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DATA SCIENCE LONG-TERM PROGRAM

Data Science & ML Course

From data wrangling to models that survive production.

DURATION
6 months
FEE
₹49,999
PAYMENT
₹10,000 × 6
FORMAT
Live · online
✓ Live mentor-led classes✓ Lifetime recordings✓ Certificate + placement support
Data Science & ML course training in India — Careers Ninza, Kolkata
QUICK ANSWERS

Data Science & ML course fee: ₹49,999 all inclusive, or No-Cost EMI of ₹10,000 × 6 with zero interest.

Duration: 6 months, part-time, with live weekday evening sessions from 7pm IST and weekend workshops.

Format: Live online across India, plus classroom batches in Kolkata, Asansol and Durgapur. Every session recorded and yours for life.

Who it suits: Beginner to job-ready. No prior experience required.

You finish with: Three end-to-end ML projects, a verifiable certificate, and twelve months of placement support.

Roles it prepares you for: Data scientist, Machine learning engineer, Applied scientist, Analytics consultant.

About the Data Science & ML course

Six months covering the full path: statistics and Python, feature engineering, model training and evaluation, then the deployment and monitoring most courses skip.

Every module is anchored to a business decision the model is supposed to improve — churn, pricing, demand, risk — so you can explain value, not just accuracy.

Who this program is for

If two or more of these describe you, this is the right course.

— Analysts moving from reporting into modelling
— Engineers adding machine learning to their skill set
— Domain experts in finance, retail or healthcare using their data
— Graduates targeting data scientist roles
— Managers who commission models and need to judge them

What you will learn

1 Python and statistics for real, messy datasets
2 Feature engineering that beats model tinkering
3 Train and evaluate regression, classification and tree ensembles
4 Avoid leakage, drift and the accuracy trap
5 Build forecasting and recommendation baselines
6 Deploy, monitor and retrain a model in production

Data Science & ML syllabus

12 modules, each taught live and reviewed by a mentor.

