Data Science Course in Khanpur

Complete Data Science Masters

Excel → ML → DL → Production Deployment

Topics Covered:

VLOOKUP/XLOOKUP/INDEX-MATCH, dynamic arrays
PivotTables, slicers, Power Query ETL
Text functions, data validation
Conditional formatting, dashboards

Topics Covered:

Variables, loops, functions, comprehensions
OOP, error handling, file I/O
SQLAlchemy + MySQL connector
Modules, lambda, decorators

Topics Covered:

CRUD + Joins + Window functions + CTEs
Normalization, indexes, EXPLAIN optimization
Stored procedures, triggers, transactions
Pagination + query performance

Topics Covered:

NumPy arrays, broadcasting, indexing
Pandas DataFrames, groupby, merge
Data cleaning: missing, outliers, duplicates
EDA: correlations, pivot tables

Topics Covered:

Matplotlib: subplots, customization
Seaborn: heatmaps, violin, pairplot
Pandas plotting, correlation matrices
Dashboard storytelling

Topics Covered:

Hypothesis testing, p-values, confidence intervals
Bias-variance tradeoff, overfitting diagnosis
Distributions, CLT, A/B testing
Regression + correlation analysis

Topics Covered:

Scaling, encoding, polynomial features
SMOTE, target encoding, imputation
Feature selection: RFE, Lasso, trees
Domain feature creation

Topics Covered:

K-fold CV, train/val/test splits
Regression: RMSE/MAE/R² metrics
Classification: ROC/F1/confusion matrix
Learning curves, baseline models

Topics Covered:

Linear/Ridge/Lasso regression
Decision Trees, Random Forest
XGBoost/LightGBM gradient boosting
Cross-validation tuning

Topics Covered:

Logistic regression, SVM, KNN
Tree ensembles, gradient boosting
Imbalanced: SMOTE, threshold tuning
ROC-AUC, PR curves

Topics Covered:

Grid/random search strategies
Bayesian optimization intro
Cross-validation with tuning
Avoiding validation overfitting

Topics Covered:

K-Means, elbow/silhouette method
Hierarchical clustering, dendrograms
DBSCAN density-based clustering
Gaussian Mixture Models

Topics Covered:

PCA principal component analysis
t-SNE, UMAP visualization
Isolation Forest anomalies
Z-score/IQR outlier detection

Topics Covered:

Perceptron, backpropagation, gradient descent
Feedforward networks, Keras/TensorFlow
Dropout, batch normalization
Loss functions, optimizers

Topics Covered:

CNN convolutions, pooling, filters
Image classification basics
LSTM/GRU sequence modeling
ARIMA + LSTM forecasting

Topics Covered:

Tokenization, TF-IDF, embeddings
Sentiment analysis, text classification
Stemming, lemmatization, stopwords
Word2Vec similarity concepts

Topics Covered:

Content-based filtering, cosine similarity
Collaborative filtering matrix factorization
Cold start solutions
Precision@K evaluation

Topics Covered:

FastAPI model serving, Docker containerization
Model serialization, MLflow tracking
CI/CD pipelines, GitHub Actions
Data drift monitoring

Topics Covered:

Dog Breed CNN, House Price Predictor
Customer Churn, Fraud Detection
Healthcare ML, ChatGPT Clone
Zomato + E-commerce systems

Want the Complete Course Details?

Download our comprehensive syllabus with detailed week-by-week breakdown, learning objectives, and project details.

Course Features & Benefits

Everything you need for a successful learning experience

Classroom-Based Learning

Learn in a focused, distraction-free classroom environment.

Expert Faculty Support

Get direct guidance from experienced and qualified trainers.

Hands-On Practical Training

Practice on real systems with live projects and exercises.

Dedicated Doubt Clearing

Ask questions face-to-face and get instant clarification.

Small Batch Size

Limited students per batch for personal attention.

Career & Job Assistance

Resume help, interview preparation, and job guidance after course completion.

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Master Data Science Engineering in Khanpur

Course Overview

From Zero to Data Scientist in 14 Months

What You'll Master

Why This 14-Month Program Wins

Production-First

8 Portfolio Projects

Job Guaranteed

Masters Faculty

Complete Data Science Masters

Module 1Excel MasteryAdvanced data analysis foundation

Excel Mastery

Topics Covered:

Module 2Python FundamentalsCore programming for data science

Python Fundamentals

Topics Covered:

Module 3SQL Database MasteryProduction database operations

SQL Database Mastery

Topics Covered:

Module 4Data Analysis ProNumPy + Pandas mastery

Data Analysis Pro

Topics Covered:

Module 5Python VisualizationPublication-ready charts

Python Visualization

Topics Covered:

Module 6Business StatisticsMath foundation for ML

Business Statistics

Topics Covered:

Module 7Feature EngineeringRaw data to ML-ready features

Feature Engineering

Topics Covered:

Module 8Model EvaluationMetrics + validation strategies

Model Evaluation

Topics Covered:

Module 9Regression ModelsSupervised learning foundation

Regression Models

Topics Covered:

Module 10Classification ModelsImbalanced + advanced classifiers

Classification Models

Topics Covered:

Module 11Hyperparameter TuningModel optimization techniques

Hyperparameter Tuning

Topics Covered:

Module 12Clustering AlgorithmsUnsupervised pattern discovery

Clustering Algorithms

Topics Covered:

Module 13Dimensionality ReductionFeature compression + anomaly detection

Dimensionality Reduction

Topics Covered:

Module 14Deep Learning BasicsNeural networks foundation

Deep Learning Basics

Topics Covered:

Module 15CNNs + Time SeriesComputer vision + forecasting

CNNs + Time Series

Topics Covered:

Module 16Natural Language ProcessingText analytics foundation

Natural Language Processing

Topics Covered:

Module 17Recommender SystemsPersonalization algorithms

Recommender Systems

Topics Covered:

Module 18MLOps & DeploymentProduction ML systems

MLOps & Deployment

Topics Covered:

Module 19Capstone Projects9+ production portfolio apps

Capstone Projects

Topics Covered:

Want the Complete Course Details?

Course Features & Benefits

Classroom-Based Learning

Expert Faculty Support

Hands-On Practical Training

Dedicated Doubt Clearing

Small Batch Size

Career & Job Assistance

Ready For Data Science Masters?

PhD Faculty

Masters Certification