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| 000_appetite_whetting_1.pdf |
1.00MB |
| 000b_appetite_whetting_2.pdf |
1.14MB |
| 001_context.pdf |
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| 002_dimensions.pdf |
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| 003_this_course_1.pdf |
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| 004_this_course_2.pdf |
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| 005_escience.pdf |
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| 006_big_data.pdf |
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| 007_logistics.pdf |
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| 008_data_models.pdf |
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| 009_relational_motivation.pdf |
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| 010_relational_algebra_intro.pdf |
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| 011.5_relational_algebra_union_diff_select.pdf |
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| 011.6_relational_algebra_project_cross_equijoin.pdf |
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| 011.7_theta_join.pdf |
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| 011.8_interpreting_complicated_sql.pdf |
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| 012_physical_optimization.pdf |
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| 014_logical_data_independence.pdf |
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| 015_scalability.pdf |
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| 016_parallel_thinking.pdf |
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| 017_map_reduce_abstraction.pdf |
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| 018_map_reduce_pseudocode.pdf |
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| 019_map_reduce_text_examples.pdf |
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| 020_map_reduce_join_social_examples.pdf |
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| 021_map_reduce_matrix_multiply.pdf |
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| 022_map_reduce_implementation_overview.pdf |
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| 023_parallel_databases.pdf |
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| 024_mapreduce_and_databases.pdf |
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| 025_mapreduce_and_databases_experiments.pdf |
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| 026_nosql_intro.pdf |
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| 027_eventual_consistency.pdf |
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| 029_dynamo.pdf |
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| 030_couchdb.pdf |
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| 031_bigtable.pdf |
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| 040_effect_size_meta_analysis_heteroskedasticity.pdf |
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| 041_fraud_benfords_law.pdf |
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| 042_multiple_hypothesis_testing_CORRECTED.pdf |
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| 049_rules_1.pdf |
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| 052_information_gain.pdf |
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| 053_overfitting.pdf |
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| 055_bootstrap.pdf |
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| 057_random_forests.pdf |
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| 058_nearest_neighbor.pdf |
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| 060_gradient_descent_part_2.pdf |
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| 061_intuition_logistic_regression_svms.pdf |
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| 065_kmeans.pdf |
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| 066_dbscan.pdf |
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| 077b_graph_histograms.pdf |
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| 078_structural_analysis_tasks.pdf |
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| 079_pagerank.pdf |
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| 080_traversal_tasks.pdf |
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| 081_pattern_matching.pdf |
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| 082_relational_algebra_for_graph_tasks.pdf |
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| 083_prism_example.pdf |
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| 084_evaluating_recursive_programs.pdf |
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| 085_optimizing_recursive_programs_in_mr.pdf |
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| 086_graph_representations.pdf |
423.76KB |
| 087_pagerank_mapreduce_pregel.pdf |
803.64KB |
| 1_instructions.pdf |
260.96KB |
| 10 - 1 - 01 Guest Segment Aaron Kimball Wibidata.mp4 |
21.26MB |
| 10 - 2 - 02 Guest Segment Karen Hsu Datameer.mp4 |
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| 1 - 10 - Logistics (742).mp4 |
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| 1 - 10 - Logistics (742).srt |
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| 1 - 11 - Twitter Assignment Getting Started with Problem 0 and Problem 1.mp4 |
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| 1 - 1 - Appetite Whetting Part 1 (1538).mp4 |
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| 1 - 1 - Appetite Whetting Part 1 (1538).srt |
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| 1 - 2 - Appetite Whetting Part 2 (1344).mp4 |
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| 1 - 2 - Appetite Whetting Part 2 (1344).srt |
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| 1 - 3 - Context (930).mp4 |
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| 1 - 3 - Context (930).srt |
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| 1 - 4 - Dimensions (1024).mp4 |
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| 1 - 4 - Dimensions (1024).srt |