1 Module 1 — Foundations: mathematics and Python View
•Python for data science: environments, packages, control flow, functions, OOP basics, error handling
•NumPy: arrays, broadcasting, vectorisation, linear algebra operations
•Pandas to professional depth: indexing, groupby, merge, reshape, time series, memory optimisation
•Linear algebra that matters: vectors, matrices, dot products, eigenvalues in the context of PCA
•Calculus intuition for optimisation: derivatives, gradients, chain rule as used in gradient descent
•Probability: distributions, conditional probability, Bayes theorem, expectation and variance
•Jupyter, virtual environments, Git and reproducible project structure
2 Module 2 — Statistics and experimental design View
•Descriptive statistics, distributions and the central limit theorem in practice
•Sampling strategies, sampling bias and confidence interval construction
•Hypothesis testing: t-tests, ANOVA, chi-square, non-parametric alternatives
•p-values, effect sizes and the multiple comparisons problem
•A/B test design: power analysis, sample size, minimum detectable effect, sequential testing pitfalls
•Causal inference introduction: confounding, randomisation, difference-in-differences, propensity matching
•Bootstrapping and permutation tests for practical uncertainty estimation
3 Module 3 — SQL and data acquisition View
•SQL for data scientists: joins, window functions, CTEs, aggregation at scale
•Query optimisation and working with large tables efficiently
•Extracting analytical datasets reproducibly rather than by ad hoc query
•APIs, JSON handling, pagination, rate limits and authentication
•Web scraping ethics and technique where permitted, and parsing structured documents
•Working with files at scale: CSV, Parquet, compression and chunked reading
4 Module 4 — Exploratory analysis and feature engineering View
•Systematic EDA: distributions, missingness patterns, relationships, target leakage detection
•Data quality remediation: imputation strategies and their assumptions, outlier treatment, type coercion
•Encoding categorical variables: one-hot, ordinal, target encoding and leakage-safe implementation
•Scaling, normalisation, transformation and when each is required by the model class
•Feature creation: ratios, interactions, aggregations, time-based windows, lag features
•Text features: bag of words, TF-IDF, embeddings as features
•Datetime feature extraction and cyclical encoding
•Feature selection: filter, wrapper and embedded methods; multicollinearity handling
•Dimensionality reduction: PCA, t-SNE and UMAP with correct interpretation
•Building a leakage-free preprocessing pipeline with scikit-learn
5 Module 5 — Supervised learning View
•Regression: linear, polynomial, ridge, lasso, elastic net — assumptions and diagnostics
•Classification: logistic regression, k-nearest neighbours, naive Bayes, support vector machines
•Decision trees: splitting criteria, depth control, interpretability
•Ensemble methods: bagging, random forests, extra trees
•Gradient boosting in depth: XGBoost, LightGBM, CatBoost — hyperparameters that matter and why
•Imbalanced classification: resampling, class weights, threshold selection, and why accuracy is the wrong metric
•Cross-validation strategies including stratified, grouped and time-series splits
•Hyperparameter optimisation: grid, random, Bayesian and Optuna in practice
•Model evaluation: precision, recall, F1, ROC AUC, PR AUC, log loss, calibration, and choosing the metric that matches the business decision
•Model interpretation: coefficients, feature importance, permutation importance, SHAP and partial dependence
6 Module 6 — Unsupervised learning and recommendation View
•Clustering: k-means, hierarchical, DBSCAN, Gaussian mixtures, and validating cluster quality
•Customer segmentation as an applied clustering problem with business interpretation
•Anomaly detection: isolation forest, one-class SVM, autoencoder approaches
•Association rule mining and market basket analysis
•Recommender systems: collaborative filtering, matrix factorisation, content-based and hybrid approaches
•Cold start handling and evaluation metrics for recommendation
7 Module 7 — Time series and forecasting View
•Time series components: trend, seasonality, cyclicality, residual
•Stationarity, differencing and autocorrelation diagnostics
•Classical models: exponential smoothing, ARIMA and SARIMA
•Prophet and its appropriate use cases
•Machine learning for time series: lag features, rolling statistics, and correct validation
•Hierarchical and multi-series forecasting for retail and demand planning
•Forecast evaluation: MAE, RMSE, MAPE, and why MAPE misleads on low-volume series
•Building a forecast a business will actually plan against, including uncertainty intervals
8 Module 8 — Deep learning View
•Neural network fundamentals: perceptron, activation functions, loss functions, backpropagation
•Training mechanics: optimisers, learning rate schedules, batch size, regularisation, early stopping
•PyTorch essentials: tensors, autograd, datasets, dataloaders, training loops
•Convolutional networks for image tasks and transfer learning with pretrained models
•Recurrent networks and LSTMs for sequence data, and their limitations
•Transformer architecture explained: attention, positional encoding, encoder-decoder structure
•Fine-tuning pretrained transformers for classification and extraction tasks
•When deep learning is the wrong choice, and why gradient boosting still wins on tabular data
9 Module 9 — Natural language processing View
•Text preprocessing: tokenisation, normalisation, stemming, lemmatisation, and Indian language considerations
•Vector representations: TF-IDF, word2vec, contextual embeddings
•Classification and named entity recognition with transformer models
•Topic modelling and document clustering
•Sentiment analysis done properly, including domain adaptation
•Using large language models for extraction, classification and summarisation in a pipeline
•Retrieval-augmented generation basics: chunking, embedding, vector search, reranking
•Evaluating LLM outputs systematically rather than by impression
10 Module 10 — MLOps and production deployment View
•Model packaging and serving: FastAPI, containerisation with Docker
•Batch versus real-time inference and the architecture each requires
•Experiment tracking with MLflow: parameters, metrics, artefacts, model registry
•Feature stores and the training-serving skew problem
•Monitoring in production: data drift, concept drift, prediction distribution shift, performance decay
•Retraining strategy: triggers, cadence, validation gates and rollback
•CI/CD for machine learning and automated testing of data and models
•Cost management: inference cost, GPU usage, and knowing when a simpler model is commercially correct
•Responsible AI: bias detection, fairness metrics, explainability requirements, DPDP Act implications in India
11 Module 11 — Cloud and scale View
•Cloud ML platforms in outline: AWS SageMaker, Google Vertex AI, Azure ML
•Cloud storage, compute and cost control for training workloads
•Distributed data processing with Spark and PySpark fundamentals
•Working with BigQuery or Snowflake as an analytical and ML data source
•Orchestration with Airflow: DAGs, scheduling, dependency management, failure handling
•Choosing an architecture appropriate to the organisation rather than the most impressive one
12 Module 12 — Capstone and career preparation View
•Project one: end-to-end supervised learning problem with honest baseline comparison and business memo
•Project two: a deployed model with API, monitoring and retraining plan documented
•Project three: a specialised project in forecasting, NLP or recommendation aligned to your target industry
•Portfolio and GitHub presentation: README quality, reproducibility, commit hygiene
•Technical interview preparation: SQL, Python, statistics, ML theory and case rounds
•Explaining model decisions and trade-offs to non-technical stakeholders under questioning
•Resume positioning for data scientist, ML engineer and applied scientist roles
•Mock interviews with practitioners and written feedback

