Our Courses

Introduction to High-Performance and Parallel Computing

Introduction to High-Performance and Parallel Computing

This course introduces the fundamentals of high-performance and parallel computing. It is targeted to scientists, engineers, scholars, really everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. We will cover the basics of Linux environments and bash scripting all the way to high throughput computing and parallelizing code. We recommend you are familiar with either Fortran 90, C++, or Python to complete some of the programming assignments.

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  • 14 hours
  • English
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Data Scientist Career Guide and Interview Preparation

Data Scientist Career Guide and Interview Preparation

Data science professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field? This course will prepare you to enter the job market as a great candidate for a data scientist position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch.

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  • 9 hours
  • English
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What is Data Science?

What is Data Science?

Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field. The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes.

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  • 19 hours
  • English
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Julia for Beginners in Data Science

Julia for Beginners in Data Science

This guided project is for those who want to learn how to use Julia for data cleaning as well as exploratory analysis.

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  • 3 hours
  • English
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Inventory Management

Inventory Management

Inventory is a strategic asset for organizations. The effective management of inventory can minimize a company’s spending while dramatically increasing its profit. In this course, we will explore how to use data science to manage inventory in uncertain environments, how to set inventory levels based on customer service requirements, and how to calculate inventory for products that have short sales cycles.

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  • 6 hours
  • English
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AI For Everyone

AI For Everyone

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take.

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  • 11 hours
  • English
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Bioconductor for Genomic Data Science

Bioconductor for Genomic Data Science

Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.

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  • 9 hours
  • English
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Applied Data Science for Data Analysts

Applied Data Science for Data Analysts

In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance.

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  • 17 hours
  • English
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Data Science Companion

Data Science Companion

The Data Science Companion provides an introduction to data science. You will gain a quick background in data science and core machine learning concepts, such as regression and classification. You’ll be introduced to the practical knowledge of data processing and visualization using low-code solutions, as well as an overview of the ways to integrate multiple tools effectively to solve data science problems. You will then leverage cloud resources from Amazon Web Services to scale data processing and accelerate machine learning model training.

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  • 2 hours
  • English
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Making Data Science Work for Clinical Reporting

Making Data Science Work for Clinical Reporting

This course is aimed to demonstate how principles and methods from data science can be applied in clinical reporting. By the end of the course, learners will understand what requirements there are in reporting clinical trials, and how they impact on how data science is used. The learner will see how they can work efficiently and effectively while still ensuring that they meet the needed standards.

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  • 11 hours
  • English
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Machine Learning Rapid Prototyping with IBM Watson Studio

Machine Learning Rapid Prototyping with IBM Watson Studio

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

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  • 9 hours
  • English
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Python Essentials for MLOps

Python Essentials for MLOps

Python Essentials for MLOps (Machine Learning Operations) is a course designed to provide learners with the fundamental Python skills needed to succeed in an MLOps role. This course covers the basics of the Python programming language, including data types, functions, modules and testing techniques. It also covers how to work effectively with data sets and other data science tasks with Pandas and NumPy. Through a series of hands-on exercises, learners will gain practical experience working with Python in the context of an MLOps workflow.

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  • 23 hours
  • English
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Integral Calculus and Numerical Analysis for Data Science

Integral Calculus and Numerical Analysis for Data Science

Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will provide an intuitive understanding of foundational integral calculus, including integration by parts, area under a curve, and integral computation. It will also cover root-finding methods, matrix decomposition, and partial derivatives. This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.

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  • 4 hours
  • English
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Advanced Linear Models for Data Science 2: Statistical Linear Models

Advanced Linear Models for Data Science 2: Statistical Linear Models

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective.

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  • 6 hours
  • English
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Prepare for DP-100: Data Science on Microsoft Azure Exam

Prepare for DP-100: Data Science on Microsoft Azure Exam

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses.​​ In this course, you will prepare to take the DP-100 Azure Data Scientist Associate certification exam. You will refresh your knowledge of how to plan and create a suitable working environment for data science workloads on Azure, run data experiments, and train predictive models.

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  • 9 hours
  • English
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SQL: A Practical Introduction for Querying Databases

SQL: A Practical Introduction for Querying Databases

Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and manipulating data in databases. A working knowledge of databases and SQL is a must for anyone who wants to start a career in Data Engineering, Data Warehousing, Data Analytics, Data Science or Business Intelligence.

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  • 21 hours
  • English
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Introduction to R Programming and Tidyverse

Introduction to R Programming and Tidyverse

This course is a gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if: • you want to do data analysis but don’t know programming • you know programming but aren’t familiar with R • you know some R programming but want to learn the tidyverse verbs You will learn to do data visualization and analysis in a reproducible manner and use functions that allow your code to be easily read and understood.

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  • 23 hours
  • English
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Introduction to R Programming for Data Science

Introduction to R Programming for Data Science

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks. You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language. The emphasis in this course is hands-on and practical learning .

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  • 11 hours
  • English
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Stability and Capability in Quality Improvement

Stability and Capability in Quality Improvement

In this course, you will learn to analyze data in terms of process stability and statistical control and why having a stable process is imperative prior to perform statistical hypothesis testing. You will create statistical process control charts for both continuous and discrete data using R software. You will analyze data sets for statistical control using control rules based on probability.

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  • 10 hours
  • English
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Getting Started with CyberGIS

Getting Started with CyberGIS

This course is intended to introduce students to CyberGIS—Geospatial Information Science and Systems (GIS)—based on advanced cyberinfrastructure as well as the state of the art in high-performance computing, big data, and cloud computing in the context of geospatial data science. Emphasis is placed on learning the cutting-edge advances of cyberGIS and its underlying geospatial data science principles.

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  • 9 hours
  • English
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Introduction to Data Science and scikit-learn in Python

Introduction to Data Science and scikit-learn in Python

This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn.

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  • 14 hours
  • English
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Spatial Data Science and Applications

Spatial Data Science and Applications

Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes. Consequently, they are bound to hire more and more spatial data scientists.

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  • 12 hours
  • English
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Introduction to Programming

Introduction to Programming

Designed for the not-yet-experienced programmer, this course will provide you with a structured foundation for developing complex programs in the fields of computer science or data science. If you are a self-taught programmer with scattered bits of understanding, or a complete novice, this is the course for you. Here, you will gain a thorough understanding of how to write programs to solve problems, through structured, scaffolded, hands-on exercises with many examples and opportunities to practice.

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  • 36 hours
  • English
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Foundations of Data Science

Foundations of Data Science

This is the first of seven courses in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace.

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  • 23 hours
  • English
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Statistics for Machine Learning for Investment Professionals

Statistics for Machine Learning for Investment Professionals

One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary).

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  • 18 hours
  • English
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