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.gitignore
.gitignore
 
 
README.md
README.md
 
 
c1_lec03_Python_Tables.ipynb
c1_lec03_Python_Tables.ipynb
 
 
c1_lec04_Expressions.ipynb
c1_lec04_Expressions.ipynb
 
 
c1_lec05_Strings_Tables.ipynb
c1_lec05_Strings_Tables.ipynb
 
 
c1_lec06_Census.ipynb
c1_lec06_Census.ipynb
 
 
c1_lec07_Charts.ipynb
c1_lec07_Charts.ipynb
 
 
c1_lec08_9a_histograms.ipynb
c1_lec08_9a_histograms.ipynb
 
 
c1_lec09b_functions_apply.ipynb
c1_lec09b_functions_apply.ipynb
 
 
c1_lec10_groups_pivot.ipynb
c1_lec10_groups_pivot.ipynb
 
 
c1_lec11_joins_maps.ipynb
c1_lec11_joins_maps.ipynb
 
 
c1_lec12_table_review.ipynb
c1_lec12_table_review.ipynb
 
 
c1_lec13_iteration.ipynb
c1_lec13_iteration.ipynb
 
 
c2_lab1.ipynb
c2_lab1.ipynb
 
 
c2_lab3_inference.ipynb
c2_lab3_inference.ipynb
 
 
c2_lec02_probability.ipynb
c2_lec02_probability.ipynb
 
 
c2_lec04_sampling.ipynb
c2_lec04_sampling.ipynb
 
 
c2_lec05_hypothesis_testing.ipynb
c2_lec05_hypothesis_testing.ipynb
 
 
c2_lec06_comparing_distributions.ipynb
c2_lec06_comparing_distributions.ipynb
 
 
c2_lec07_decisions_uncertainty.ipynb
c2_lec07_decisions_uncertainty.ipynb
 
 
c2_lec08_ABtesting.ipynb
c2_lec08_ABtesting.ipynb
 
 
c2_lec09_causality.ipynb
c2_lec09_causality.ipynb
 
 
c2_lec10_confidence_intervals.ipynb
c2_lec10_confidence_intervals.ipynb
 
 
c2_lec11_confidence_interpretation.ipynb
c2_lec11_confidence_interpretation.ipynb
 
 
c3_lab1_variance_correlation.ipynb
c3_lab1_variance_correlation.ipynb
 
 
c3_lec01_center_spread.ipynb
c3_lec01_center_spread.ipynb
 
 
c3_lec02_normal_curve.ipynb
c3_lec02_normal_curve.ipynb
 
 
c3_lec03_correlation.ipynb
c3_lec03_correlation.ipynb
 
 

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Berkeley_DataScience

Taking MOOC, Foundations of Data Science: Computational Thinking with Python: https://courses.edx.org/courses/course-v1:BerkeleyX+Data8.1x+1T2018/course/

Using standard modules (ie. sklearn, pandas, seaborn) rather than Berkeley's Datascience module used by instructors. My goal is to get more practice with these modules while reviewing and extending my data science knowledge.

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Taking MOOC, Foundations of Data Science: Computational Thinking with Python. Using standard modules (ie. sklearn, pandas, seaborn) rather than Berkeley's datascience module provided by instructors.

courses.edx.org/courses/course-v1:BerkeleyX+Data8.1x+1T2018/course/

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