Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
Precision Medicine
This course will provide you with the key knowledge and tools to understand the fundamentals and practical implications of precision medicine, its opportunities and challenges. It will address precision-medicine era diagnostics, treatment selection, genetic counseling, public health interventions, and biomedical research.
-
Course by
-
Self Paced
-
29 hours
-
English
CertNexus Certified Data Science Practitioner
The field of Data Science has topped the Linked In Emerging Jobs list for the last 3 years with a projected growth of 28% annually and the World Economic Forum lists Data Analytics and Scientists as the top emerging job for 2022. Data can reveal insights and inform business—by guiding decisions and influencing day-to-day operations.
-
Course by
-
Self Paced
-
English
Capstone: Data Science Problem in Linear Algebra Framework
In this course, you'll review the specifics of the Capstone project. In addition, you will create and run your regression model and share your results with your peers. Let's get started!
-
Course by
-
Self Paced
-
9 hours
-
English
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose.
-
Course by
-
Self Paced
-
14 hours
-
English
Python and Machine-Learning for Asset Management with Alternative Data Sets
Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications.
-
Course by
-
Self Paced
-
21 hours
-
English
Predictive Analytics for Business with H2O in R
This is a hands-on, guided project on Predictive Analytics for Business with H2O in R. By the end of this project, you will be able apply machine learning and predictive analytics to solve a business problem, explain and describe automatic machine learning, perform automatic machine learning (AutoML) with H2O in R.
-
Course by
-
Self Paced
-
3 hours
-
English
Health Data Science Foundation
This course is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios. We cover deep learning (DL) methods, healthcare data and applications using DL methods.
-
Course by
-
Self Paced
-
24 hours
-
English
Data Analysis in Python: Using Pandas DataFrames
This Guided Project Data Analysis in Python: Using Pandas DataFrames is for those who are interested in using python for data science in practice. In this 90-minute Guided Project, learn how to import and visualize an IMDb data set in Pandas. You will learn how to import JSON data into a Pandas Dataframe and apply the data preparation process to ensure the data is ready for analysis. To achieve this, we will explore the famous IMDb Movies dataset. We will start with importing our JSON data into a Pandas data frame.
-
Course by
-
Self Paced
-
2 hours
-
English
Python Programming Fundamentals
This introductory course is designed for beginners and individuals with limited programming experience who want to embark on their software development or data science journey using Python. Throughout the course, learners will gain a solid understanding of algorithmic thinking, Python syntax, code testing, debugging techniques, and modular code development--essential skills for a successful career in software engineering, development, or data science.
-
Course by
-
Self Paced
-
24 hours
-
English
The Outcomes and Interventions of Health Informatics
For clinical data science to be effective in healthcare—to achieve the outcomes desired—it must translate into decision support of some sort, either at the patient, clinician, or manager level. By the end of this course, students will be able to articulate the need for an intervention, to right size it, to choose the appropriate technology, to describe how knowledge should be obtained, and to design a monitoring plan.
-
Course by
-
Self Paced
-
7 hours
-
English
Measuring Total Data Quality
By the end of this second course in the Total Data Quality Specialization, learners will be able to: 1. Learn various metrics for evaluating Total Data Quality (TDQ) at each stage of the TDQ framework. 2. Create a quality concept map that tracks relevant aspects of TDQ from a particular application or data source. 3. Think through relative trade-offs between quality aspects, relative costs and practical constraints imposed by a particular project or study. 4. Identify relevant software and related tools for computing the various metrics. 5.
-
Course by
-
Self Paced
-
9 hours
-
English
Python and Machine Learning for Asset Management
This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University.
-
Course by
-
Self Paced
-
16 hours
-
English
Data Visualization with R
In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data.
-
Course by
-
Self Paced
-
12 hours
-
English
Machine Learning for Investment Professionals
This course is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. Incorporating real-life case studies, this course covers both the technical and the “soft skills” necessary for investment professionals to stay relevant.
In this course, you will learn how to:
-\tDistinguish between supervised and unsupervised machine learning and deep learning
-
Course by
-
Self Paced
-
17 hours
-
English
Go Beyond the Numbers: Translate Data into Insights
This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations.
-
Course by
-
Self Paced
-
33 hours
-
English
IoT Devices
The Internet of Things (IoT) stands to be the next revolution in computing. Billions of data-spouting devices connected to the Internet are already fundamentally changing the way we live and work. This course teaches a deep understanding of IoT technologies from the ground up. Students will learn IoT device programming (Arduino and Raspberry Pi), sensing and actuating technologies, IoT protocol stacks (Zigbee, 5G, NFC, MQTT, etc), networking backhaul design and security enforcement, data science for IoT, and cloud-based IoT platforms such as AWS IoT.
-
Course by
-
Self Paced
-
12 hours
-
English
Cloud Virtualization, Containers and APIs
Welcome to the second course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn to design Cloud-native systems with the fundamental building blocks of Cloud computing. These building blocks include virtual machines and containers. You will also learn how to build effective Microservices using technologies like Flask and Kubernetes. Finally, you will analyze successful patterns in Operations including: Effective alerts, load testing and Kaizen.
-
Course by
-
Self Paced
-
14 hours
-
English
Build and Operate Machine Learning Solutions with Azure
Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning.
-
Course by
-
Self Paced
-
32 hours
-
English
Linux Basics: The Command Line Interface - 6
This course will introduce you to Linux, a powerful operating system used by most professional developers! Why add Linux to your C programming skills? Most people use Linux without knowing it! Whether you use a smartphone, search the web, or use an ATM, each time Linux is involved somewhere in the background. It is the most used operating system for embedded devices and high-performance servers.
-
Course by
-
Self Paced
-
8 hours
-
English
SQL for Data Science Capstone Project
Data science is a dynamic and growing career field that demands knowledge and skills-based in SQL to be successful. This course is designed to provide you with a solid foundation in applying SQL skills to analyze data and solve real business problems. Whether you have successfully completed the other courses in the Learn SQL Basics for Data Science Specialization or are taking just this course, this project is your chance to apply the knowledge and skills you have acquired to practice important SQL querying and solve problems with data.
-
Course by
-
Self Paced
-
35 hours
-
English
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. 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. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history.
-
Course by
-
Self Paced
-
24 hours
-
English
Software Architecture Patterns for Big Data
The course is intended for individuals looking to understand the architecture patterns necessary to take large software systems that make use of big data to production. You will transform big data prototypes into high quality tested production software. After measuring the performance characteristics of distributed systems, you will identify trouble areas and implement scalable solutions to improve performance.
-
Course by
-
Self Paced
-
49 hours
-
English
Build a Data Science Web App with Streamlit and Python
Welcome to this hands-on project on building your first data science web app with the Streamlit library in Python.
-
Course by
-
Self Paced
-
2 hours
-
English
Introduction to High-Throughput Materials Development
This course is an introduction to high-throughput experimental methods that accelerate the discovery and development of new materials. It is well recognized that the discovery of new materials is the key to solving many technological problems faced by industry and society. These problems include energy production and utilization, carbon capture, tissue engineering, and sustainable materials production, among many others. This course will introduce the learner to a remarkable new approach to materials discovery and characterization: high-throughput materials development (HTMD).
-
Course by
-
Self Paced
-
16 hours
-
English
Generalized Linear Models and Nonparametric Regression
In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools.
-
Course by
-
Self Paced
-
42 hours
-
English