Data Science, Big Data & Data Mining: Complete Learning Roadmap

Phase 1: Mathematics & Statistics Foundations (6-8 weeks)

Linear Algebra

• Vectors and matrices
• Matrix operations: addition, multiplication, transpose
• Determinants and inverses
• Eigenvalues and eigenvectors
• Singular Value Decomposition (SVD)
• Principal Component Analysis (PCA) mathematical foundation
• Vector spaces and linear transformations
• Matrix factorization techniques

Calculus

• Derivatives and partial derivatives
• Gradient and gradient descent
• Chain rule for backpropagation
• Multivariate calculus
• Optimization techniques
• Taylor series and approximations
• Jacobian and Hessian matrices

Probability Theory

• Probability axioms and rules
• Conditional probability and Bayes' theorem
• Random variables: discrete and continuous
• Probability distributions: uniform, normal, binomial, Poisson, exponential
• Expected value and variance
• Covariance and correlation
• Law of large numbers
• Central limit theorem
• Joint and marginal distributions

Statistics

• Descriptive statistics: mean, median, mode, standard deviation
• Inferential statistics
• Hypothesis testing: t-tests, chi-square tests, ANOVA
• Confidence intervals
• p-values and significance levels
• Type I and Type II errors
• Statistical power
• Correlation vs causation
• A/B testing fundamentals
• Sampling techniques and bias

Phase 2: Programming Fundamentals (6-8 weeks)

Python Programming

• Python basics: data types, control structures, functions
• Object-oriented programming
• Error handling and exceptions
• File I/O operations
• List comprehensions and generators
• Lambda functions and functional programming
• Decorators and context managers
• Virtual environments and package management

Essential Python Libraries

• NumPy: array operations, broadcasting, linear algebra
• Pandas: DataFrames, Series, data manipulation, groupby, merge
• Matplotlib: basic plotting, customization
• Seaborn: statistical visualizations
• SciPy: scientific computing, statistical functions
• Jupyter Notebooks: interactive development

Data Structures & Algorithms

• Arrays, linked lists, stacks, queues
• Trees: binary trees, BST, heaps
• Hash tables and dictionaries
• Graphs and graph algorithms
• Sorting algorithms: quicksort, mergesort
• Searching algorithms
• Time and space complexity (Big O notation)
• Dynamic programming basics

SQL and Databases

• Relational database concepts
• SQL queries: SELECT, WHERE, JOIN, GROUP BY, HAVING
• Aggregate functions: COUNT, SUM, AVG, MAX, MIN
• Subqueries and CTEs
• Window functions
• Database normalization
• Indexes and query optimization
• NoSQL basics: MongoDB, document stores

Phase 3: Data Collection & Preprocessing (5-6 weeks)

Data Collection

• Web scraping: BeautifulSoup, Scrapy, Selenium
• APIs: REST, authentication, rate limiting
• Data extraction from various formats: CSV, JSON, XML, Excel
• Database connections and querying
• Real-time data streaming basics
• Ethical considerations and legal compliance

Data Cleaning

• Handling missing data: imputation techniques, deletion strategies
• Outlier detection and treatment
• Data type conversion
• String manipulation and regex
• Duplicate removal
• Inconsistency resolution
• Data validation

Data Transformation

• Feature scaling: normalization, standardization, min-max scaling
• Feature encoding: one-hot encoding, label encoding, target encoding
• Feature engineering: creating new features, polynomial features
• Binning and discretization
• Log transformations
• Date-time feature extraction
• Text preprocessing: tokenization, stemming, lemmatization

Exploratory Data Analysis (EDA)

• Univariate analysis
• Bivariate and multivariate analysis
• Distribution analysis
• Correlation analysis
• Visualization techniques
• Statistical summaries
• Pattern and trend identification
• Anomaly detection in EDA

Phase 4: Machine Learning Fundamentals (8-10 weeks)

Supervised Learning - Regression

• Linear regression: simple and multiple
• Polynomial regression
• Ridge regression (L2 regularization)
• Lasso regression (L1 regularization)
• Elastic Net
• Support Vector Regression (SVR)
• Decision tree regression
• Random forest regression
• Gradient boosting regression
• Evaluation metrics: MSE, RMSE, MAE, R², adjusted R²

Supervised Learning - Classification

• Logistic regression
• K-Nearest Neighbors (KNN)
• Naive Bayes: Gaussian, Multinomial, Bernoulli
• Decision trees: CART algorithm
• Random forests
• Support Vector Machines (SVM): linear and kernel
• Gradient boosting: XGBoost, LightGBM, CatBoost
• Evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC
• Confusion matrix analysis
• Multi-class classification strategies

