Data Science Course in Kolkata
Industry Curriculum & Syllabus Modules
Python for Data Science Core
Jupyter environments, Python data structures, list comprehensions, and functional methods.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Mathematics & Statistics for Data Science
Linear algebra, matrix operations, probability distributions, hypothesis testing, and p-values.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
High-Performance Computing with NumPy
Array creation, broadcasting, vectorized math operations, and linear algebra routines.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Data Wrangling & Feature Engineering with Pandas
Data cleaning, imputation, handling outliers, categorical encoding, and feature scaling.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Exploratory Data Analysis (EDA) & Data Viz
Visual analysis with Matplotlib and Seaborn, pair plots, box plots, and multivariate analysis.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Supervised Learning: Regression Algorithms
Linear regression, multiple regression, ridge/lasso regularization, and evaluation metrics (RMSE, R2).
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Supervised Learning: Classification Models
Logistic regression, decision trees, random forests, SVM, and confusion matrix metrics.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Unsupervised Learning & Clustering
K-Means clustering, hierarchical clustering, PCA dimensionality reduction, and anomaly detection.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Advanced Ensemble Learning
XGBoost, LightGBM, AdaBoost, cross-validation, and hyperparameter tuning with GridSearchCV.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Introduction to Deep Learning & Neural Networks
Perceptrons, multi-layer neural networks, activation functions, backpropagation, and TensorFlow/Keras.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Natural Language Processing (NLP) Basics
Text tokenization, lemmatization, TF-IDF vectorization, sentiment classification, and spaCy.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
Model Deployment & Industry Capstone
Deploying ML models with Streamlit/Flask, Docker basics, live client projects, and placement drives.
- Hands-on practical code implementations
- Live scenario architecture & debugging
- Enterprise code review & optimization
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