Michael Galarnyk

Data Science Repo and blog for John Hopkins Coursera Courses. Please let me know if you have any questions.

Python tutorials in both Jupyter Notebook and youtube format.

Installations for Data Science. Anaconda, RStudio, Spark, TensorFlow, AWS (Amazon Web Services).

Probability and Statistics Using Python Data Science Masters Course at UCSD (DSE 210)

Homework/Classwork for my DSE 200 Python for Data Analysis Class at UC San Diego (UCSD)

Interview stuff for friends

Repo for my graduate data science machine learning class at UCSD (UC San Diego). This course provides a broad introduction to the practical side of machine-learning and data analysis. The topics covered in this class include topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, PCA and Gaussian mixture models.

Map-reduce, streaming analysis, and external memory algorithms and their implementation using the Hadoop and its eco-system: HBase, Hive, Pig and Spark. The class will include assignment of analyzing large existing databases.

Coursera machine learning specialization coursework (python based, University of Washington).