Google Code for Remarketing Tag - Bloom
YCBS 255 Computational Applied Statistics (4 CEUs)
This course introduces fundamental statistical machine learning concepts and tools using Python. Emphasis is placed on the following subjects: descriptive statistics, statistical distributions, random number generation, basic data visualization; linear regression; basic classification; error estimation: cross-validation, bias-variance trade-off; shrinkage methods; dimension reduction; beyond linearity: smoothing splines, local regression, additive models; tree and ensemble methods; powerful classifiers; unsupervised learning.
YCBS 256 Data Science for Business Decisions (4 CEUs)
This course aims to provide an overview of how data science can help drive business decisions and create new business models. The emphasis is placed on data strategy and how to move from data to insight. The course explores the data science process and how companies could surmount the different challenges they face when implementing a data driven business including ethics, data governance and privacy. The evolution of data technology and storage, as well as application of data science tools and techniques to different business areas such as customer and web analytics, operations analytics, human resources related analytics are explored through examples from various fields such as retail, healthcare and marketing.
YCBS 257 Data at Scale (6 CEUs)
Fall 2018, Winter 2019
This course familiarizes participants with different aspects of large data sets and how they are managed both on site and in the Cloud. Emphasis is placed on providing participants with hands-on experience from data ingestion to analysis of large data sets, both data-atrest or data-in-motion (streaming data), including defining Big Data and its 5 V's: Volume, Velocity, Variety, Veracity, and Value. Architectures of distributed databases and storage, ecosystems such as Hadoop and Spark are covered followed by introduction to Scala, Spark-Shell and PySpark.
YCBS 258 Practical Machine Learning (6 CEUs)
Fall 2018, Winter 2019
This course aims to introduce participants to essential machine learning methods and techniques through an end-to-end machine learning project. Emphasis is placed on practical experience with machine learning using Python programming language, scikit-learn and TensorFlow, as well as on understanding classification and training models. The course will provide an introduction to artificial Neural Networks, deep learning, convolutional and recurrent neural nets and reinforcement learning.
YCBS 299 Data Science Capstone Project (6 CEUs)
Winter 2019, Spring/Summer 2019
This capstone course supported by our industry partners will provide the opportunity to apply all the knowledge gained during the program in order to build a full data science pipeline from preparing and visualizing data, building and testing models, analyzing results and deriving business insights from their analysis. The focus is placed on communicating the insights gleaned from the data analysis through visualizations and on presenting the recommendations reached.