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دوراتنا
Probability Theory
This course provides an introduction to probability theory. You will encounter discrete and continuous random variables and learn in which situations they appear, what their properties are and how they interact.
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Course by
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التعلم الذاتي
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23
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الإنجليزية
MathTrackX: Probability
Understand probability and how it manifests in the world around us.
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Course by
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التعلم الذاتي
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16
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الإنجليزية
Statistics 2 Part 1: Probability and Distribution Theory
The third in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
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Course by
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الإنجليزية
Statistics 1 Part 1: Introductory statistics, probability and estimation
The first in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
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Course by
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الإنجليزية
Introduction to Probability
Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty.
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Course by
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30
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الإنجليزية
Data Science: Probability
Learn probability theory -- essential for a data scientist -- using a case study on the financial crisis of 2007-2008.
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Course by
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Self Paced
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12
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الإنجليزية
Fat Chance: Probability from the Ground Up
Increase your quantitative reasoning skills through a deeper understanding of probability and statistics.
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Course by
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التعلم الذاتي
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30
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الإنجليزية
Risk Management
This 4-course Specialization from the New York Institute of Finance (NYIF) is intended for STEM undergraduates, finance practitioners, bank and investment managers, business managers, regulators, and policymakers. This Specialization will teach you how to measure, assess, and manage risk in your organization.
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Course by
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Self Paced
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الإنجليزية
Computer Vision Basics
By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence.
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Course by
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13 ساعات
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الإنجليزية
Introduction to Computational Statistics for Data Scientists
The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST).
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Course by
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Self Paced
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الإنجليزية
Introduction to Topic Modelling in R
By the end of this project, you will know how to load and pre-process a data set of text documents by converting the data set into a document feature matrix and reducing it’s dimensionality. You will also know how to run an unsupervised machine learning LDA topic model (Latent Dirichlet Allocation). You will know how to plot the change in topics over time as well as explore the distribution of topic probability in each document.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Bayesian Statistics
This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution.
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Course by
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Self Paced
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الإنجليزية
Advanced Statistics for Data Science
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression. This specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications.
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Course by
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Self Paced
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الإنجليزية
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry.
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Course by
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Self Paced
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الإنجليزية
Data Science Math Skills
Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Data Literacy
This specialization is intended for professionals seeking to develop a skill set for interpreting statistical results. Through four courses and a capstone project, you will cover descriptive statistics, data visualization, measurement, regression modeling, probability and uncertainty which will prepare you to interpret and critically evaluate a quantitative analysis.
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Course by
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Self Paced
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الإنجليزية
Statistics for Data Science with Python
This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Probability and Statistics IV: Confidence Intervals and Hypothesis Tests
This course covers two important methodologies in statistics – confidence intervals and hypothesis testing. Confidence intervals allow us to make probabilistic statements such as: “We are 95% sure that Candidate Smith’s popularity is 52% +/- 3%.” Hypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug?"
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الإنجليزية
Statistical Analysis with R for Public Health
Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis.
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Course by
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Self Paced
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الإنجليزية
Computer Communications
This specialization is developed for seniors and fresh graduate students to understand fundamental network architecture concepts and their impacts on cyber security, to develop skills and techniques required for network protocol design, and prepare for a future of constant change through exposure to network design alternatives. Students will require a prior knowledge of C programming, an understanding of math probability and a computer science background is a plus.
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Course by
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Self Paced
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الإنجليزية
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Econometrics: Methods and Applications
Welcome! Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making. * What do I learn? When you know econometrics, you are able to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from macroeconomics to finance and marketing.
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Course by
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التعلم الذاتي
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66 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
Supervised Machine Learning: Classification
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models.
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Course by
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Self Paced
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25 ساعات
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الإنجليزية
Think Again III: How to Reason Inductively
Want to solve a murder mystery? What caused your computer to fail? Who can you trust in your everyday life? In this course, you will learn how to analyze and assess five common forms of inductive arguments: generalizations from samples, applications of generalizations, inference to the best explanation, arguments from analogy, and causal reasoning.
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Course by
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Self Paced
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24 ساعات
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الإنجليزية