Data Science Course — SeekhowithRua
Master data science end-to-end — Python, NumPy, Pandas, statistics, ML, deep learning, NLP, data engineering and MLOps. data science course india, machine learning course hindi, python data science, deep learning india
Python Foundations
Variables & Data Types: int, float, str, bool, Type casting, Variable naming rules, Constants
Operators & Expressions: Arithmetic operators, Comparison operators, Logical operators, Bitwise operators
Control Flow: if / elif / else, Nested conditions, Ternary expressions, Match statement (3.10+)
Loops: for loops, while loops, break / continue / pass, Loop comprehensions
Functions: def keyword, Parameters & arguments, *args and **kwargs, Lambda functions
Strings Deep Dive: String methods, f-strings, String slicing, Regular expressions
Lists & Tuples: List operations, List methods, Tuple immutability, Packing/unpacking
Dictionaries & Sets: dict methods, set operations, defaultdict, Counter
File I/O: open() / read() / write(), Context managers, CSV files, JSON files
Error Handling: try / except / finally, Custom exceptions, raise, Logging
NumPy & Pandas
NumPy Arrays: ndarray creation, Shape & reshape, Indexing & slicing, Broadcasting
NumPy Operations: Arithmetic ops, Statistical functions, Linear algebra, Random module
Pandas Series: Series creation, Index operations, Boolean indexing, apply()
Pandas DataFrame: DataFrame creation, read_csv / read_excel, info() & describe(), head() & tail()
Data Selection: loc vs iloc, Conditional selection, MultiIndex, at & iat
Data Cleaning: Handling NaN, fillna() & dropna(), Duplicates, Data type conversion
GroupBy & Aggregation: groupby(), agg() functions, pivot_table(), crosstab()
Merging & Joining: merge() types, concat(), join(), append()
Time Series: DatetimeIndex, resample(), rolling(), shift()
Data Export: to_csv(), to_excel(), to_json(), SQL export
Data Visualization
Matplotlib Basics: pyplot API, Figure & Axes, Line plots, Saving figures
Matplotlib Customization: Colors & styles, Annotations, Subplots, Twinx axes
Seaborn Intro: Statistical plots, distplot / histplot, countplot, Themes
Seaborn Advanced: heatmap(), pairplot(), FacetGrid, clustermap()
Plotly Basics: px.line / bar / scatter, Interactive plots, Hover data, Layout config
Plotly Advanced: Subplots, 3D plots, Animations, Dash intro
Chart Types Guide: When to use bar vs line, Pie chart pitfalls, Box plots, Violin plots
Storytelling with Data: Color psychology, Annotations strategy, Audience alignment, Dashboard design
Geographic Plots: Folium maps, Choropleth maps, Plotly geo, Geopandas intro
EDA Project: Dataset exploration, Missing value viz, Correlation matrix, Full EDA report
Statistics & Math
Descriptive Statistics: Mean, median, mode, Variance & std dev, Skewness & kurtosis, Percentiles
Probability Basics: Sample space & events, Conditional probability, Bayes theorem, Probability rules
Distributions: Normal distribution, Binomial distribution, Poisson distribution, Uniform distribution
Inferential Statistics: Hypothesis testing, p-values, Confidence intervals, t-tests
Correlation & Regression: Pearson correlation, Spearman correlation, Linear regression math, Residual analysis
Linear Algebra: Vectors & matrices, Matrix operations, Eigenvalues, SVD intro
Calculus for DS: Derivatives basics, Chain rule, Gradients, Optimization intuition
Information Theory: Entropy, KL Divergence, Mutual information, Cross entropy
SciPy for Stats: scipy.stats module, Distribution fitting, Statistical tests, Curve fitting
Math Project: Stats on real dataset, Probability simulations, Linear algebra in ML, Full math report
Machine Learning
ML Fundamentals: Supervised vs unsupervised, Training/test split, Overfitting & underfitting, Bias-variance tradeoff
Linear & Logistic Regression: Simple linear regression, Multiple regression, Sigmoid function, Decision boundary
Decision Trees & Random Forests: Gini impurity, Information gain, Bagging, Feature importance
