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[ TutGator com ] Linkedin - Data Science Foundations - Fundamentals (2022)
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01. Introduction
001. Getting started.en.srt
001. Getting started.mp4
02. What Is Data Science
002. Supply and demand for data science.en.srt
002. Supply and demand for data science.mp4
003. The data science Venn diagram.en.srt
003. The data science Venn diagram.mp4
004. The data science pathway.en.srt
004. The data science pathway.mp4
005. The CRISP-DM model in data science.en.srt
005. The CRISP-DM model in data science.mp4
006. Roles and teams in data science.en.srt
006. Roles and teams in data science.mp4
007. The role of questions in data science.en.srt
007. The role of questions in data science.mp4
03. The Place of Data Science in the Data Universe
008. Artificial intelligence.en.srt
008. Artificial intelligence.mp4
009. Machine learning.en.srt
009. Machine learning.mp4
010. Deep learning neural networks.en.srt
010. Deep learning neural networks.mp4
011. Big data.en.srt
011. Big data.mp4
012. Predictive analytics.en.srt
012. Predictive analytics.mp4
013. Prescriptive analytics.en.srt
013. Prescriptive analytics.mp4
014. Business intelligence.en.srt
014. Business intelligence.mp4
04. Ethics and Agency
015. Bias.en.srt
015. Bias.mp4
016. Security.en.srt
016. Security.mp4
017. Legal.en.srt
017. Legal.mp4
018. Explainable AI.en.srt
018. Explainable AI.mp4
019. Agency of algorithms and decision-makers.en.srt
019. Agency of algorithms and decision-makers.mp4
05. Sources of Data
020. Data preparation.en.srt
020. Data preparation.mp4
021. Labeling data.en.srt
021. Labeling data.mp4
022. In-house data.en.srt
022. In-house data.mp4
023. Open data.en.srt
023. Open data.mp4
024. APIs.en.srt
024. APIs.mp4
025. Scraping data.en.srt
025. Scraping data.mp4
026. Creating data.en.srt
026. Creating data.mp4
027. Passive collection of training data.en.srt
027. Passive collection of training data.mp4
028. Self-generated data.en.srt
028. Self-generated data.mp4
029. Data vendors.en.srt
029. Data vendors.mp4
030. Data ethics.en.srt
030. Data ethics.mp4
06. Sources of Rules
031. The enumeration of explicit rules.en.srt
031. The enumeration of explicit rules.mp4
032. The derivation of rules from data analysis.en.srt
032. The derivation of rules from data analysis.mp4
033. The generation of implicit rules.en.srt
033. The generation of implicit rules.mp4
07. Tools for Data Science
034. Applications for data analysis.en.srt
034. Applications for data analysis.mp4
035. Languages for data science.en.srt
035. Languages for data science.mp4
036. AutoML.en.srt
036. AutoML.mp4
037. Machine learning as a service.en.srt
037. Machine learning as a service.mp4
08. Mathematics for Data Science
038. Sampling and probability.en.srt
038. Sampling and probability.mp4
039. Algebra.en.srt
039. Algebra.mp4
040. Calculus.en.srt
040. Calculus.mp4
041. Optimization and the combinatorial explosion.en.srt
041. Optimization and the combinatorial explosion.mp4
042. Bayes' theorem.en.srt
042. Bayes' theorem.mp4
09. Unsupervised Learning
043. Supervised vs. unsupervised learning.en.srt
043. Supervised vs. unsupervised learning.mp4
044. Descriptive analyses.en.srt
044. Descriptive analyses.mp4
045. Clustering.en.srt
045. Clustering.mp4
046. Dimensionality reduction.en.srt
046. Dimensionality reduction.mp4
047. Anomaly detection.en.srt
047. Anomaly detection.mp4
10. Supervised Learning
048. Supervised learning with predictive models.en.srt
048. Supervised learning with predictive models.mp4
049. Time-series data.en.srt
049. Time-series data.mp4
050. Classifying.en.srt
050. Classifying.mp4
051. Feature selection and creation.en.srt
051. Feature selection and creation.mp4
052. Aggregating models.en.srt
052. Aggregating models.mp4
053. Validating models.en.srt
053. Validating models.mp4
11. 10 Generative Methods in Data Science
054. Generative adversarial networks (GANs).en.srt
054. Generative adversarial networks (GANs).mp4
055. Reinforcement learning.en.srt
055. Reinforcement learning.mp4
12. Acting on Data Science
056. The importance of interpretability.en.srt
056. The importance of interpretability.mp4
057. Interpretable methods.en.srt
057. Interpretable methods.mp4
058. Actionable insights.en.srt
058. Actionable insights.mp4
13. Conclusion
059. Next steps and additional resources.en.srt
059. Next steps and additional resources.mp4
Bonus Resources.txt
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