Women in Big Data Global


Training Links

Welcome to the Women in Big Data Training Resources!

Here you will find information on data related training resources.


Eight Types of Data

Great breakdown of data sets into categories. Don’t get stuck figuring out what can be analyzed.

Comprehensive Repository of Data Science and ML Resources

Twenty-nine resources, mostly in the form of tutorials, covering most important topics in data science.

Fifteen-minute guide to choose effective courses for machine learning and data science

A useful walk through a data science learning journey, complete with course recommendations! Advice for young professionals in non-CS field who wants to learn and contribute to data science/machine learning. Curated from personal experience.

Data and design are tools that, together, build great experiences for your users

Data capture, management, and analysis builds a bridge between design, user experience, and business relevance.

Deep Learning

Take Machine Learning to the next level

Machine Learning

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Omidyar Network Learning Series: What is Big Data, Small Credit?

This learning series explores an innovative business model that leverages big data to help consumers who are invisible to lenders to gain access to credit. Find out how Big Data, Small Credit can help consumers to improve their lives, financial service providers to open new markets, and emerging markets to bolster their economies.