performance.

We will cover models such as linear and logistic regression, KNN, decision trees, and ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM.

One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data.

In this course, you will learn:

• Unsupervised learning methods for dimensionality reduction, clustering, and learning latent features.
• Real-world applications such as recommender systems, through hands-on examples of product recommendation algorithms.

This course will cover the basics of deep learning, including how to build and train:

• Multilayer perceptron
• Convolutional Neural Networks (CNNs)
• Recurrent neural networks (RNNs)
• Autoencoders (AE)
• Generative adversarial networks (GANs).

The course includes several hands-on projects, including:

• Cancer detection with CNNs and RNNs on disaster tweets
• Generating dog images with GANs

This course will provide a set of foundational statistical modeling tools for data science.

You will be introduced to:

• Methods, theory, and applications of linear statistical models
• The topics of parameter estimation, residual diagnostics, the goodness of fit
• Various strategies for variable selection and model comparison

Attention will be given to the misuse of statistical models and the ethical implications of such misuse.

Statistical modeling will introduce you to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design.

ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications.

Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality.

Some attention will also be given to ethical issues raised in experimentation.

You will study a broad set of more advanced statistical modeling tools, including:

• Generalized linear models (GLMs), which will introduce classification (through logistic regression)
• Monparametric modeling, including kernel estimators and smoothing splines
• Semi-parametric generalized additive models (GAMs).

Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models.