Data Science
Data Scientist remains one of the highest-paying tech roles — ₹12-30 LPA at mid-level, with senior scientists at top product companies crossing ₹50 LPA. This intensive three-month program walks you through the complete data science workflow: Python and statistics fundamentals, exploratory analysis, feature engineering, classical machine learning with scikit-learn, deep learning with TensorFlow / PyTorch, NLP, time-series forecasting, A/B testing, and model deployment. Four capstone projects across e-commerce, healthcare, finance, and marketing leave you with a portfolio hiring managers actually care about.
0 lessons
What you'll learn
- Apply every major ML algorithm with Python + scikit-learn
- Train deep learning models in TensorFlow + PyTorch confidently
- Build NLP pipelines with modern transformers (BERT, GPT)
- Forecast time-series data using ARIMA, Prophet, and LSTM
- Design + analyze A/B tests with proper statistical rigour
- Engineer features from raw data — the make-or-break skill
- Deploy ML models as REST APIs with FastAPI + Docker
- Communicate complex models to non-technical stakeholders
- Pass Data Scientist technical interviews at top product companies
- Land Data Scientist roles paying ₹10-30 LPA
Technologies Taught
Python — NumPy, Pandas, Matplotlib, Seaborn, scikit-learnStatistics + probability + hypothesis testing fundamentalsClassical ML — regression, classification, clustering, ensemblesDeep learning — TensorFlow, PyTorch, KerasNatural Language Processing — transformers, BERT, Hugging FaceTime-series — ARIMA, Prophet, LSTM, Exponential SmoothingA/B testing + experimental designSQL for data exploration + feature engineeringMLflow + FastAPI for model lifecycle + deploymentVector databases + semantic search basics
Training Unique Features
- Full data science journey — statistics through production deployment
- Four capstone projects across e-commerce, healthcare, finance, marketing
- Strong mathematical foundations — not just sklearn API users
- Daily 90-minute live class with live coding + debugging walkthroughs
- Hands-on with real, messy datasets that mirror production data
- GPU-enabled labs for deep learning exercises
- Mock interview rounds covering ML coding + system design + case studies
- Trained by Data Scientists with 10+ years of industry + research experience
- Resume + LinkedIn + GitHub polish for Data Science roles
- Direct intros to product companies actively hiring Data Scientists
Job Opportunities
Top job positions you can apply for after completing this training.
| Job Role | Experience | Salary Range |
|---|---|---|
| 1. Junior Data Scientist | Fresher to 2+ Years | 4–8 LPA |
| 2. Data Scientist (Entry/Mid) | Fresher to 3+ Years | 6–12 LPA |
| 3. Machine Learning Engineer (Junior) | 1 to 3+ Years | 6–12 LPA |
| 4. Business Analyst / BI Analyst | 2 to 4+ Years | 7–12 LPA |
| 5. Data Scientist (Mid-Level) | 3 to 5+ Years | 12–20 LPA |
| 6. Machine Learning Engineer (Mid-Level) | 3 to 5+ Years | 12–22 LPA |
| 7. Senior Data Analyst | 4 to 6+ Years | 10–18 LPA |
| 8. Senior Data Scientist | 4 to 7+ Years | 20–35 LPA |
| 9. Applied Scientist (ML/NLP/CV) | 5 to 8+ Years | 20–40 LPA |
| 10. Data Engineer (ML/AI Focused) | 5 to 8+ Years | 12–25 LPA |
You Can Work As
Data ScientistMachine Learning EngineerApplied ScientistAI EngineerResearch ScientistSenior Data Scientist
Upcoming In-Demand Jobs
LLM Engineer / ResearcherCausal Inference SpecialistMLOps EngineerAI Product Scientist
Training Curriculum
FOUNDATIONS OF DATA SCIENCE
57 topics
FOUNDATIONS OF DATA SCIENCE
- •Python syntax essentials
- •Variables, data types
- •Control flow (if/else), loops
- •Functions, modules, virtual environments
- •Introduction to OOPS in Python
- •Jupyter Notebook setup
- •Basic scripting
- •Working with `os`, `json`, `requests`
- •Small exercises (temperature converter, file reader, simple API call)
- •Arrays, vectorization
