Data Analytics with Python

By: Abderrahim Benmoussa, Ph.D.

This repository showcases a comprehensive set of skills in data analytics and data science, especially in the domain of biology and health through a unique project, ranging from foundational data visualization to intricate machine learning techniques.

Table of Contents

• Foundations of Data Analytics
• Intermediate Data Analysis Techniques
• Advanced Data Analysis Techniques
• Special Topics in Biology and Health
• Advanced Machine Learning Techniques
• Final Projects and Capstone

Foundations of Data Analytics

Data Importing and Cleaning

• Objective: Understand the basics of data importing, cleaning, and preprocessing.
• Tasks: Import datasets, conduct descriptive statistics, handle missing values, and normalize data.
• Dataset: Breast Cancer Wisconsin (Diagnostic) Data Set

Basic Statistical Modeling

• Objective: Apply basic statistical models to understand relationships in the data.
• Tasks: Data exploration, statistical test selection using LLM and group comparison through hypothesis testing
• Dataset: Breast Cancer Wisconsin (Diagnostic) Data Set

Data Visualization

• Objective: Visualize data distributions and relationships.
• Tasks: Use Matplotlib, Seaborn, and Plotly for histograms, scatter plots, and heatmaps.
• Dataset: Human Resources Analytics

Introduction to Genetics Data

• Objective: Understand the basics of genetics data and its structure.
• Tasks: Introduction to genetic markers, SNPs, and genotypes.
• Dataset: Genetic Variation Dataset

Intermediate Data Analysis Techniques

Time Series Analysis

• Objective: Analyze time-dependent data.
• Tasks: Decomposition, ARIMA modeling, and forecasting.
• Dataset: Malaria in Colombia

Clustering and Segmentation

• Objective: Group data based on similarities.
• Tasks: K-means clustering, hierarchical clustering, and DBSCAN.
• Dataset: Metabolomics Data

Classification Techniques

• Objective: Predict categorical outcomes.
• Tasks: Logistic regression, decision trees, and support vector machines.
• Dataset: Pima Indians Diabetes Database

Dimensionality Reduction

• Objective: Reduce the dimensionality of data.
• Tasks: PCA and t-SNE.
• Dataset: Genetic Variation Dataset.

Advanced Data Analysis Techniques

Neural Networks and Deep Learning

• Objective: Understand the basics of neural networks.
• Tasks: Introduction to neural networks using TensorFlow/Keras.
• Dataset: Skin Cancer MNIST

Convolutional Neural Networks (CNNs)

• Objective: Image data analysis.
• Tasks: Introduction and implementation of CNNs.
• Dataset: Skin Cancer MNIST.

Natural Language Processing (NLP)

• Objective: Analyze text data.
• Tasks: Text preprocessing, sentiment analysis, and topic modeling.
• Dataset: PubMed 200k RCT

Reinforcement Learning

• Objective: Understand the basics of reinforcement learning.
• Tasks: Introduction to Q-learning.
• Dataset: Custom environment using OpenAI Gym.

Special Topics in Biology and Health

Genomics and Bioinformatics

• Objective: Analyze genomic sequences.
• Tasks: Sequence alignment, gene prediction, and phylogenetics.
• Dataset: NCBI GenBank

Proteomics

• Objective: Understand protein structures and functions.
• Tasks: Protein sequence and structure analysis.
• Dataset: Protein Data Bank

Metabolomics

• Objective: Analyze metabolic pathways.
• Tasks: Metabolic pathway analysis and biomarker discovery.
• Dataset: MetaboLights

Systems Biology

• Objective: Understand interactions within biological systems.
• Tasks: Network and pathway enrichment analysis.
• Dataset: BioGRID

Advanced Machine Learning Techniques

Ensemble Methods

• Objective: Improve model performance using ensemble techniques.
• Tasks: Bagging, boosting, and stacking.
• Dataset: Heart Disease UCI

Transfer Learning

• Objective: Use pre-trained models for new tasks.
• Tasks: Introduction and fine-tuning of pre-trained models.
• Dataset: Malaria Cell Images Dataset

Unsupervised Learning

• Objective: Discover patterns without labeled data.
• Tasks: Autoencoders and GANs.
• Dataset: MNIST

Model Interpretability

• Objective: Understand model decisions.
• Tasks: Feature importance, SHAP values, and LIME.
• Dataset: Heart Disease UCI.

Final Projects and Capstone

Multi-omics Integration

• Objective: Integrate data from multiple omics levels.
• Tasks: Data integration and joint analysis.
• Dataset: TCGA Pan-Cancer (PANCAN)

Personalized Medicine

• Objective: Predict treatment outcomes based on individual data.
• Tasks: Predict drug responses and personalized treatment recommendations.
• Dataset: GDSC

Disease Prediction and Prevention

• Objective: Predict disease risk and outcomes.
• Tasks: Risk factor analysis and predictive modeling.
• Dataset: Framingham Heart Study dataset

Capstone Project

• Objective: Demonstrate all learned skills in a comprehensive project.
• Tasks: End-to-end data analysis.
• Dataset: Choose based on personal interest or combine multiple datasets.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License