University of Colorado Boulder
Certificate

Data Science Graduate Certificate

Develop interdisciplinary skills in data science and gain knowledge of statistical analysis, data mining, and machine learning from one of the nation’s top-ranked Tier 1 research institutions.

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Enroll by April 19, 2024

Classes start March 11, 2024

6-9 months

The certificate is 12 credits and can be completed in approximately in 6-9 months, depending on chosen course load per session

$525 per credit

$6,300 total cost

100% online

No application required

Data Science Graduate Certificate

There is a growing need for data scientists, data analysts, and statisticians equipped with the knowledge and essential skills to work across diverse organizations

CU Boulder is committed to teaching the next generation of interdisciplinary data scientists

In this Graduate Certificate, you will gain knowledge of statistical analysis, data mining, and machine learning and prepare for a successful career in this high-paying, in-demand field

Boulder Data Science Graduate Certificate

Program description

Develop interdisciplinary skills in data science and gain knowledge of statistical analysis, data mining, and machine learning from one of the nation’s top-ranked Tier 1 research institutions.

Overview

Data science is a multidisciplinary field that focuses on extracting knowledge and insight from large datasets.

In this program, you can build the skills to take advantage of the increasing demand for data scientists, data analysts, and statisticians equipped with the knowledge and experience to work across diverse organizations. You’ll gain new data skills, build a portfolio through hands-on projects, and earn an industry-recognized credential to help you stand out to recruiters and hiring managers.

To earn the Data Science Graduate Certificate (12 credits), students must complete the following required specializations:

  • Data Mining Foundations and Practice Specialization (3 credits)
  • Data Science Foundations: Statistical Inference Specialization (3 credits).

Choose two specializations from the following:

  • Introduction to Statistical Learning for Data Science Specialization (3 credits)
  • Machine Learning Specialization (3 credits)
  • Statistical Modeling for Data Science Specialization (3 credits)

The certificate will be stackable, and the credits can be applied to the Master of Science in Data Science on Coursera degree for students interested in continuing their education.

Required background

There are no formal prerequisites, but we recommend that you have prior knowledge of basic mathematical concepts and computer programming.

  • Math: Calculus and Linear Algebra
  • Programming: Python and R Programming

If you do not have this knowledge already, we encourage you to try out non-credit coursework before attempting for-credit courses.

Skills you will gain

  • Probability Theory
  • Unstructured Data
  • Machine Learning
  • Artificial Intelligence
  • Deep Learning
  • Data Visualization
  • Big Data Analytics
  • Statistical Modeling
  • Data Mining
  • Python
  • R Programming
  • Calculus
  • Linear Algebra

12 required courses

Course 1 of 15

Data Mining Foundations and Practice Specialization - Data Mining Pipeline (1-credit)

Overview

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

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

Course 2 of 15

Data Mining Foundations and Practice Specialization - Data Mining Methods (1-credit)

Overview

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

  • Frequent Pattern Analysis
  • Classification
  • Clustering
  • Outlier Analysis
  • Mining Complex Data
  • Research Frontiers in the Data Mining Field

Course 3 of 15

Data Mining Foundations and Practice Specialization - Project (1-credit)

Overview

This course offers step-by-step guidance and hands-on experience in designing and implementing a real-world data mining project. You will learn:

  • Problem Formulation
  • Literature Survey
  • Proposed Work
  • Evaluation
  • Discussion
  • Future Work

Course 4 of 15

Data Science Foundations: Statistical Inference Specialization - Probability Theory: Foundation for Data Science (1-credit)

Overview

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.

Course 5 of 15

Data Science Foundations: Statistical Inference Specialization - Statistical Inference for Estimation in Data Science (1-credit)

Overview

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

Course 6 of 15

Data Science Foundations: Statistical Inference Specialization - Statistical Inference and Hypothesis Testing in Data Science Applications (1-credit)

Overview

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.

Course 7 of 15

Introduction to Statistical Learning for Data Science Specialization - Statistical Learning for Data Science: Regression and Classification (1-credit)

Overview

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.

Course 8 of 15

Introduction to Statistical Learning for Data Science Specialization - Statistical Learning for Data Science: Resampling, Selection, and Splines (1-credit)

Overview

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

Course 9 of 15

Introduction to Statistical Learning for Data Science Specialization - Statistical Learning for Data Science: Trees, SVM and Unsupervised Learning (1-credit)

Overview

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

Course 10 of 15

Machine Learning Specialization - Introduction to Machine Learning: Supervised Learning (1-credit)

Overview

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.

Course 11 of 15

Machine Learning Specialization - Unsupervised Algorithms in Machine Learning (1-credit)

Overview

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.

Course 12 of 15

Machine Learning Specialization - Introduction to Deep Learning (1-credit)

Overview

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

Course 13 of 15

Statistical Modeling for Data Science Specialization - Modern Regression Analysis in R (1-credit)

Overview

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.

Course 14 of 15

Statistical Modeling for Data Science Specialization - ANOVA and Experimental Design (1-credit)

Overview

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.

Course 15 of 15

Statistical Modeling for Data Science Specialization - Generalized Linear Models and Nonparametric Regression (1-credit)

Overview

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.

The certificate stacks directly into the full Master of Science in Data Science (MS-DS) degree from the University of Colorado Boulder.

Take the next step in your education to boost your career. This Graduate Certificate can be stacked toward the Master of Science in Data Science on Coursera degree if you are interested in continuing your education.

The Data Science Foundations Specialization, a requirement to earn this Data Science Graduate Certificate, is a pathway for admissions to the Master of Science in Data Science (MS-DS) degree program.

The Master of Data Science degree is designed to prepare you to successfully work and collaborate with others across a variety of scientific, business, and other fields.

To pursue admission to the MS-DS degree program, you’ll need to complete the following four required protocols:

  • Pass one pathway with a pathway GPA of 3.0 or higher
  • Earn a C or better in all pathway courses within your chosen pathway
  • Earn an overall cumulative GPA of 3.0 or higher
  • Indicate interest in degree admission (via the enrollment form)

By completing these steps, you will go from working on a certificate to earning a degree.

Upon completion of the Data Science Graduate Certificate, you can apply these 12 credits to the Master of Science in Data Science degree.

University of Colorado Boulder

Certificate

Data Science Graduate Certificate

Data Science Graduate Certificate Certificate earn credit directly towards the:

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Frequently asked questions

Coursera does not grant academic credit; the decision to grant, accept, or recognize academic credit, and the process for awarding such credit, is at the sole discretion of the academic institutions offering the Graduate Certificate program and/or other institutions that have determined that completion of the program may be worthy of academic credit. Completion of a Graduate Certificate program does not guarantee admission into the full Master’s program referenced herein, or any other degree program.