General Assembly Data Science Slides and Content

Boston Summer 2016

This is the github repo for all the lessons and projects.

Each lesson is structured as follows

And each project statement will also contain links to datasets.

Name		Name	Last commit message	Last commit date
Latest commit History 113 Commits
01_what_is_a_data_scientist		01_what_is_a_data_scientist
02_research_design_and_pandas		02_research_design_and_pandas
03_statistics_fundamentals		03_statistics_fundamentals
04_statistics_inference		04_statistics_inference
05_visualization		05_visualization
06_scikit_regression		06_scikit_regression
07_bias_variance		07_bias_variance
08_classification_knn		08_classification_knn
09_logistic_regression		09_logistic_regression
10_unsupervised		10_unsupervised
11_svm_imbalanced		11_svm_imbalanced
12_decision_trees		12_decision_trees
13-nlp		13-nlp
14_topic_modeling		14_topic_modeling
16_time_series		16_time_series
17_ts2		17_ts2
18_R_AWSML		18_R_AWSML
19_recap		19_recap
datasets		datasets
final_project		final_project
project_01		project_01
project_02		project_02
project_03		project_03
.gitignore		.gitignore
DS_CourseCurriculum__1_.pdf		DS_CourseCurriculum__1_.pdf
K-means on blobs.ipynb		K-means on blobs.ipynb
PCA eigenvalue.ipynb		PCA eigenvalue.ipynb
README.md		README.md
test.txt		test.txt
the_curse_of_dimensionality.png		the_curse_of_dimensionality.png