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| 1 - 5 - This Course Part 1 (1402).mp4 |
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| 1 - 7 - eScience (1146).mp4 |
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| 1 - 8 - Big Data (1436).mp4 |
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| 1 - 9 - Guest Lecture Biomedical Informatics (1024).mp4 |
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| 2_instructions.pdf |
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| 2 - 10 - Declarative Languages (1030).mp4 |
15.28MB |
| 2 - 10 - Declarative Languages (1030).srt |
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| 2 - 11 - Logical Data Independence (1123).mp4 |
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| 2 - 11 - Logical Data Independence (1123).srt |
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| 2 - 1 - From Data Models to Databases (1035).mp4 |
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| 2 - 1 - From Data Models to Databases (1035).srt |
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| 2 - 2 - Motivating Relational Algebra (857).mp4 |
12.66MB |
| 2 - 2 - Motivating Relational Algebra (857).srt |
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| 2 - 3 - Relational Algebra Introduction (1058).mp4 |
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| 2 - 3 - Relational Algebra Introduction (1058).srt |
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| 2 - 4 - Relational Algebra Details Union Diff Select (1053).mp4 |
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| 2 - 4 - Relational Algebra Details Union Diff Select (1053).srt |
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| 2 - 5 - Relational Algebra Details Project Cross Product Equi-Join (1106).mp4 |
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| 2 - 5 - Relational Algebra Details Project Cross Product Equi-Join (1106).srt |
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| 2 - 6 - Relational Algebra Details Theta-Join (834).mp4 |
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| 2 - 6 - Relational Algebra Details Theta-Join (834).srt |
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| 2 - 7 - SQL for Data Science Interpreting Complicated SQL (1212).mp4 |
18.23MB |
| 2 - 7 - SQL for Data Science Interpreting Complicated SQL (1212).srt |
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| 2 - 8 - SQL for Data Science User-Defined Functions (759).mp4 |
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| 2 - 8 - SQL for Data Science User-Defined Functions (759).srt |
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| 2 - 9 - Physical Optimization (1114).mp4 |
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| 2 - 9 - Physical Optimization (1114).srt |
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| 3_js_quiz.pdf |
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| 3_python_instructions.pdf |
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| 3 - 10 - Comparing MapReduce and Databases (0639).mp4 |
8.95MB |
| 3 - 10 - Comparing MapReduce and Databases (0639).srt |
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| 3 - 11 - Experimental Results MR and DB (1501).mp4 |
20.16MB |
| 3 - 11 - Experimental Results MR and DB (1501).srt |
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| 3 - 1 - Scalability Basics (1618).mp4 |
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| 3 - 1 - Scalability Basics (1618).srt |
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| 3 - 2 - Parallel Processing Patterns (1126).mp4 |
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| 3 - 2 - Parallel Processing Patterns (1126).srt |
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| 3 - 3 - MapReduce Abstractions (1117).mp4 |
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| 3 - 3 - MapReduce Abstractions (1117).srt |
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| 3 - 4 - MapReduce Pseudocode (754).mp4 |
12.28MB |
| 3 - 4 - MapReduce Pseudocode (754).srt |
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| 3 - 5 - MapReduce Text Examples (958).mp4 |
16.16MB |
| 3 - 5 - MapReduce Text Examples (958).srt |
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| 3 - 6 - MapReduce Relational Join Social Example (1317).mp4 |
18.74MB |
| 3 - 6 - MapReduce Relational Join Social Example (1317).srt |
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| 3 - 7 - MapReduce Matrix Multiply Example (931).mp4 |
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| 3 - 7 - MapReduce Matrix Multiply Example (931).srt |
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| 3 - 8 - MapReduce Implementation Overview (1338).mp4 |
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| 3 - 8 - MapReduce Implementation Overview (1338).srt |
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| 3 - 9 - Parallel Databases (1618).mp4 |
22.69MB |
| 3 - 9 - Parallel Databases (1618).srt |
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| 4_aws-setup.pdf |
270.41KB |
| 4_quiz.pdf |
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| 4 - 1 - Statistics Intro (1036).mp4 |
16.87MB |
| 4 - 1 - Statistics Intro (1036).srt |
15.54KB |
| 4 - 2 - Publication Bias (845).mp4 |
13.01MB |
| 4 - 2 - Publication Bias (845).srt |
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| 4 - 3 - Effect Size Meta-analysis Heteroskedasticity (931).mp4 |
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| 4 - 3 - Effect Size Meta-analysis Heteroskedasticity (931).srt |
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| 4 - 4 - Fraud and Benfords Law (1055).mp4 |
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| 4 - 4 - Fraud and Benfords Law (1055).srt |
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| 4 - 5 - Multiple Hypothesis Testing (1122).mp4 |
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| 4 - 5 - Multiple Hypothesis Testing (1122).srt |
