Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Microsoft Azure Machine Learning
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. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. 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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11 ساعات
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الإنجليزية
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Essential Linear Algebra 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 teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Visualizing Data in the Tidyverse
Data visualization is a critical part of any data science project. Once data have been imported and wrangled into place, visualizing your data can help you get a handle on what’s going on in the data set. Similarly, once you’ve completed your analysis and are ready to present your findings, data visualizations are a highly effective way to communicate your results to others.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Executive Data Science Capstone
The Executive Data Science Capstone, the specialization’s culminating project, is an opportunity for people who have completed all four EDS courses to apply what they've learned to a real-world scenario developed in collaboration with Zillow, a data-driven online real estate and rental marketplace, and DataCamp, a web-based platform for data science programming. Your task will be to lead a virtual data science team and make key decisions along the way to demonstrate that you have what it takes to shepherd a complex analysis project from start to finish.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
Create Your First Multithreaded Application in Java
In this 2-hour long project-based course, you will learn how to create a banking application that uses conditions and locks to ensure thread safety, practice designing programs that take advantage of multiple threads without losing data integrity, and learn how to apply multithreading to your own projects. By the end of the guided project, you will be able to create your own thread-safe Java multithreaded applications.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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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التعلم الذاتي
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15 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية