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Our Courses
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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Self Paced
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23
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English
MathTrackX: Probability
Understand probability and how it manifests in the world around us.
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Course by
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Self Paced
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16
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English
Supply Chain Analytics
Master and apply the core methodologies used in supply chain analysis and modeling, including statistics, regression, optimization and probability - part of the MITx Supply Chain Management MicroMasters Credential.
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English
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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Self Paced
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English
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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Self Paced
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English
Introduction to Probability
Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty.
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Self Paced
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30
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English
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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Self Paced
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12
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English
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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Self Paced
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English
Pre-MBA Statistics
Welcome to the Pre-MBA Statistics course! By the end of this course, you will be able to describe how statistics can be used to summarize, analyze, and interpret data. This course introduces you to some aspects of descriptive and inferential statistics. You will learn to distinguish between various data types and describe the operations that you can execute with each type of data and the right tools to use. The course also discusses the concepts of probability, which form the backbone of statistical analysis.
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Course by
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Self Paced
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24 hours
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English
Business Statistics and Analysis
The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. Informed by our world-class Data Science master's and PhD course material, you’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions.
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Self Paced
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English
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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13 hours
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English
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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Self Paced
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English
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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Self Paced
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English
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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Self Paced
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English
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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Self Paced
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English
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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Self Paced
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English
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 hours
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English
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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Self Paced
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30
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English
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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Self Paced
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English
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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Self Paced
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English
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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Self Paced
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7 hours
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English
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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Self Paced
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66 hours
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English
Uncertainty and Research
This course teaches the fundamentals of scientific research. We approach the research process as a means of systematically reducing uncertainty and demonstrate how conducting a scientific investigation can be posed as an exercise in Bayesian uncertainty quantification. We begin by exploring the scientific landscape to understand the different types of research, where they are conducted, how they are supported, and why each of these types of research is important. We then formalize scientific inquiry and the scientific method and elaborate the research process and its scientific merits.
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Self Paced
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15 hours
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English
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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Self Paced
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11 hours
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English
Causal Inference
This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships. We will study methods for collecting data to estimate causal relationships.
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Course by
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Self Paced
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13 hours
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English