mGalarnyk/DSE210_Probability_Statistics_Python

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mGalarnyk/DSE210_Probability_Statistics_Python

mGalarnyk

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

DSE210_Probability_Statistics_Python

Looks best on google chrome. Probability and Statistics Using Python: Data Science Masters Course (DSE 210). Highly similar to UCSD's "CSE 250B. Principles of Artificial Intelligence: Learning Algorithms" course. Most of the early portions of the class are worksheet based, but the later portions are mostly in ipython notebook (numpy, sklearn, pandas).

IPython Notebooks for Assignments (From Newest to Oldest)

[Hypothesis testing](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/10_Hypothesis_Testing.ipynb) (1, 2, 6, 7, 8, 9, 10)

[Sampling](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/9_Sampling.ipynb) (1, 3, 5, 8, 9, 10, 11)

[Matrix factorization](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/8_Matrix_Factorization.ipynb) # 1,2,3,4 (PCA Projection, python),5 (PCA Projection, python)

[Clustering](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/7_Clustering.ipynb)

[Generative models 2](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/6_Generative_Models_number_9-FINAL.ipynb) Gaussian Classifier

[Generative models 1](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/5_Generative_Models_I_Class_Generators.ipynb) (object oriented, next up is pandas and sql)

[Random variable, expectation, and variance](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/4_RandomVariable_Expectation_Variance.ipynb) (1,2,3,6,7a,7c,8,12)

[Multiple events, conditioning, and independence](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/3_Multiple_events_%20conditioning_and_independence.ipynb) (1,2,3,5,6,10,15a,16)

[Probability spaces](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/2_Probability_spaces.ipynb) (1a,1b,1e,2,3,4a,5,6,7,14,16)

[Sets and counting](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/1_Sets_and_Counting_mGalarnyk.ipynb) (1,2,3,4,5,6)

Worksheets (From Newest to Oldest)

[Hypothesis testing](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet10Hypothesis_testing.pdf) (1, 2, 6, 7, 8, 9, 10)

[Sampling](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet9Sampling.pdf) (1, 3, 5, 8, 9, 10, 11)

[Matrix factorization](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet8Matrix_factorization.pdf) (1,2,3,4,5)

[Clustering](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet7Clustering.pdf) (all)

[Generative models 2](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet6GenerativeModels2.pdf) (#9 python based Gaussian Classifier)

[Generative models 1](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet5Generative_models_1.pdf) (all)

[Random variable, expectation, and variance](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet4Random_variable_expectation_and_variance.pdf) (1,2,3,6,7a,7c,8,12)

[Multiple events, conditioning, and independence](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet3_Multiple_events_%20conditioning_and_independence.pdf) (1,2,3,5,6,10,15a,16)

[Probability spaces](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet2_Probability_spaces.pdf) (1a,1b,1e,2,3,4a,5,6,7,14,16)

[Sets and counting](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/worksheets/worksheet1_Sets_and_counting.pdf) (1,2,3,4,5,6)

Other (Iris Dataset plus other scratch worksheet)

[K-Means, PCA, and Dendrogram on the Animals with Attributes Dataset](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/K-Means%2C%20PCA%2C%20and%20Dendrogram%20on%20the%20Animals%20with%20Attributes%20Dataset.ipynb)

[Iris Dataset](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/IRIS%20data%20set.ipynb)

[Scratch stats problems](https://github.com/mGalarnyk/DSE210_Probability_Statistics_Python/blob/master/Other_worksheet.pdf)

datasciencecoursera

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

Python_Tutorials

Python tutorials in both Jupyter Notebook and youtube format.

Jupyter Notebook

Installations_Mac_Ubuntu_Windows

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

Jupyter Notebook

DSE200_Python_for_Data_Analysis

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

Jupyter Notebook

DataScienceInterview

Interview stuff for friends

DSE220_Machine_Learning

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.

Jupyter Notebook

DSE230_Data_Analysis_Using_Hadoop_and_Spark_UCSD

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.

Jupyter Notebook

DSGO_IntroductionScikitLearn

Jupyter Notebook

Machine_Learning_Coursera

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

Jupyter Notebook

DSE201_Database_Management_Systems

Database Management Systems Data Science Masters Course (DSE 201)

Jupyter Notebook

Tutorial_Data

This is a repo to keep the data for my tutorials. This is to make it so people dont need a Kaggle account and such as much as possible.

UCSD_EXT_Data_Analytics_Python

Legally allowable public portion of the UCSD Extension course: Data Analytics Using Python (CSE-41204)

Jupyter Notebook

Machine_Learning_Froyo

Jupyter Notebook

LinkedInLearningResources

Resources for my LinkedIn Learning Courses

mgalarny

Jupyter Notebook

web_development

New repo for my web development certificate through UCSD extension. I am looking forward to the new courses!