Unsupervised Learning

• K-Means clustering
• Hierarchical clustering: agglomerative and divisive
• DBSCAN (Density-Based Spatial Clustering)
• Gaussian Mixture Models (GMM)
• Principal Component Analysis (PCA)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• UMAP (Uniform Manifold Approximation and Projection)
• Anomaly detection algorithms
• Association rule mining: Apriori, FP-Growth
• Evaluation: silhouette score, elbow method, Davies-Bouldin index

Model Evaluation & Validation

• Train-test split
• Cross-validation: k-fold, stratified k-fold, leave-one-out
• Bias-variance tradeoff
• Overfitting and underfitting
• Learning curves
• Validation curves
• Hyperparameter tuning: grid search, random search
• Model selection criteria

Phase 5: Advanced Machine Learning (8-10 weeks)

Ensemble Methods

• Bagging and bootstrapping
• Random forests in depth
• AdaBoost
• Gradient Boosting Machines (GBM)
• XGBoost: advanced parameters and tuning
• LightGBM: optimization techniques
• CatBoost: handling categorical features
• Stacking and blending
• Voting classifiers

Feature Engineering & Selection

• Domain-specific feature creation
• Interaction features
• Feature importance analysis
• Recursive Feature Elimination (RFE)
• L1-based feature selection
• Correlation-based selection
• Mutual information
• Sequential feature selection
• Dimensionality reduction techniques

Time Series Analysis

• Time series components: trend, seasonality, cyclical, irregular
• Stationarity and differencing
• Autocorrelation (ACF) and Partial Autocorrelation (PACF)
• Moving averages: simple, weighted, exponential
• ARIMA models
• SARIMA (Seasonal ARIMA)
• Prophet for forecasting
• LSTM for time series
• Time series cross-validation

Recommender Systems

• Collaborative filtering: user-based, item-based
• Matrix factorization
• Content-based filtering
• Hybrid approaches
• Singular Value Decomposition (SVD)
• Alternating Least Squares (ALS)
• Neural collaborative filtering
• Evaluation metrics: precision@k, recall@k, NDCG

Phase 6: Deep Learning (10-12 weeks)

Neural Networks Fundamentals

• Perceptron and multilayer perceptron
• Activation functions: sigmoid, tanh, ReLU, Leaky ReLU, ELU, Swish
• Forward propagation
• Backpropagation algorithm
• Gradient descent variants: SGD, momentum, AdaGrad, RMSprop, Adam
• Loss functions: MSE, cross-entropy, hinge loss
• Batch normalization
• Dropout and regularization
• Weight initialization techniques

Convolutional Neural Networks (CNN)

• Convolution operation and filters
• Pooling layers: max pooling, average pooling
• CNN architectures: LeNet, AlexNet, VGG, ResNet, Inception, EfficientNet
• Transfer learning and fine-tuning
• Image classification
• Object detection: YOLO, R-CNN, Fast R-CNN, Faster R-CNN
• Image segmentation: U-Net, Mask R-CNN
• Data augmentation techniques

Recurrent Neural Networks (RNN)

• Vanilla RNN architecture
• Long Short-Term Memory (LSTM)
• Gated Recurrent Units (GRU)
• Bidirectional RNNs
• Sequence-to-sequence models
• Attention mechanisms
• Applications: text generation, sentiment analysis, machine translation

Advanced Deep Learning

• Transformer architecture
• Self-attention and multi-head attention
• BERT, GPT, T5 architectures
• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAE)
• Autoencoders for dimensionality reduction
• Reinforcement learning basics
• Deep reinforcement learning

Deep Learning Frameworks

• TensorFlow and Keras
• PyTorch
• Model building and training
• Custom layers and loss functions
• Model checkpointing and callbacks
• TensorBoard for visualization
• Model deployment basics

Phase 7: Natural Language Processing (8-10 weeks)

Text Preprocessing

• Tokenization: word, sentence, subword
• Stopword removal
• Stemming and lemmatization
• Part-of-speech tagging
• Named Entity Recognition (NER)
• Dependency parsing
• Text normalization

Text Representation

• Bag of Words (BoW)
• TF-IDF (Term Frequency-Inverse Document Frequency)
• Word embeddings: Word2Vec, GloVe, FastText
• Document embeddings: Doc2Vec
• Contextualized embeddings: ELMo, BERT embeddings
• Sentence transformers

