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Our Courses
Developing AI Applications on Azure
This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics and you'll receive specific guidance on Microsoft's Team Data Science Approach.
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Course by
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Self Paced
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16 hours
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English
Tools for Data Science
In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio.
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Course by
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Self Paced
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18 hours
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English
Advanced Topics and Future Trends in Database Technologies
This course consists of four modules covering some of the more in-depth and advanced areas of database technologies, followed by a look at the future of database software and where the industry is heading. Advanced Topics and Future Trends in Database Technologies can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others.
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Course by
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Self Paced
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17 hours
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English
Machine Learning with PySpark: Customer Churn Analysis
This 90-minute guided-project, "Pyspark for Data Science: Customer Churn Prediction," is a comprehensive guided-project that teaches you how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company. This guided-project covers a range of essential tasks, including data loading, exploratory data analysis, data preprocessing, feature preparation, model training, evaluation, and deployment, all using Pyspark.
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Course by
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Self Paced
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3 hours
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English
Command Line Tools for Genomic Data Science
Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
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Course by
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Self Paced
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12 hours
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English
Python and Statistics for Financial Analysis
Course Overview: https://youtu.be/JgFV5qzAYno Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry.
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Course by
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Self Paced
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13 hours
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English
Application of Data Analysis in Business with R Programming
This Guided Project “Application of Data Analysis in Business with R Programming” is for the data science learners and enthusiasts of 2 hours long. The learners will learn to discover the underlying patterns and analyse the trends in data with Data Science functions.
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Course by
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Self Paced
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3 hours
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English
Data Analysis and Interpretation Capstone
The Capstone project will allow you to continue to apply and refine the data analytic techniques learned from the previous courses in the Specialization to address an important issue in society. You will use real world data to complete a project with our industry and academic partners. For example, you can work with our industry partner, DRIVENDATA, to help them solve some of the world's biggest social challenges!
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Course by
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Self Paced
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8 hours
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English
R Programming and Tidyverse Capstone Project
In this third and final course of the "Expressway to Data Science: R Programming and Tidyverse" specialization you will reinforce and display your R and tidyverse skills by completing an analysis of COVID-19 data! Here is a chance to apply your skills to a real-world dataset that has effected all of us. Throughout the capstone, you will import COVID-19 data; clean, tidy, and join datasets; and develop visualizations. You will also provide some analysis and interpretation to your results, preparing you for your journey into data science.
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Course by
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Self Paced
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10 hours
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English
Genomic Data Science and Clustering (Bioinformatics V)
How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world?
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Course by
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Self Paced
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10 hours
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English
BigQuery Soccer Data Ingestion
This is a self-paced lab that takes place in the Google Cloud console. Get started with sports data science by importing soccer data on matches, teams, players, and match events into BigQuery tables. Information access uses multiple formats, and BigQuery makes working with multiple data sources simple. In this lab you will get started with sports data science by importing external sports data sources into BigQuery tables. This will give you the basis for building more sophisticated analytics in subsequent labs.
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Course by
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Self Paced
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1 hour
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English
Finalize a Data Science Project
This course is designed for business professionals that want to learn how to gather results from previous stages of the data science project and present them to stakeholders. Learners will communicate the results of a model to stakeholders, be shown how to build a basic web app to demonstrate machine learning models and implement and test pipelines that automate the model training, tuning and deployment processes.
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Course by
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Self Paced
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12 hours
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English
Cloud Computing Foundations
Welcome to the first course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines. You will also learn how to apply Agile software development techniques to projects which will be useful in building portfolio projects and global-scale Cloud infrastructures. This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering.
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Course by
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Self Paced
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19 hours
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English
Understanding the Enterprise Systems Environment
Understanding the Enterprise Systems Environment is the second course in the SAP Technology Consultant Professional Certificate program. The course builds your understanding of the digital landscape. You’ll explore business processes and organizational alignment. You’ll get an overview of how systems are designed and developed, and consider architecture, infrastructure, application development, data science, cloud, privacy, and security.
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Course by
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Self Paced
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12 hours
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English
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform.
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Course by
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Self Paced
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38 hours
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English
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives.
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Course by
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Self Paced
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15 hours
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English
The Data Science of Health Informatics
Health data are notable for how many types there are, how complex they are, and how serious it is to get them straight. These data are used for treatment of the patient from whom they derive, but also for other uses. Examples of such secondary use of health data include population health (e.g., who requires more attention), research (e.g., which drug is more effective in practice), quality (e.g., is the institution meeting benchmarks), and translational research (e.g., are new technologies being applied appropriately).
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Course by
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Self Paced
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10 hours
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English
C Programming: Advanced Data Types - 5
In this course you will define your own data types in C, and use the newly created types to more efficiently store and process your data. Many programming languages provide a number of built-in data types to store things such as integers, decimals, and characters in variables, but what if you wanted to store more complex data? Defining your own data types in C allows you to more efficiently store and process data such as a customer's name, age and other relevant data, all in one single variable!
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Course by
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Self Paced
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8 hours
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English
Clinical Natural Language Processing
This course teaches you the fundamentals of clinical natural language processing (NLP). In this course you will learn the basic linguistic principals underlying NLP, as well as how to write regular expressions and handle text data in R. You will also learn practical techniques for text processing to be able to extract information from clinical notes. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop text processing algorithms to identify diabetic complications from clinical notes.
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Course by
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Self Paced
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13 hours
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English
Preparing for AI-900: Microsoft Azure AI Fundamentals 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 AI-900 Microsoft Azure AI Fundamentals certification exam. You will refresh your knowledge of fundamental principles of machine learning on Microsoft Azure. You will go back over the main consideration of AI workloads and the features of computer vision, Natural Language Processing (NLP), and conversational AI workloads on Azure.
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Course by
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Self Paced
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10 hours
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English
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces&nb
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Course by
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Self Paced
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17 hours
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English
Optimize ML Models and Deploy Human-in-the-Loop Pipelines
In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence. After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting.
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Course by
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Self Paced
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11 hours
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English
Statistics For Data Science
This is a hands-on project to give you an overview of how to use statistics in data science.
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Course by
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Self Paced
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3 hours
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English
The Nature of Data and Relational Database Design
This course provides a comprehensive overview of data, various data types, design of databases for storage of data, and creation and manipulation of data in databases using SQL. By the end of this course, students will be able to describe what business intelligence is and how it’s different from business analytics and data science, conduct a basic descriptive statistical analysis and articulate the findings, and differentiate between types of statistics.
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Course by
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Self Paced
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7 hours
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English
Leadership for Cancer Informatics Research
Informatics research often requires multidisciplinary teams. This requires more flexibility to communicate with team members with distinct backgrounds. Furthermore, team members often have different research and career goals. This can present unique challenges in making sure that everyone is on the same page and cohesively working together.
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Course by
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Self Paced
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8 hours
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English