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[ FreeCourseWeb com ] Becoming a Good Data Science Customer
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01 - Introduction
01 - Good data science customers ask critical questions.mp4
01 - Good data science customers ask critical questions.srt
02 - What you should know for this course.mp4
02 - What you should know for this course.srt
02 - 1. Tools of the Trade
01 - Stages of data workflow.mp4
01 - Stages of data workflow.srt
02 - Data storage options.mp4
02 - Data storage options.srt
03 - Data sources.mp4
03 - Data sources.srt
04 - Ensuring data quality.mp4
04 - Ensuring data quality.srt
05 - Coding languages and repositories.mp4
05 - Coding languages and repositories.srt
06 - Data products.mp4
06 - Data products.srt
07 - Data workflow exercise.mp4
07 - Data workflow exercise.srt
03 - 2. Descriptive Statistics Foundations
01 - Data limitations.mp4
01 - Data limitations.srt
02 - Summary statistics.mp4
02 - Summary statistics.srt
03 - Correlations.mp4
03 - Correlations.srt
04 - Testing hypothesis Effect sizes and p-values.mp4
04 - Testing hypothesis Effect sizes and p-values.srt
05 - Common mistakes.mp4
05 - Common mistakes.srt
04 - 3. Making Good Decisions with Data
01 - Causality.mp4
01 - Causality.srt
02 - Benefits of randomization.mp4
02 - Benefits of randomization.srt
03 - What if you can't randomize.mp4
03 - What if you can't randomize.srt
04 - Spotting biases.mp4
04 - Spotting biases.srt
05 - Matching description to bias exercise.mp4
05 - Matching description to bias exercise.srt
05 - 4. Customer Segmentation
01 - What projects involve unsupervised machine learning.mp4
01 - What projects involve unsupervised machine learning.srt
02 - Reducing dimensions.mp4
02 - Reducing dimensions.srt
03 - Clustering algorithms.mp4
03 - Clustering algorithms.srt
06 - 5. Predictive Modeling
01 - What projects involve predictive modeling.mp4
01 - What projects involve predictive modeling.srt
02 - Feature selection.mp4
02 - Feature selection.srt
03 - Model training and testing (data partitioning).mp4
03 - Model training and testing (data partitioning).srt
04 - Model tuning.mp4
04 - Model tuning.srt
05 - Measuring model performance.mp4
05 - Measuring model performance.srt
06 - Confusion matrix.mp4
06 - Confusion matrix.srt
07 - Customer simulation exercise.mp4
07 - Customer simulation exercise.srt
07 - 6. Other Modeling Methods
01 - Natural language processing (NLP).mp4
01 - Natural language processing (NLP).srt
02 - Geospatial analysis.mp4
02 - Geospatial analysis.srt
03 - Computer vision.mp4
03 - Computer vision.srt
04 - Network analysis.mp4
04 - Network analysis.srt
05 - Large language models (LLM).mp4
05 - Large language models (LLM).srt
08 - 7. Ethics
01 - Data science code of ethics.mp4
01 - Data science code of ethics.srt
02 - Bias and fairness.mp4
02 - Bias and fairness.srt
03 - Data drift.mp4
03 - Data drift.srt
04 - Privacy and security.mp4
04 - Privacy and security.srt
09 - Conclusion
01 - Next steps.mp4
01 - Next steps.srt
Bonus Resources.txt
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