Welcome to my Data Science journey! This repository tracks my progression through key stages essential for becoming proficient in Data Science: Python, Statistics, Machine Learning, and Deep Learning.
- Matrices & Linear Algebra: Basics of matrices, vectors, operations, and linear algebra concepts.
- Database Basics: Understanding databases, their structures, and operations.
- Relational vs. Non-relational databases: Differentiating between SQL (relational) and NoSQL (non-relational) databases.
- SQL + Joins: Learning SQL commands and various types of joins (inner, outer, cross, theta).
- NoSQL: Exploring non-relational databases.
- Tabular Data: Understanding and working with structured data.
- Data Frames & Series: Handling tabular data structures like those in Pandas.
- Extract, Transform, Load (ETL): Processes for extracting, transforming, and loading data.
- Reporting vs. BI vs. Analytics: Differentiating between reporting, business intelligence, and analytics.
- Data Formats: Understanding data formats like JSON, XML, and regular expressions (RegEx).
- Install packages: Using pip, conda, or similar tools to install Python packages.
- Code style (PEP8): Following Python coding conventions.
- Numpy & Pandas Basics: Basics of using NumPy and Pandas libraries for numerical operations and data manipulation.
- Exploratory Data Analysis (EDA): Techniques for exploring and understanding datasets.
- Data Cleaning: Techniques like handling missing values, normalization, denoising, and feature extraction.
- Dimensionality & Numerosity Reduction: Methods like PCA for reducing data complexity.
- Data Scrubbing: Techniques for cleaning and preparing data.
- Binning Sparse Values: Grouping infrequent values into a separate category.
- Sampling: Methods for extracting representative subsets of data.
- Handling Missing Values: Strategies for dealing with missing data.
- Feature Extraction: Deriving new features from existing ones.
- Probability Theory: Basics of probability, distributions (continuous and discrete), and important laws.
- Hypothesis Testing: Methods for making inferences about data.
- Monte Carlo Method: Simulating random outcomes for complex problems.
- Variance, Standard Deviation, Covariance, Correlation: Measures of variability and relationships between variables.
- Median, Quartile, Percentile, Mode: Summary statistics and data distribution metrics.
- Concepts, Inputs & Attributes: Understanding the components of machine learning models.
- Overfitting / Underfitting: Problems related to model complexity and performance.
- Training, Validation, and Test Data: Splitting data for model evaluation.
- Precision vs. Recall: Measures for classification model evaluation.
- Bias & Variance: Balancing model simplicity and accuracy.
- Supervised Learning Methods: Various techniques for teaching machines using labeled data.
- Unsupervised Learning: Techniques for finding patterns without labeled data.
- Ensemble Learning: Methods for combining multiple models.
- Neural Networks: Understanding neural network architectures like feedforward, convolutional, recurrent, etc.
- Deep Learning Libraries: Tools like TensorFlow and PyTorch for building deep learning models.
- Loss Functions, Activation Functions: Components used in neural networks for optimization.
- Optimizers: Algorithms for adjusting neural network parameters during training.
- Training Techniques: Strategies for effectively training deep learning models.
- Model Optimization (Advanced): Advanced techniques for optimizing deep learning models.
I welcome any contributions, suggestions, or insights that could enhance this documentation or provide further learning resources!
Want to embark on this exciting Data Science journey together? Feel free to join me! Let's learn and grow in the world of data together. Drop me a message or collaborate on this repository. Looking forward to having you onboard! 🌟