Tools you will work in

PythonPandas / NumPyscikit-learnXGBoostSQLMLflowDockerJupyter

What you walk out with

OUTCOME
Three end-to-end ML projects
OUTCOME
A deployed and monitored model
OUTCOME
A business memo per project

Roles this prepares you for

Salary depends on your city, experience and the strength of your project, so we do not publish package figures we cannot substantiate.

Data scientistMachine learning engineerApplied scientistAnalytics consultant

Fees and payment

TOTAL PROGRAM FEE
₹49,999
All inclusive. No registration or material charges.
PAYMENT PLAN
₹10,000 × 6
No interest, no processing fee. First instalment at enrolment.
✓  Live classes with a working practitioner
✓  Lifetime access to every recording
✓  Weekly doubt-clearing session
✓  Certified project reviewed by a mentor
✓  Rejoin any future batch free if you fall behind

Data Science & ML training across India

Batches are live online, so learners join from every state. These are the cities we hear from most — each has its own page with local batch and fee details.

See all locations across India →

Data Science & ML — frequently asked questions

Still unsure? Call an advisor on +91 90888 39993.

What is the Data Science & ML course fee in India?+

The Data Science & ML course fee at Careers Ninza is ₹49,999, inclusive of everything. You can pay it as ₹10,000 × 6 under our No-Cost EMI plan, with no interest and no processing charge. The fee covers all live classes, lifetime recordings, weekly doubt-clearing sessions, assessments, the mentor-reviewed capstone project and placement assistance.

Is Data Science & ML available online across India?+

Yes. Every batch runs live online, so you can join Data Science & ML from anywhere in India. We currently have students from Kolkata, Asansol, Durgapur, Siliguri, Howrah, Gwalior, Patna, Ranchi, Bhubaneswar, Guwahati, Pune, Mumbai and many other cities, plus learners outside India. On-site delivery is available in Kolkata, Asansol and Durgapur, and on request in other cities for corporate or campus groups.

How long is the Data Science & ML course and what is the weekly time commitment?+

Data Science & ML runs for 6 months. Plan on six to eight hours a week: two live weekday evening sessions plus project work, with occasional weekend workshops. Working professionals complete it without taking leave.

Do I need prior experience to join Data Science & ML?+

Beginner to job-ready. The course starts from first principles and the mentor calibrates pace to the batch. Basic comfort with a computer is enough; no prior coding background is assumed. If a specific prerequisite genuinely matters for your goal, the advisor will tell you honestly on the counselling call rather than take the enrolment.

Who teaches Data Science & ML?+

A working practitioner in the field, not a full-time trainer. Careers Ninza has more than 100 industry mentors who teach while still doing the job, so the examples come from current work rather than a textbook. You can ask which mentor is assigned to your batch before enrolling, or meet them at a free live masterclass.