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| 4 - 6 - Recap and Big Data (1139).mp4 |
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| 4 - 6 - Recap and Big Data (1139).srt |
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| 4 - 7 - Bayesian Intro (759).mp4 |
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| 4 - 7 - Bayesian Intro (759).srt |
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| 4 - 8 - Bayes Rule (1153).mp4 |
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| 4 - 8 - Bayes Rule (1153).srt |
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| 5 - 10 - 10 Ensembles Bagging and Boosting (0919).mp4 |
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| 5 - 10 - 10 Ensembles Bagging and Boosting (0919).srt |
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| 5 - 1 - 01 Introduction to Machine Learning Part 1 (0754).mp4 |
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| 5 - 1 - 01 Introduction to Machine Learning Part 1 (0754).srt |
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| 5 - 11 - 11 Random Forests (1116).mp4 |
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| 5 - 11 - 11 Random Forests (1116).srt |
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| 5 - 12 - 12 k Nearest Neighbors (1143).mp4 |
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| 5 - 12 - 12 k Nearest Neighbors (1143).srt |
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| 5 - 2 - 02 Introduction to Machine Learning Part 2 (0523).mp4 |
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| 5 - 2 - 02 Introduction to Machine Learning Part 2 (0523).srt |
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| 5 - 3 - 03 Rules Part 1 (0919).mp4 |
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| 5 - 3 - 03 Rules Part 1 (0919).srt |
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| 5 - 4 - 04 Rules Part 2 (0539).mp4 |
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| 5 - 4 - 04 Rules Part 2 (0539).srt |
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| 5 - 5 - 05 Decision Trees Entropy (1051).mp4 |
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| 5 - 5 - 05 Decision Trees Entropy (1051).srt |
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| 5 - 6 - 06 Information Gain (1143).mp4 |
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| 5 - 6 - 06 Information Gain (1143).srt |
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| 5 - 7 - 07 Overfitting (1104).mp4 |
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| 5 - 7 - 07 Overfitting (1104).srt |
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| 5 - 8 - 08 Evaluation and Cross-Validation (1046).mp4 |
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| 5 - 8 - 08 Evaluation and Cross-Validation (1046).srt |
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| 5 - 9 - 09 The Bootstrap (0413).mp4 |
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| 5 - 9 - 09 The Bootstrap (0413).srt |
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| 6_VisualizationAssignment.twbx |
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| 6 - 10 - 10 Evaluation (325).mp4 |
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| 6 - 10 - 10 Evaluation (325).srt |
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| 6 - 1 - 01 Introduction (717).mp4 |
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| 6 - 1 - 01 Introduction (717).srt |
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| 6 - 2 - 02 Data Types (937).mp4 |
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| 6 - 2 - 02 Data Types (937).srt |
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| 6 - 3 - 03 Data Types (Exercises) (407).mp4 |
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| 6 - 3 - 03 Data Types (Exercises) (407).srt |
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| 6 - 4 - 04 Data Dimensions (308).mp4 |
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| 6 - 4 - 04 Data Dimensions (308).srt |
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| 6 - 5 - 05 Visual Encoding (Part 1) (638).mp4 |
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| 6 - 5 - 05 Visual Encoding (Part 1) (638).srt |
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| 6 - 6 - 06 Visual Encoding (Part 2) (254).mp4 |
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| 6 - 6 - 06 Visual Encoding (Part 2) (254).srt |
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| 6 - 7 - 07 Visual Perception (Part 1) (439).mp4 |
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| 6 - 7 - 07 Visual Perception (Part 1) (439).srt |
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| 6 - 8 - 08 Visual Perception (Part 2) (418).mp4 |
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| 6 - 8 - 08 Visual Perception (Part 2) (418).srt |
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| 6 - 9 - 09 Visual Perception (Part 3) (356).mp4 |
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| 6 - 9 - 09 Visual Perception (Part 3) (356).srt |
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| 7 - 10 - 10 Optimizing MapReduce for Graph Traversal (0820).mp4 |
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| 7 - 1 - 01 Graph Basics (0622).mp4 |
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| 7 - 11 - 11 Graph Representations (0651).mp4 |
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| 7 - 12 - 12 PageRank in MapReduce and Pregel (1042).mp4 |
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| 7 - 2 - 02 Structure Degree Histograms (0814).mp4 |
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| 7 - 3 - 03 Structure Diameter Connectivity Centrality (0656).mp4 |
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| 7 - 4 - 04 Traversal PageRank (0637).mp4 |
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| 7 - 5 - 05 Traversal Spanning Trees Circuits Flows (0657).mp4 |
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| 7 - 6 - 06 Patterns Triangles SPARQL Datalog (1002).mp4 |