NLP Tasks & Applications

• Text classification
• Sentiment analysis
• Topic modeling: LDA, NMF
• Text summarization: extractive and abstractive
• Machine translation
• Question answering systems
• Chatbots and conversational AI
• Information extraction

Advanced NLP

• Transformer models in depth
• BERT and variants: RoBERTa, ALBERT, DistilBERT
• GPT models: GPT-2, GPT-3, GPT-4
• Fine-tuning pre-trained models
• Prompt engineering
• Few-shot and zero-shot learning
• Multilingual NLP

Phase 8: Big Data Technologies (10-12 weeks)

Big Data Fundamentals

• Big Data characteristics: Volume, Velocity, Variety, Veracity, Value
• Distributed computing concepts
• CAP theorem
• Data lakes vs data warehouses
• Lambda and Kappa architectures
• Data governance and quality

Hadoop Ecosystem

• HDFS (Hadoop Distributed File System)
• MapReduce programming model
• YARN (Yet Another Resource Negotiator)
• Hive: SQL on Hadoop
• Pig: data flow scripting
• HBase: NoSQL database
• Sqoop: data ingestion
• Flume: log aggregation
• Oozie: workflow scheduling

Apache Spark

• Spark architecture: driver, executors, cluster manager
• RDDs (Resilient Distributed Datasets)
• DataFrames and Datasets
• Spark SQL
• Spark Streaming
• Structured Streaming
• MLlib: machine learning library
• GraphX: graph processing
• PySpark programming
• Performance optimization and tuning

NoSQL Databases

• Document stores: MongoDB, Couchbase
• Key-value stores: Redis, DynamoDB
• Column-family stores: Cassandra, HBase
• Graph databases: Neo4j, Amazon Neptune
• Time-series databases: InfluxDB, TimescaleDB
• Choosing the right database

Stream Processing

• Apache Kafka: producers, consumers, topics, partitions
• Kafka Streams
• Apache Flink
• Apache Storm
• Real-time analytics
• Event-driven architectures
• Stream processing patterns

Cloud Platforms

• AWS: S3, EC2, EMR, Redshift, Glue, Athena, SageMaker
• Google Cloud: BigQuery, Dataflow, Dataproc, AI Platform
• Azure: Data Lake, HDInsight, Databricks, Synapse Analytics
• Data pipeline orchestration: Apache Airflow
• Infrastructure as Code: Terraform

Phase 9: Data Mining Techniques (6-8 weeks)

Pattern Recognition

• Frequent pattern mining
• Sequential pattern mining
• Association rules: support, confidence, lift
• Apriori algorithm
• FP-Growth algorithm
• Market basket analysis

Classification Techniques

• Decision tree algorithms: ID3, C4.5, CART
• Rule-based classifiers
• Bayesian classification
• Lazy learners: KNN, case-based reasoning
• Ensemble classification methods

Clustering Algorithms

• Partitioning methods: K-Means, K-Medoids, CLARA
• Hierarchical methods: AGNES, DIANA
• Density-based methods: DBSCAN, OPTICS, DENCLUE
• Grid-based methods: STING, CLIQUE
• Model-based clustering: EM algorithm
• Cluster validation techniques

Outlier Detection

• Statistical approaches
• Distance-based methods
• Density-based methods
• Isolation Forest
• Local Outlier Factor (LOF)
• One-Class SVM
• Autoencoders for anomaly detection

Advanced Data Mining

• Text mining and web mining
• Graph mining: community detection, link prediction
• Social network analysis
• Spatial data mining
• Multimedia data mining
• Mining data streams

Phase 10: MLOps & Production (6-8 weeks)

Model Deployment

• Model serialization: pickle, joblib, ONNX
• REST API development: Flask, FastAPI
• Containerization: Docker
• Orchestration: Kubernetes
• Serverless deployment: AWS Lambda, Google Cloud Functions
• Model serving: TensorFlow Serving, TorchServe

ML Pipeline Automation

• CI/CD for ML: Jenkins, GitLab CI, GitHub Actions
• Feature stores: Feast, Tecton
• Experiment tracking: MLflow, Weights & Biases, Neptune
• Model registry
• Automated retraining pipelines
• Data versioning: DVC, Pachyderm

Monitoring & Maintenance

• Model performance monitoring
• Data drift detection
• Concept drift
• Model explainability: SHAP, LIME, ELI5
• A/B testing frameworks
• Model versioning
• Rollback strategies
• Logging and alerting

Production Best Practices

• Scalability considerations
• Latency optimization
• Batch vs real-time predictions
• Model compression and quantization
• Edge deployment
• Security in ML systems
• Ethical AI and bias mitigation