What will I build during the Data Science & ML course?+

One real capstone project, scoped in the first fortnight and carried through every module: three end-to-end ML projects. It is reviewed by your mentor with written feedback, and it is what interviewers or clients actually discuss with you afterwards.

Is the Data Science & ML certificate recognised?+

You receive a verifiable Careers Ninza industry certificate with a unique ID and a public verification link, issued by Ninza Career Solutions Pvt. Ltd. once your project is accepted. It is an industry certificate, not a government-accredited degree or diploma, and we say so plainly. Its weight comes from the reviewed project behind it.

Does Data Science & ML include placement support?+

Yes. Long-term programs include portfolio review, resume and LinkedIn clinics, mock interviews with working professionals, and referrals to our hiring partner network, for twelve months after completion. There is no success fee and no income-share agreement. We do not guarantee employment, and we would be cautious of any institute that does.

What career roles does Data Science & ML prepare me for?+

Data scientist, Machine learning engineer, Applied scientist, Analytics consultant. Salary depends on your city, prior experience and the strength of your project, so we do not publish package figures we cannot substantiate; an advisor will give you an honest band for your target role and city.

What if I miss classes or fall behind in Data Science & ML?+

Every session is recorded and stays yours for life. There is a weekly doubt-clearing slot where you can bring your own work, and if you fall too far behind you may rejoin any future batch of Data Science & ML free of charge, with no conditions.

How is Data Science & ML at Careers Ninza different from a recorded online course?+

It is live and never pre-recorded. There is no cheaper self-paced tier, because removing the live class removes the reason the course works. Batches are capped so a quiet student still gets asked questions, and the mentor reviews your project personally rather than auto-marking a quiz.

Can I see a Data Science & ML class before paying?+

Yes. Careers Ninza runs free live masterclasses every week. Reserve a seat, watch a practitioner teach for ninety minutes, ask questions, and then decide. To enrol or to ask which batch suits you, call +91 90888 39993 or message us on WhatsApp.

How do I enrol in Data Science & ML?+

Call +91 90888 39993, email [email protected], or send an enquiry on WhatsApp from this page. An advisor confirms whether Data Science & ML fits your goal, then sends written confirmation of your batch, the fee and No-Cost EMI of ₹10,000 × 6. Seats are capped, so batches close once full.

Data Science & ML across India — city questions

Same class, same fee, same mentor, wherever you are.

Is Data Science & ML available in my city?+

Yes. Every batch runs live online, so learners across India attend the same class in real time — including Visakhapatnam, Guwahati, Patna, Raipur, Ahmedabad, Surat, Vadodara, Gurugram, Ranchi, Bengaluru and every other city and town. There is no separate recorded version for students outside Kolkata, and no city has a different curriculum.

Does the Data Science & ML course fee change by city?+

No. The fee is ₹49,999 everywhere in India, with the same No-Cost EMI of ₹10,000 × 6. We do not price differently for metro and non-metro learners, and there are no travel or centre charges for online batches.

Do you have a classroom centre near me?+

Classroom batches run in Kolkata, Asansol and Durgapur. Everywhere else in India is served by live online delivery, which most working professionals and students prefer. On-site delivery in other cities is available for corporate teams and college groups on request.

What are the batch timings for people in different time zones within India?+

India runs on a single time zone, so timings are identical nationwide: weekday evening sessions from 7pm IST with weekend workshops. Every session is recorded and stays yours for life, so a late shift or travel never costs you a module.

Will placement support help me find work in my own city?+

Yes. Our hiring partner network covers roles across Indian cities, and a growing share of openings are remote and open to candidates anywhere in India. We share your profile only with your consent, for a specific role. We do not guarantee employment.

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Seats in every batch are capped

Talk to an advisor for fifteen minutes. We will tell you whether Data Science & ML fits your goal — or which program does instead.

APPLY NOW CALL +91 90888 39993