- •DataFrames, indexing, grouping
- •Plotting foundations (line, bar, scatter, histograms)
- •NumPy exercises (array math, reshaping)
- •Pandas exercises (filtering, groupby, joins)
- •Exploratory plots with Matplotlib/Seaborn
- •Mini-project: Clean + Explore a CSV dataset
- •Descriptive stats: mean, variance, skew, kurtosis
- •Probability basics
- •Distributions (Normal, Bernoulli, Binomial)
- •Correlation vs causation
- •Confidence intervals, hypothesis testing
- •Distribution visualizations
- •t-test, chi-square test on sample data
- •Correlation heatmaps and interpretation
- •Data cleaning workflow
- •Handling missing values & outliers
- •Feature scaling & encoding
- •Train-test splits
- •Leakage and good ML practice
- •Handling NaNs in Pandas
- •Encoding categorical variables (OneHot, Label)
- •Normalization & standardization
- •Pipeline building in scikit-learn
- •Mini-project: full cleaning workflow
- •ML building blocks
- •Bias–variance tradeoff
- •Cross-validation
- •Regularization (Lasso, Ridge)
- •Algorithms:
- •Linear regression
- •Logistic regression
- •KNN
- •Naïve Bayes
- •Decision trees
- •Random forest, Gradient Boosting (intro)
- •Build regression + classification models in sklearn
- •Model evaluation metrics: RMSE, MAE, Accuracy, Precision, Recall, F1
- •Hyperparameter tuning with GridSearchCV
- •Mini-project: Prediction task (housing prices or churn)
- •Clustering concepts
- •K-Means algorithm
- •Hierarchical clustering
- •Dimensionality reduction: PCA theory & intuition
- •Elbow method, silhouette score
- •Apply K-Means on a customer segmentation dataset
- •Hierarchical clustering dendrogram
- •PCA decomposition & visualization
ADVANCED DATA SCIENCE & DEEP LEARNING
42 topics
ADVANCED DATA SCIENCE & DEEP LEARNING
- •Neuron, weights, biases
- •Activation functions
- •Loss functions
- •Gradient descent & backpropagation (conceptual)
- •Overfitting & regularization in neural nets
- •Build a small neural net from scratch in NumPy (forward pass only)
- •Tensors
- •Autograd
- •Datasets/Dataloaders
- •Optimizers
- •Build a classifier for MNIST or a tabular dataset
- •Custom training loop (forward → loss → backward → step)
- •Saving/loading models
- •CNNs — convolution, filters, pooling
- •RNNs, LSTMs, GRUs (overview only)
- •Regularization in deep nets: dropout, batch norm
- •CNN for image classification (CIFAR-10 or fashion-MNIST)
- •Compare performance with data augmentation
- •Modern vision models
- •Transfer learning
- •Feature extraction vs fine-tuning
- •ResNet, EfficientNet (high-level)
- •Fine-tune a pretrained ResNet on a small custom dataset
- •Implement augmentations using torchvision
- •Why RNNs fail
- •Self-attention mechanism
- •Encoder–decoder design
- •Transformer block anatomy
- •Positional embeddings
- •Overview of BERT, GPT, Vision Transformers
- •Attention mechanism in PyTorch
- •Tokenization, subword embeddings
- •Pretraining vs fine-tuning
- •Task types: classification, NER, QA
- •Fine-tune BERT (HuggingFace) for text classification
- •Evaluate & interpret embeddings
- •Export model for inference
- •Intro to LLMs beyond transformers
- •Vector databases & retrieval
- •MLOps foundations
- •Model interpretability (SHAP/LIME)
- •Reinforcement learning (very high level)
Training Instructed By
MK
Mr. Karan E---
A senior Data Science & Artificial Intelligence practitioner with 19+ years of experience in designing, deploying, and scaling real-world ML and AI systems. With strong expertise in statistics, software engineering, and applied research, he has led large-scale machine learning, deep learning, and generative AI initiatives across multiple industries. He also contributes to the AI community through publications, patents, and mentorship in AI strategy and scalable system design. Approved trainer by Raj Cloud Technologies.
Approved trainer by Raj Cloud Technologies
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