Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Materials Data Sciences and Informatics
This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Microsoft Azure Machine Learning for Data Scientists
Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Perform data science with Azure Databricks
In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data science workloads in the cloud. This is the fourth course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurec ertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning.
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Course by
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Self Paced
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26 ساعات
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الإنجليزية
Data science perspectives on pandemic management
The COVID-19 pandemic is one of the first world-wide scenarios where data made a difference in capturing and analyzing the diffusion and impact of the disease. We offer an introductory course for decision makers, policy makers, public bodies, NGOs, and private organizations about methods, tools, and experiences on the use of data for managing current and future pandemic scenarios. This course describes modern methods for data-driven policy making in the context of pandemics.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
Introduction to Clinical Data Science
This course will prepare you to complete all parts of the Clinical Data Science Specialization. In this course you will learn how clinical data are generated, the format of these data, and the ethical and legal restrictions on these data. You will also learn enough SQL and R programming skills to be able to complete the entire Specialization - even if you are a beginner programmer. While you are taking this course you will have access to an actual clinical data set and a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Machine Learning Introduction for Everyone
This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Big Data, Genes, and Medicine
This course distills for you expert knowledge and skills mastered by professionals in Health Big Data Science and Bioinformatics. You will learn exciting facts about the human body biology and chemistry, genetics, and medicine that will be intertwined with the science of Big Data and skills to harness the avalanche of data openly available at your fingertips and which we are just starting to make sense of.
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Course by
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Self Paced
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40 ساعات
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الإنجليزية
Probability Theory: Foundation for Data Science
Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science
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Course by
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Self Paced
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48 ساعات
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الإنجليزية
Scalable Machine Learning on Big Data using Apache Spark
This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
The exposome: cracking the science about what makes us sick
What are the causes of disease? We know that most diseases result from a combination of genes and environment (nature and nurture). Our genes alone do not determine our fate. For most complex diseases, externalities - environmental factors in the broad sense - are more important. This includes our living and working environments, diet, social support and stress, pollution, and exposure to infectious agents.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية
AI Workflow: Data Analysis and Hypothesis Testing
This is the second 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. In this course you will begin your work for a hypothetical streaming media company by doing exploratory data analysis (EDA). Best practices for data visualization, handling missing data, and hypothesis testing will be introduced to you as part of your work. You will learn techniques of estimation
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Statistical Inference for Estimation in Data Science
This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings. This course 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.
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Course by
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Self Paced
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28 ساعات
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الإنجليزية
Computer Vision in Microsoft Azure
In Microsoft Azure, the Computer Vision cognitive service uses pre-trained models to analyze images, enabling software developers to easily build applications"see" the world and make sense of it. This ability to process images is the key to creating software that can emulate human visual perception. In this course, you'll explore some of these capabilities as you learn how to use the Computer Vision service to analyze images. This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Calculus through Data & Modelling: Vector Calculus
This course continues your study of calculus by focusing on the applications of integration to vector valued functions, or vector fields. These are functions that assign vectors to points in space, allowing us to develop advanced theories to then apply to real-world problems. We define line integrals, which can be used to fund the work done by a vector field. We culminate this course with Green's Theorem, which describes the relationship between certain kinds of line integrals on closed paths and double integrals.
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Course by
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Self Paced
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5 ساعات
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الإنجليزية
Introduction to the Tidyverse
This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of "tidy data" and how this idea serves to organize data for analysis and modeling. We will also cover how non-tidy can be transformed to tidy data, the data science project life cycle, and the ecosystem of Tidyverse R packages that can be used to execute a data science project.
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Course by
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Self Paced
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الإنجليزية
Google Advanced Data Analytics Capstone
You’re almost there! This is the seventh and final course of the Google Advanced Data Analytics Certificate. In this course, you have the opportunity to complete an optional capstone project that includes key concepts from each of the six preceding courses.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
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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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Processing Data with Python
Processing data is used in virtually every field these days. It is used for analyzing web traffic to determine personal preferences, gathering scientific data for biological analysis, analyzing weather patterns, business practices, and on. Data can take on many different forms and come from many different sources. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
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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Course by
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Self Paced
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9 ساعات
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الإنجليزية
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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Course by
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Self Paced
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19 ساعات
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الإنجليزية
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. This project covers the syntax of Julia from a data science perspective. So you will not build anything during the course of this project.
While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning.
Special Features:
1) Work with 2 real-world datasets.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
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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Course by
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Self Paced
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6 ساعات
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الإنجليزية
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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Course by
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Self Paced
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11 ساعات
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الإنجليزية
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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Course by
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Self Paced
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9 ساعات
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الإنجليزية
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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Course by
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Self Paced
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17 ساعات
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الإنجليزية