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# UArizona Data Lab Workshops - Fall 2023
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:construction: :construction: :construction: :construction: :construction:

***

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>
> ## I. Workshop _"Unlocking the Power of Data: A Journey Through Machine Learning & Deep Learning"_
> :bookmark_tabs: [Workshop Wiki](https://github.com/ua-datalab/Workshops/wiki)
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#### 1. **Introduction to Python for Data Science**

**Content**: Basics of Python, variable types, flow control, and functions.

[Notes for the Workshop](https://github.com/ua-datalab/Workshops/wiki/Introduction-to-Python-for-Data-Science) / [Link to Zoom recording](https://arizona.zoom.us/rec/share/4oHvg2ZVv2oE4axy8AfKIrq0hsGw6I4KyClOcOWXZsT_Id8w8S0Ko4Ufw4NRTA3c.wjBE_f6PBcS52aoA) - (10-03-2023)

**More references:**
* [Introduction to Python for Data Science - RezBaz AZ 2022.](https://github.com/clizarraga-UAD7/Workshops/wiki/Introduction-to-Python-for-Data-Science)
* [Jupyter Notebooks](https://github.com/clizarraga-UAD7/Workshops/wiki/Jupyter-Notebooks)

#### 2. **Data Wrangling 101: Pandas in Action**

**Content**: Data manipulation and cleaning using Pandas.

[Notes for the Workshop](https://github.com/ua-datalab/Workshops/wiki/Pandas-for-Data-Analysis) / [Link to Zoom recording](https://arizona.zoom.us/rec/share/qJroJ0ZpGJrx7COBBE4Vkw5EuVYs0l7q0X3i1ZFaG3WY67KBPMsADHb1ulZMcew-.zeCTYDwRtFSSxl7l) (10-10-2023)

**More references**:
* [Pandas for Data Analysis](https://github.com/clizarraga-UAD7/Workshops/wiki/Pandas-for-Data-Analysis)
* [Exploratory Data Analysis with Python](https://github.com/clizarraga-UAD7/Workshops/wiki/Exploratory-Data-Analysis)

#### 3. **Statistical Inference: The Backbone of Data Science**

**Content**: Descriptive and inferential statistics.

[Notes for the Workshop](https://github.com/ua-datalab/Workshops/wiki/Statistical-Inference) / [Link to Zoom recording](https://arizona.zoom.us/rec/share/-3hPUfIlDHmm-TvZk2oxk-EYrafTu5zA4yqL5lP0nl8vcp1NddussAQXOMKTyjlI.943CeiYkK2GM7Y0n?startTime=1697573332000) (10-10-2023)

**References**:

#### 4. **Machine Learning Basics: Scikit-learn Unveiled**

**Content**: Introduction to machine learning libraries in Python.

[Notes](https://github.com/ua-datalab/Workshops/wiki/Machine-Learning-Basics:-scikit%E2%80%90learn-Unveiled) | [Link to Zoom recording](https://arizona.zoom.us/rec/share/0tgbnzZ0KTcVAAlXVfoC8_6R_s46NFKZhfj-B0J9IZ9CzYuIldUi-2Jy-tyCeokz.7o2COrWoKayYv5Nk?startTime=1698178193000)

**References**:
* [Introduction to Machine Learning](https://github.com/clizarraga-UAD7/Workshops/wiki/Introduction-to-Machine-Learning)
* [Overview of Machine Learning Algorithms](https://github.com/clizarraga-UAD7/Workshops/wiki/An-Overview-of-Machine-Learning-Algorithms)

#### 5. **Natural Language Processing: Text Mining and Sentiment Analysis**

**Content**: Text data processing and sentiment analysis.

[Notes](https://github.com/ua-datalab/Workshops/wiki/Natural-Language-Processing:-Text-Mining-and-Sentiment-Analysis) | [Link to Zoom recording](https://arizona.zoom.us/rec/share/zPdhHe4f2QiMp2gUQ_SG-4keC8bEolyV3H9G8DqKR8c2kirfurN3mO78SIk2VFg.uCpUPVEGNDU3U1rF?startTime=1698784397000).

#### 6. **Time Series Analysis: Forecasting the Future**

[Notes](https://github.com/ua-datalab/Workshops/wiki/Time-Series-Analysis) | [Link to Zoom recording](https://arizona.zoom.us/rec/share/JQD1XHWBSGT2T9pZ5P7J0nx6aTmeYtmYBL1bx4HzdF-JU8lGq3rrMzQBHqP6YP8t.hZiVPmXo0ciYztbs?startTime=1699386144000) (11-0-2023)
**Content**: Time series data analysis and forecasting techniques.
**References**:

#### 7. **Deep Dive into Deep Learning: Neural Networks Demystified**

**Content**: Introduction to neural networks and deep learning.
**References**:
* UT Austin's AI and Machine Learning Program[**[4](https://onlineexeced.mccombs.utexas.edu/online-ai-machine-learning-course)**].
* [Overview of Deep Learning Algorithms](https://github.com/clizarraga-UAD7/Workshops/wiki/Overview-of-Deep-Learning-Algorithms)

#### 8. **Computer Vision: Image and Video Analysis**

**Content**: Basics of image and video data processing.

:spiral_note_pad: [Notes](https://github.com/ua-datalab/Workshops/wiki/Computer-Vision:-Image-Analysis)

:video_camera: [Zoom recording link](https://arizona.zoom.us/rec/share/VkHK0o4bKb7oVFLYlDlDUbrz1USEOfvAZBt3tCv27AEIH7GhGQ4vq7GqOTqlsRA7._ypokB-dKD4SEOGq?startTime=1700595291000)
**References**:
*

***
::: info
>#### Tuesday's Workshop: "Unlocking the Power of Data: A Journey Through Machine Learning & Deep Learning"
>
> **Room:** **_Main Library: Catalyst Data Studio_**
>

| Date | Time | Title |
| :--: | :--: | :-- |
| 10/03 | 1pm | Introduction to Python for Data Science (Megh) |
| 10/10 | 1pm | Data Wrangling 101: Pandas in Action (Brenda) |
| 10/17 | 1pm | Statistical Inference: The Backbone of Data Science (Carlos) |
| 10/24 | 1pm | Machine Learning Basics: Scikit-learn Unveiled (Carlos) |
| 10/31 | **2pm** | Natural Language Processing: Text Mining and Sentiment Analysis (Megh) |
| 11/07 | 1pm | Time Series Analysis: Forecasting the Future (Shravan) |
| 11/14 | 1pm | Deep Dive into Deep Learning: Neural Networks Demystified (Mithun) |
| 11/21 | 1pm | Computer Vision: Image Analysis (Michele, Brenda)|
| 11/28 | 1pm | AI Tools Landscape (Carlos) |

:::

***

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>
> ## II. Workshop _"Introduction to Deep Learning"_
> :bookmark_tabs: [Workshop Wiki](https://github.com/ua-datalab/DLWorkshops/wiki)

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#### 1. Deep Learning Refresher (Mithun)

[Notes for the Workshop](https://github.com/ua-datalab/DLWorkshops/wiki/Lecture1:-Introduction-To-HuggingFace) / [Zoom recording](https://arizona.zoom.us/rec/share/RhxfiuurCmetpBgPcxdPUvj73G5PuVu8dY5Q_vSWPaYyUB2_iAINWSuWd07toWAp.4p1kq-cFAdh46hTp?startTime=1697141373000) | [Part 2](https://arizona.zoom.us/rec/share/GTRm53SPXDGBbj8ooUcAzGvvtt7ojOoQch3asuK6VNBsE3Dq0lyz0qRp4nIYuAZZ.34dqJmomIn6tUGb7?startTime=1697141480000) (10-12-2023)

- **Reference**:
- What are neural networks?
- Encoders
- Decoders
- Sequence to Sequence

#### 2. **Introduction to HuggingFace**

:notebook: [Materials](https://github.com/ua-datalab/DLWorkshops/wiki/Lecture2:-Introduction-To-Transformers) |
:movie_camera: [Zoom recording](https://arizona.zoom.us/rec/share/UWCpupRr_SpWc9If4HIQxwKgJVd3OjMQMijiVM3CMelpf2qjHrZIROQvil33DsDI.9waXaWGlGkIb8x_s?startTime=1697746026000)

- **Reference**: [HuggingFace](https://huggingface.co/) .

#### 3. **Fine-tuning a pre-trained model**

:notebook: [Materials](https://github.com/ua-datalab/DLWorkshops/wiki/Lecture3:-Fine-Tuning-A-Pre-trained-Model) |
:movie_camera: [Zoom recording](https://arizona.zoom.us/rec/share/_psFkKSU27oDU4-BvkfNq2z6oOfyYNgKdX5dp3zooQ8ZLExV5cGDoiLeHTgDOgsP.zmWxx8aDkJTl5I8r?startTime=1698349763000)

- **Reference**: TBD

#### 4. How GPT and other Large Language Models Work

:notebook: [Materials](https://github.com/ua-datalab/DLWorkshops/wiki/Lecture4:-Introduction-to-ChatGPT)
:movie_camera: [Zoom recordings link](https://arizona.zoom.us/rec/share/l3OZeaIno_G2esLO-mfJkghiCBi1t449dtVWy02ZbvmZSZT6UH8moInfjXXqYeYg.HCBVXMMPNO_aGHPt?startTime=1698954890000)
- **Reference**: TBD

#### 5. **Training a model from scratch**

[Zoom recording link](https://arizona.zoom.us/rec/share/CHa_GacX12C-iMSSHRxEXA6HFsBZl33K-f9ttVzmh9tdkNwp4oi7CcgxtwVNMhIv.DhG8MRUD7IBw8IA7?startTime=1699558919000)

- **Reference**: TBD

#### 6. **Databricks and SageMaker**

- **Reference**: TBD

#### 7. **How GPT and other Large Language Models Work**

- **Reference**: TBD

Open topics/possible additions
- Multimodality
- Generative models
- Bard, LLAMA, AI Image generators, DALL-E.
***

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> #### Thursday's Workshop: "Intro to Deep Learning: An Exploration"
>
>**Room:** **_Weaver Science and Engineering Library 212_**

| Date | Time | Title |
| :--: | :--: | :-- |
| 10/12 | 1pm | Intro to hugging face |
| 10/19| 1pm | Using Transformers
| 10/26 | 1pm | Fine Tuning A Pre-trained model|
| 11/02 | 1pm |How GPT and other Large Language Models Work X
| 11/09 | 1pm | Fine tuning GPT
| 11/16 | 1pm |Fast.AI, spacy.io , Databricks, SageMaker, Cyverse|
| 11/23 | - | Thanksgiving Day |
| 11/30 | 1pm |Reinforcement learning, Multimodal- vision, video, audio, Quantum AI|

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***

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>
>## III. Workshop _"Mastering High-Performance Computing and Machine Learning"_
>
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#### 1. Navigating Campus HPC for Machine Learning Projects

- **Main Content**: Introduction to campus High-Performance Computing (HPC) resources, setting up accounts, and running basic machine learning algorithms.
- **Factual Example**: Middle school students collaborating with practicing scientists over the Internet can benefit from HPC resources for data-intensive tasks[[3](https://www2.ed.gov/rschstat/eval/tech/evidence-based-practices/finalreport.pdf)].

#### 2. Optimizing ML and DL Workloads on Campus HPC

- **Main Content**: Best practices for distributing machine learning and deep learning tasks across HPC clusters.
- **Factual Example**: Workshops can introduce practical skills and techniques for optimizing workloads[[2](https://ctb.ku.edu/en/table-of-contents/structure/training-and-technical-assistance/workshops/main)].

#### 3. GPU Utilization for Accelerated Computing

- **Main Content**: Overview of Graphics Processing Units (GPUs), and how to effectively use them for computational tasks.
- **Factual Example**: Determining the main idea or topic is crucial for effective GPU utilization[[6](https://www.oecd.org/education/school/programmeforinternationalstudentassessmentpisa/33693997.pdf)].

#### 4. Crafting Your Ideal Programming Environment

- **Main Content**: Setting up IDEs, version control, and containerization for a seamless programming experience.
- **Factual Example**: Academic Essay Writing for Postgraduates discusses planning, drafting, and revising assignments, similar to setting up a programming environment[[5](https://www.ed.ac.uk/files/atoms/files/aewpg_ismaterials.pdf)].

#### 5. Leveraging Hugging Face for Deep Learning

- **Main Content**: Introduction to the Hugging Face library, and how to implement transformers for NLP tasks.
- **Factual Example**: Trained and skilled volunteers can be valuable assets in implementing deep learning projects[[1](https://www.samhsa.gov/sites/default/files/volunteer_handbook.pdf)].

#### 6. Mastering PyTorch and PyTorch Lightning

- **Main Content**: Introduction to PyTorch and PyTorch Lightning frameworks, and how to build and deploy models.
- **Factual Example**: Research-based frameworks can help leaders understand six types of family and community involvement, similar to understanding machine learning frameworks[[4](https://www.govinfo.gov/content/pkg/ERIC-ED467082/pdf/ERIC-ED467082.pdf)].

#### 7. Data Pipelines

#### 8. ...

***

::: info
>
>#### Wednesday's Workshop: "Mastering High-Performance Computing and Machine Learning "
>
>**Room:** **TBD**

| Date | Time | Title |
| :--: | :--: | :-- |
| | | Navigating Campus HPC for Machine Learning Projects |
| | | Optimizing ML and DL Workloads on Campus HPC |
| | | GPU Utilization for Accelerated Computing |
| | | Crafting Your Ideal Programming Environment |
| | | Leveraging Hugging Face for Deep Learning |
| | | Mastering PyTorch and PyTorch Lightning |
| | | TBD |
| | | TBD |

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***

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>
> **[IV UArizona DataLab](https://www.datascience.arizona.edu/research/uarizona-data-lab)** *A SQL Masterclass: Research & Interview Focused Workshop*
>
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Become an SQL knowledgeable with our two-part sessions.

Part 1, covers basic topics such as tables, joins, filtering, and views.

Part 2, explores advanced techniques like subqueries, CTEs, and window functions.

**Learning Objectives**:

Master the fundamentals of SQL and advanced techniques.

Learn how to integrate data using joins and views.

Handle unstructured data effortlessly.

Impress in interviews with your advanced SQL skills.

**Instructors:** Ankit Pal & Carlos Lizárraga

**When:** Monday Oct. 16th. 1 PM

**Where:** Virtual

**[Please register](https://uarizona.co1.qualtrics.com/jfe/form/SV_7Ut5xZmKF67a3Qi)** now for an amazing SQL learning experience,

and attend the [virtual Zoom session](https://arizona.zoom.us/j/84309105282).

**SQL workshop**

[Zoom recording](https://arizona.zoom.us/rec/share/fZuRxoPuXcQRq5D078bveqhj6pm9XZRnUdxWzvd1ggR8aM9EOmiebt9ydpX7e63y.czKbtAqIHPZZEnqz?startTime=1697486674000) | [Jupyter Notebook](https://github.com/ua-datalab/Workshops/blob/main/A_SQL_Masterclass/A_SQL_Masterclass_Part_1_.ipynb) (10-16-2023)

***

Updated: 09/26/2023

Carlos Lizárraga

UArizona Data Lab @ Data Science Institute