Statistics

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Deep Learning and Reinforcement Learning (Coursera) Coursera
IBM

Deep Learning and Reinforcement Learning (Coursera)

Dive into the world of cutting-edge Machine Learning disciplines with our 'Deep Learning and Reinforcement Learning' course. Learn about Neural Networks, modern Deep Learning architectures, and apply these concepts to Supervised and Unsupervised Learning scenarios. This course is ideal for those looking to advance their skills in AI and machine learning.

Sep 14th 2026
5-12 Weeks
Specialized Models: Time Series and Survival Analysis (Coursera) Coursera
IBM

Specialized Models: Time Series and Survival Analysis (Coursera)

Dive into the world of specialized machine learning models with 'Specialized Models: Time Series and Survival Analysis'. This comprehensive online course will equip you with essential skills in forecasting and analyzing complex datasets that involve time components or censored data. Perfect for professionals looking to advance their predictive modeling expertise.

Sep 14th 2026
4 Weeks
Getting Started with Teaching Data Science in Schools (FutureLearn) FutureLearn
University of Glasgow

Getting Started with Teaching Data Science in Schools (FutureLearn)

Embark on a journey to introduce data science into your classroom with our beginner-friendly course. Gain insights, practical teaching methods, and strategies to foster students' understanding of data interpretation and application in everyday life. This course is designed for educators looking to bridge the gap between traditional curriculum and the modern world's reliance on data.

Self Paced
3 Weeks
Basics of Statistical Inference and Modelling Using R (edX) EdX
University of Canterbury,UCx

Basics of Statistical Inference and Modelling Using R (edX)

Dive into the world of statistical analysis with 'Basics of Statistical Inference and Modelling Using R'. This course will equip you with a strong foundation in understanding why certain statistical methods work, how to implement them using R, and when to apply them. It's an essential step for anyone looking to delve deeper into data science.

Self Paced
Self-Paced
Introductory Statistics : Analyzing Data Using Graphs and Statistics (edX) EdX
Seoul National University,SNUx

Introductory Statistics : Analyzing Data Using Graphs and Statistics (edX)

Discover the fundamentals of statistics and delve into practical applications with 'Introductory Statistics: Analyzing Data Using Graphs and Statistics'. This course equips you with essential skills in data interpretation through graphing and statistical analysis, using compelling real-world examples to illustrate key concepts.

Self Paced
Self-Paced
Statistics for Data Science with Python (Coursera) Coursera
IBM

Statistics for Data Science with Python (Coursera)

Dive into the world of data analysis with our 'Statistics for Data Science with Python' course. Gain a solid understanding of statistical methods and their application in real-world scenarios. This hands-on course will teach you how to gather, summarize, visualize, and analyze data using Python and Jupyter Notebooks, essential skills for any aspiring data scientist.

Sep 14th 2026
5-12 Weeks
Introductory Statistics : Sample Survey and Instruments for Statistical Inference (edX) EdX
Seoul National University,SNUx

Introductory Statistics : Sample Survey and Instruments for Statistical Inference (edX)

Discover the fundamentals of sample surveys and statistical inference through practical examples in this introductory statistics course from edX. Gain insights into samples vs. populations, identify common survey biases, and understand probability errors in sampling to build a strong foundation for data analysis.

Self Paced
Self-Paced
Public Sector Debt Statistics (edX) EdX
International Monetary Fund - IMF,IMFx

Public Sector Debt Statistics (edX)

Discover the essential skills needed to compile and disseminate accurate and useful public sector debt statistics (PSDS) with this expert-led course from the International Monetary Fund's Statistics Department. This course delves into critical topics such as coverage rules, accounting principles, valuation techniques, classification systems, key methodological considerations, and the sources and methods used in compiling comprehensive PSDS data. Perfect for policy-makers, decision-makers, and anyone involved in financial statistics.

Self Paced
Self-Paced