Linkedin Learning Data Science Foundations Fundamentals-XQZT

Another exquisit release
Linkedin.Learning.Data.Science.Foundations.Fundamentals-XQZT
     Title: Data Science Foundations: Fundamentals
 Publisher: Linkedin Learning
  Category: N/A
      Size: 960M
     Files: 11F
      Date: 2022-02-04
  Course #: 3001273
 Published: Linkedin.Learning
   Updated: N/A
       URL: https://linkedin.com/learning/data-science-foundations-fundamentals-14537508
    Author: Barton Poulson
  Duration: 5:17:15
 Exer/Code: [ ]
Installation:
Unpack that shit, run that shit
Description:
Get an accessible, nontechnical overview of data science,
covering the vocabulary, skills, jobs, tools, and techniques of the

13M	01.01-getting_started.mkv
9.2M	02.01-supply_and_demand_for_data_science.mkv
9.7M	02.02-the_data_science_venn_diagram.mkv
28M	02.03-the_data_science_pathway.mkv
14M	02.04-the_crisp-dm_model_in_data_science.mkv
16M	02.05-roles_and_teams_in_data_science.mkv
14M	02.06-the_role_of_questions_in_data_science.mkv
22M	03.01-artificial_intelligence.mkv
31M	03.02-machine_learning.mkv
28M	03.03-deep_learning_neural_networks.mkv
17M	03.04-big_data.mkv
14M	03.05-predictive_analytics.mkv
15M	03.06-prescriptive_analytics.mkv
12M	03.07-business_intelligence.mkv
20M	04.01-bias.mkv
18M	04.02-security.mkv
16M	04.03-legal.mkv
23M	04.04-explainable_ai.mkv
14M	04.05-agency_of_algorithms_and_decision-makers.mkv
17M	05.01-data_preparation.mkv
33M	05.02-labeling_data.mkv
15M	05.03-in-house_data.mkv
17M	05.04-open_data.mkv
7.2M	05.05-apis.mkv
29M	05.06-scraping_data.mkv
16M	05.07-creating_data.mkv
16M	05.08-passive_collection_of_training_data.mkv
15M	05.09-self-generated_data.mkv
13M	05.10-data_vendors.mkv
15M	05.11-data_ethics.mkv
14M	06.01-the_enumeration_of_explicit_rules.mkv
20M	06.02-the_derivation_of_rules_from_data_analysis.mkv
11M	06.03-the_generation_of_implicit_rules.mkv
17M	07.01-applications_for_data_analysis.mkv
14M	07.02-languages_for_data_science.mkv
8.4M	07.03-automl.mkv
9.5M	07.04-machine_learning_as_a_service.mkv
16M	08.01-sampling_and_probability.mkv
16M	08.02-algebra.mkv
8.3M	08.03-calculus.mkv
16M	08.04-optimization_and_the_combinatorial_explosion.mkv
11M	08.05-bayes_theorem.mkv
14M	09.01-supervised_vs._unsupervised_learning.mkv
19M	09.02-descriptive_analyses.mkv
17M	09.03-clustering.mkv
20M	09.04-dimensionality_reduction.mkv
16M	09.05-anomaly_detection.mkv
22M	10.01-supervised_learning_with_predictive_models.mkv
26M	10.02-time-series_data.mkv
14M	10.03-classifying.mkv
16M	10.04-feature_selection_and_creation.mkv
22M	10.05-aggregating_models.mkv
20M	10.06-validating_models.mkv
20M	11.01-generative_adversarial_networks_(gans).mkv
19M	11.02-reinforcement_learning.mkv
9.9M	12.01-the_importance_of_interpretability.mkv
20M	12.02-interpretable_methods.mkv
8.7M	12.03-actionable_insights.mkv
9.7M	13.01-next_steps_and_additional_resources.mkv
956M	total