Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
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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Course by
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Self Paced
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10 ساعات
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الإنجليزية
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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Course by
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Self Paced
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9 ساعات
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الإنجليزية
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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Course by
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Self Paced
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14 ساعات
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الإنجليزية
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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Course by
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Self Paced
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12 ساعات
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الإنجليزية
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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Course by
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Self Paced
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36 ساعات
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الإنجليزية
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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Course by
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Self Paced
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23 ساعات
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الإنجليزية
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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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Tools and Practices for Addressing Pandemic Challenges
An overview of the tools, techniques, and practices that can be enacted by policy makers, countries, and organizations to monitor, manage, and react to pandemics and mitigate and govern their impacts. An introductory, multidisciplinary course covering data science, social science, healthcare, and management, paving the way to various courses on specific matters. This course is part of the research project 'Pan-European Response to the Impacts of the COVID-19 and future Pandemics and Epidemics' (PERISCOPE, https://www.periscopeproject.eu/).
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
C Programming: Pointers and Memory Management - 4
In this course, we will examine a key concept, foundational to any programming language: the usage of memory. This course builds upon the basic concept of pointers, discussed in C Programming: Modular Programming and Memory Management, and introduces the more advanced usage of pointers and pointer arithmetic. Arrays of pointers and multidimensional arrays are addressed, and you will learn how to allocate memory for your own data during program execution.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more.
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Course by
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Self Paced
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24 ساعات
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الإنجليزية
Data Science Ethics
What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية
Intro to Analytic Thinking, Data Science, and Data Mining
Welcome to Introduction to Analytic Thinking, Data Science, and Data Mining. In this course, we will begin with an exploration of the field and profession of data science with a focus on the skills and ethical considerations required when working with data. We will review the types of business problems data science can solve and discuss the application of the CRISP-DM process to data mining efforts.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Data Science Tutorial
Start learning Data science with the W3Schools course. Data Science is about data gathering, analysis and decision-making. Data Science is about finding patterns in data, through analysis, and make future predictions. This is a structured and interactive version of the W3Schools Data science Tutorial. The course is self-paced with text based modules, practical interactive examples and exercises to check your understanding as you progress. W3schools is the world's largest web developer learning site. Start learning with our proven tutorials used by millions of learners!
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Data Science for Business Innovation
This is your chance to learn all about Data Science for Business innovation and future-proof your career. Match your business experience tech and analytics! The Data Science for Business Innovation nano-course is a compendium of the must-have expertise in data science for executives and managers to foster data-driven innovation. The course explains what Data Science is and why it is so hyped.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results!
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Course by
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Self Paced
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6 ساعات
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الإنجليزية
Unsupervised Algorithms in Machine Learning
One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.
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Course by
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Self Paced
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38 ساعات
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الإنجليزية
Code Free Data Science
The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Calculus for Machine Learning and Data Science
Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises.
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Course by
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Self Paced
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26 ساعات
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الإنجليزية
AI Workflow: Feature Engineering and Bias Detection
This is the third 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. Course 3 introduces you to the next stage of the workflow for our hypothetical media company. In this stage of work you will learn best practices for feature engineering, handling class imbalances and detecting bias in the data. Class imbalances can seriously affect the validity of your
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Analyze Datasets and Train ML Models using AutoML
In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Using Sensors With Your Raspberry Pi
This course on integrating sensors with your Raspberry Pi is course 3 of a Coursera Specialization and can be taken separately or as part of the specialization. Although some material and explanations from the prior two courses are used, this course largely assumes no prior experience with sensors or data processing other than ideas about your own projects and an interest in building projects with sensors. This course focuses on core concepts and techniques in designing and integrating any sensor, rather than overly specific examples to copy.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Extract, Transform, and Load Data
This course is designed for business and data professional seeking to learn the first technical phase of the data science process known as Extract, Transform and Load or ETL. Learners will be taught how to collect data from multiple sources so it is available to be transformed and cleaned and then will dive into collected data sets to prepare and clean data so that it can later be loaded into its ultimate destination. In the conclusion of the course learners will load data into its ultimate destination so that it can be analyzed and modeled.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية
Julia Scientific Programming
This course introduces you to Julia as a first programming language. Julia is a high-level, high-performance dynamic programming language developed specifically for scientific computing. This language will be particularly useful for applications in physics, chemistry, astronomy, engineering, data science, bioinformatics, and many more.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Regression and Classification
Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more! 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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35 ساعات
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الإنجليزية
Supervised Text Classification for Marketing Analytics
Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised machine learning and dive into real-world datasets through instructor-led tutorials in Python.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية