https://github.com/jorgeandrespadilla/data-science-course
Data Science Course by Jennifer Widom (UDLA, 2022)
https://github.com/jorgeandrespadilla/data-science-course
data-mining data-science machine-learning network-analysis
Last synced: 9 months ago
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Data Science Course by Jennifer Widom (UDLA, 2022)
- Host: GitHub
- URL: https://github.com/jorgeandrespadilla/data-science-course
- Owner: jorgeandrespadilla
- Created: 2022-07-19T14:22:40.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-07-25T08:40:45.000Z (almost 4 years ago)
- Last Synced: 2025-01-12T14:47:44.858Z (over 1 year ago)
- Topics: data-mining, data-science, machine-learning, network-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 46.6 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Science Course
## Introduction
Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing data. Professor Widom's short-courses provide an introduction to data science, including some history, case studies, and common pitfalls, along with broad, interactive, hands-on coverage of tools & techniques for data collection, analysis, and visualization.
This short-courses where dictated by Jennifer Widom, Dean of the School of Engineering at Stanford University, from July 19th to July 21st, 2022, at UDLA's campus.
This repository is a collection of all the course materials, including the course notes, presentation slides, and hands-on activities.
See more at [Data Science Fact Sheet](materials/documents/Data%20Science%20Fact%20Sheet.pdf).
## Itinerary
### Day 1 (19th July)
**Introduction to Data Science**:
*Content:*
- The “Instructional Odyssey”
- Motivation and terminology
- Applications and services
- Tools and Techniques overview:
- Basic Data Manipulation and Analysis
- Data Mining
- Machine Learning
- Data Visualization
- Data Collection and Preparation
- Languages, Systems, Platforms
- Pitfalls:
- Correlation and Causation
- Underfitting and Overfitting
- Privacy considerations
See more at [Overview of Data Science](materials/slides/OverviewSlides.pdf) or [Overview of Data Science (COVID Edition)](materials/slides/OverviewSlidesCE.pdf).
**Data Analysis and Visualization:**
*Before starting:*
- [Getting Started with Google Sheets](materials/documents/getting-started/Getting%20Started%20with%20Google%20Sheets.pdf)
- [Getting Started with Tableau Public](materials/documents/getting-started/Getting%20Started%20with%20Tableau%20Public.pdf)
*Content:*
- Data analysis using Spreadsheets (see more [here](materials/slides/day1/SpreadsheetsSlides.pdf))
- Data visualization using Spreadsheets (see more [here](materials/slides/day1/VisualizationSlides.pdf.pdf))
- Data visualization using Tableau (see more [here](materials/slides/day1/TableauSlides.pdf))
*Hands-on activities:*
- Data analysis using Spreadsheets (*Spreadsheets Module*)
- Data visualization using Spreadsheets (*Visualization Module*)
### Day 2 (20th July)
**Relational Databases and SQL**:
*Before starting:*
- [Getting Started with Google Colab](materials/documents/getting-started/Getting%20Started%20with%20Google%20Colab.pdf)
*Content:*
- Introduction and basic concepts
- Basic SQL:
- Basic SELECT statement
- Ordering
- Joins
- Basic aggregation
- Limit clause
See more at [Relational Databases and SQL](materials/slides/day2/RelationalDBandSQLSlides.pdf).
*Hands-on activities:*
- Basic SQL (*SQL Modules (only Basic SQL)*)
**Python for Data Analysis and Visualization**:
*Before starting:*
- [Getting Started with Google Colab](materials/documents/getting-started/Getting%20Started%20with%20Google%20Colab.pdf)
*Content:*
- Python basics
- Data manipulation
- Pandas and data analysis
- Plotting and data visualization
See more at [Python for Data Analysis and Visualization](materials/slides/day2/PythonSlides.pdf).
*Hands-on activities:*
- Python for Data Analysis and Visualization (*Python Modules*)
### Day 3 (21th July)
**Python for Machine Learning**:
*Content:*
- Regression (see more [here](materials/slides/day3/RegressionSlides.pdf)):
- Simple linear regression
- Regression and Correlation (measuring correlation)
- Polynomial regression
- Anscombe’s quartet
- Classification (see more [here](materials/slides/day3/ClassificationSlides.pdf)):
- Terminology
- K-Nearest Neighbors (KNN)
- Decision Trees
- Naïve Bayes
- Deep Neural Networks
- Training and testing
- Clustering (see more [here](materials/slides/day3/ClusteringSlides.pdf)):
- K-Means Clustering
- Applications
See more at [Python for Machine Learning](materials/slides/day3/PythonMLslides.pdf).
*Hands-on activities:*
- Python for Machine Learning (*Machine Learning Modules*)
**Data Mining**:
*Content:*
- Basic concepts
- Data mining algorithms:
- Frequent item-sets
- Association rules
See more at [Data Mining Algorithms](materials/slides/day3/MiningSlides.pdf).
*Hands-on activities:*
- Data Mining using Python (*Data Mining Modules (only Python)*)
**Network Analysis**:
*Content:*
- Basic concepts
- Examples
- Network analysis:
- Undirected graphs
- Directed graphs
- Labeled graphs
- Other analyses
See more at [Network Analysis](materials/slides/day3/NetworksSlides.pdf).
*Hands-on activities:*
- Network Analysis (*Network Analysis (module)*)
## Materials
http://www.professorwidom.org/