> For the complete documentation index, see [llms.txt](https://zavalab.gitbook.io/statschemebook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://zavalab.gitbook.io/statschemebook/chapter-6-statistical-data-analysis-and-learning/01-statistical-data-analysis-and-learning.md).

# Part I: Dimensionality Reduction

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**Slides:** [stats\_ch6\_partI.pdf](https://github.com/vzavala/StatsBook/blob/master/Slides/stats_ch6_partI.pdf) · [4-up handout](https://github.com/vzavala/StatsBook/blob/master/Slides/handouts/stats_ch6_partI_4up.pdf) · **Code:** [Python notebooks](https://github.com/vzavala/StatsBook/tree/master/Python) · [MATLAB scripts](https://github.com/vzavala/StatsBook/tree/master/Matlab) · [MATLAB live scripts](https://github.com/vzavala/StatsBook/tree/master/Matlab-Live)
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* Data science and machine learning (ML) are fields that provide tools for:
  * Data Analysis (e.g., dimension reduction, time-series, clustering, computer vision)
  * Predictive Modeling (e.g., neural nets, kriging, classification)
  * Artificial Intelligence (e.g., data collection, experimentation, learning, control)
* Some tools used are derived from *statistical principles* while others are derived from other mathematical principles (e.g., geometry, optimization, linear algebra).
* Our focus here is discussing general statistical principles behind such tools.
* Data is at the core of modeling and decision-making tasks.
* Data is what allows us to characterize the behavior of systems and appears in different forms such as spatial fields, time series, images, video, and text.
* Ultimately, our goal is to extract knowledge from such data (in the form of a model) to characterize and analyze the dominant trends.
* We will discuss advanced techniques to extract knowledge from data and to use this knowledge to make predictions and decisions.
* We will explore techniques to extract interesting features of the data.
* We will see how to use this info to analyze and compare complex systems, identify abnormal behavior, build predictive models, and make decisions.
* We will discuss predictive modeling techniques that do not require parameters (called non-parametric) to make predictions.
* We will explore parametric modeling techniques that are universal (can use data to predict virtually any type of behavior seen in practice).
* Techniques mimic how brain learns from data collected from our sensory systems.