Mini-Projects in Data Science

References:

• Fast Forwards Labs

Useful Algorithms for Data Science

References:

https://github.com/asjad99/Algorithms-for-data-products CS168, advanced analytics in Spark Data science foundations book by CMU

Data Science Foundations - Selected Topics in Data Science

Welcome to the Fundamental Concepts in Data Science Repo!

This Repo is a collection of Jupyter notebooks aimed at teaching key concepts in data science, ranging from foundational mathematics to practical data analysis techniques. Each notebook is designed to be a standalone learning resource, complete with explanations, examples, and exercises to help you get hands-on with each topic.

Index of Notebooks

Below is an index of the notebooks included in this repository. Links to the individual notebooks will be added soon.

Topic	Description	Link
1. Exploratory Data Analysis (EDA)	EDA Notebook 1: Introduction to EDA and basic data summary techniques EDA Notebook 2: Understanding distributions, central tendency, and variability EDA Notebook 3: Univariate and multivariate relationships in data	[Link Placeholder]
2. Data Munging	Data Cleaning and Preprocessing: Handling missing data, data transformation, and feature engineering Data Wrangling: Merging, reshaping, and dealing with categorical data	[Link Placeholder]
3. Linear Algebra	Vectors and Matrices: Concepts of vectors, operations on matrices, and matrix factorizations Applications in Data Science: How linear algebra is used in machine learning models	[Link Placeholder]
4. Data Visualization Techniques	Basic Plotting: Introduction to Matplotlib and Seaborn for visualizing data Advanced Visualization: Creating interactive visualizations and dashboards	[Link Placeholder]
5. Statistics	Descriptive Statistics: Measures of central tendency and variability Inferential Statistics: Hypothesis testing, confidence intervals, and p-values	[Link Placeholder]
6. Experimental Design and Analysis	Experimental Design: Concepts of controlled experiments, A/B testing, and sample size determination Analysis Techniques: Methods for analyzing experimental results	[Link Placeholder]
7. Dimensionality Reduction	PCA and t-SNE: Introduction to Principal Component Analysis and t-Distributed Stochastic Neighbor Embedding Feature Selection: Techniques for selecting important features in a dataset	[Link Placeholder]
8. Clustering	K-Means and Hierarchical Clustering: Understanding unsupervised learning and cluster formation Clustering Evaluation: Techniques to evaluate clustering effectiveness	[Link Placeholder]
9. Graphs	Introduction to Graphs: Understanding nodes, edges, and types of graphs Network Analysis: Concepts like centrality, shortest path, and community detection	[Link Placeholder]
10. Numerical Optimization	Optimization Basics: Gradient descent, learning rates, and optimization algorithms Applications: Optimization techniques in machine learning models	[Link Placeholder]
11. Storytelling with Data (Our World in Data)	Data Storytelling Techniques: How to effectively communicate insights using real-world datasets Examples from Our World in Data: Exploring global datasets to tell compelling stories	[Link Placeholder]

Contributions are welcome! If you would like to add new notebooks, suggest changes, or fix any issues, please feel free to submit a pull request.

Advanced Algorithms: https://github.com/asjad99/Algorithms-for-data-products Everyday DS Tools: https://github.com/asjad99/Data-Science-Tools Case Stuides/Applications: https://github.com/asjad99/Data-Science-Applications

Python Concepts and Computational Thinking Foundations

https://computationalthinking.mit.edu/Fall24/

Math foundations for data Science

Jeremy Kun etc