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| 7 - 7 - 07 Patterns Relational Algebra for Graph Query (0826).mp4 |
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| 7 - 8 - 08 PRISM Example in Datalog (1110).mp4 |
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| 7 - 9 - 09 Evaluating Graph Traversal Queries (0829).mp4 |
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| 8 - 10 - Pig Functions (1141).mp4 |
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| 8 - 10 - Pig Functions (1141).srt |
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| 8 - 11 - Pig Join and Co-Group Join (1610).mp4 |
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| 8 - 11 - Pig Join and Co-Group Join (1610).srt |
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| 8 - 12 - Pig Evaluation (1011).mp4 |
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| 8 - 1 - NoSQL Introduction (830).mp4 |
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| 8 - 1 - NoSQL Introduction (830).srt |
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| 8 - 2 - Eventual Consistency (1856).mp4 |
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| 8 - 2 - Eventual Consistency (1856).srt |
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| 8 - 3 - Example Memcached (1637).mp4 |
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| 8 - 4 - Example Dynamo (1016).mp4 |
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| 8 - 4 - Example Dynamo (1016).srt |
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| 8 - 5 - Example CouchDB (1000).mp4 |
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| 8 - 5 - Example CouchDB (1000).srt |
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| 8 - 6 - Example BigTable (1105).mp4 |
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| 8 - 6 - Example BigTable (1105).srt |
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| 8 - 7 - Example Other Google Systems (1618).mp4 |
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| 8 - 7 - Example Other Google Systems (1618).srt |
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| 8 - 8 - Response to NoSQL Systems (1444).mp4 |
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| 8 - 8 - Response to NoSQL Systems (1444).srt |
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| 8 - 9 - Pig Intro (1202).mp4 |
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| 8 - 9 - Pig Intro (1202).srt |
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| 9 - 1 - 01 Gradient Descent Part 1 (0718).mp4 |
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| 9 - 1 - 01 Gradient Descent Part 1 (0718).srt |
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| 9 - 2 - 02 Gradient Descent Part 2 (0629).mp4 |
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| 9 - 2 - 02 Gradient Descent Part 2 (0629).srt |
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| 9 - 3 - 03 Intuition for Logistic Regression and Support Vector Machines (1055).mp4 |
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| 9 - 3 - 03 Intuition for Logistic Regression and Support Vector Machines (1055).srt |
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| 9 - 4 - 04 Intuition for Regularization (0659).mp4 |
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| 9 - 4 - 04 Intuition for Regularization (0659).srt |
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| 9 - 5 - 05 Stochastic Gradient Descent Minibatches Parallelization (0853).mp4 |
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| 9 - 5 - 05 Stochastic Gradient Descent Minibatches Parallelization (0853).srt |
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| 9 - 6 - 06 Unsupervised Learning (0611).mp4 |
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| 9 - 6 - 06 Unsupervised Learning (0611).srt |
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| 9 - 7 - 07 K-means (0556).mp4 |
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| 9 - 7 - 07 K-means (0556).srt |
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| 9 - 8 - 08 DBSCAN (0913).mp4 |
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| 9 - 8 - 08 DBSCAN (0913).srt |
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| Class Virtual Machine.pdf |
88.58KB |
| Course Logistics.pdf |
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| Coursera-Data-Science-Ubuntu.ova |
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| Github Instructions.pdf |
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| guests-datameer_beyond_mapreduce.pdf |
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| guests-kiji-uw-data-science.pdf |
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| Infovis Aragon 10 Evaluation.pdf |
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| Infovis Aragon 1 Introduction.pdf |
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| Infovis Aragon 2 Data Types.pdf |
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| Infovis Aragon 3 Data Types (Exercises).pdf |
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| Infovis Aragon 4 Data Dimensions.pdf |
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| Infovis Aragon 5 Visual Encoding (Part 1).pdf |
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| Infovis Aragon 6 Visual Encoding (Part 2).pdf |
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| Infovis Aragon 7 Visual Perception (Part 1).pdf |
884.68KB |
| Infovis Aragon 8 Visual Perception (Part 2).pdf |
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| Infovis Aragon 9 Visual Perception (Part 3).pdf |
330.23KB |
| Introduction to JSMapreduce.mp4 |
48.88MB |
| Python Resources.pdf |
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| Running Tableau on AWS.pdf |
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| Syllabus.pdf |
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| TableauDesktop.exe |
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| tips.txt |
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