SVM & KNN: Hyperplane, Kernel trick, Distance metrics, K selection
Clustering: K-Means, DBSCAN, Hierarchical clustering, Silhouette score
Ensemble Methods: AdaBoost, Gradient Boosting, XGBoost, Stacking
Model Evaluation: Confusion matrix, Precision & recall, F1 score, ROC-AUC
Feature Engineering: Encoding techniques, Scaling methods, Feature creation, Selection methods
Hyperparameter Tuning: GridSearchCV, RandomizedSearchCV, Optuna, Cross-validation
Scikit-learn Pipeline: Pipeline API, ColumnTransformer, Model persistence, Full workflow
Deep Learning
Neural Network Basics: Perceptron, Activation functions, Forward pass, Backpropagation
TensorFlow & Keras: Sequential API, Functional API, Model compilation, fit() & evaluate()
PyTorch Basics: Tensor operations, Autograd, nn.Module, Training loop
CNNs: Convolution operation, Pooling, Famous architectures, Transfer learning
RNNs & LSTMs: Sequence modeling, Vanishing gradient, LSTM gates, Bidirectional RNN
Transformers: Attention mechanism, Self-attention, Encoder-decoder, BERT & GPT intro
GANs: Generator & discriminator, Training GAN, Mode collapse, StyleGAN overview
DL Optimization: Adam & SGD, Learning rate scheduling, Batch normalization, Dropout
Computer Vision: Image classification, Object detection, YOLO, Semantic segmentation
DL Project: Dataset pipeline, Model checkpointing, TensorBoard, ONNX export
NLP
Text Preprocessing: Tokenization, Stemming & lemmatization, Stop words, Text normalization
Text Vectorization: Bag of Words, TF-IDF, Word2Vec, GloVe
Sentiment Analysis: Lexicon-based, ML-based, VADER, Fine-tuned BERT
Text Classification: Naive Bayes for text, SVM for text, FastText, BERT classification
Named Entity Recognition: spaCy NER, BiLSTM-CRF, Hugging Face NER, Custom NER training
Topic Modeling: LDA, NMF, BERTopic, Coherence score
Language Models: N-gram models, Neural LM, GPT series, Prompt engineering
RAG Systems: Vector databases, Embeddings, Retrieval pipeline, LangChain basics
Question Answering: Extractive QA, Generative QA, RAG systems, Evaluation
NLP Project: End-to-end pipeline, spaCy pipeline, Hugging Face deploy, API serving
Data Engineering
SQL Foundations: SELECT / WHERE / JOIN, GROUP BY / HAVING, Subqueries, Window functions
Advanced SQL: CTEs, Stored procedures, Indexes, Query optimization
PostgreSQL & SQLAlchemy: PostgreSQL setup, SQLAlchemy ORM, Migrations, Connection pooling
ETL Pipelines: Extract, Transform, Load, pandas ETL, Apache Airflow intro, Prefect intro
Apache Spark: RDD vs DataFrame, PySpark basics, Spark SQL, MLlib intro
Cloud for Data: AWS S3 & Athena, GCP BigQuery, Azure Data Factory, Databricks intro
Data Quality: Data profiling, Great Expectations, dbt intro, Data lineage
Streaming Data: Kafka basics, Spark Streaming, Flink intro, Real-time dashboards
NoSQL Databases: MongoDB basics, Document model, Aggregation pipeline, Redis caching
Data Engineering Project: Full pipeline build, Orchestration, Monitoring, Documentation
MLOps & Deployment
Model Serving: Flask API, FastAPI, ONNX runtime, TorchServe
Docker for ML: Dockerfile, docker-compose, Container registry, Multi-stage builds
MLflow: Experiment tracking, Model registry, Projects, Serving with MLflow
CI/CD for ML: GitHub Actions, DVC, Model testing, Automated retraining
Kubernetes for ML: Pods & services, Deployments, KubeFlow, Helm charts
Monitoring: Data drift, Model drift, Evidently AI, Prometheus + Grafana
Feature Stores: Feast, Hopsworks, Online vs offline store, Feature versioning
Model Explainability: SHAP, LIME, Captum, Grad-CAM
Responsible AI: Bias detection, Fairlearn, Model cards, GDPR compliance
Capstone Project: End-to-end project, Model training, API deployment, Dashboard
Master Data Science
Master data science end-to-end — Python, NumPy, Pandas, statistics, ML, deep learning, NLP, data engineering and MLOps.
Welcome to Data Science
Master data science end-to-end — Python, NumPy, Pandas, statistics, ML, deep learning, NLP, data engineering and MLOps. Select a module from the sidebar to begin.