Data Science Training in Pune with Placement

Set up Python 3.13 with virtual environments and Jupyter, write idiomatic Python — comprehensions, generators, dunder methods, type hints — and graduate to pandas 2 with the Arrow backend (10–100× faster than legacy pandas on the operations you will run daily). NumPy 2 vectorisation, broadcasting, and the rules of when to drop down to NumPy from pandas. By the end of week 2, every student can load a 5-million-row CSV, clean it, and answer ten business questions in under 30 minutes.

SQL is half of every Pune data-job. Cover the parts that actually matter for analytics — joins (inner, left, anti, semi), aggregations, CTEs, window functions (ROW_NUMBER, RANK, LAG, LEAD, running totals, moving averages), self-joins, and the EXPLAIN plan basics that separate a 5-second query from a 5-minute one. Hands-on with PostgreSQL 16 against a public retail dataset; query plans reviewed in class. By the end of the week, every student can rewrite a slow analyst query in two ways and explain which is faster and why.

The statistics that hiring panels actually test for and that you will use weekly. Descriptive statistics (mean, median, variance, percentiles), probability distributions (Bernoulli, Binomial, Normal, Poisson), the central limit theorem, hypothesis testing (z-test, t-test, chi-square, Mann-Whitney), p-values, confidence intervals, A/B testing fundamentals, Bayes' theorem, and bootstrap resampling. We use simulation in Python (NumPy + scipy.stats) to build intuition rather than relying on formula recall.

EDA is the highest-leverage skill in working data science — and the skill most courses skip. Cover the discipline of asking a dataset the right questions: missing-value handling, outlier detection, distribution checks, correlation analysis, segment-wise drill-downs. Visualisation with matplotlib for control, seaborn for default-pretty, and plotly for interactivity. We finish with a public Indian dataset (RBI, NSE, India Open Data Portal) — every student produces a 10-page exploratory PDF report graded against a Pune analyst-interview rubric.

The stage that moves a model more than swapping algorithms does. You work through encoding (one-hot, ordinal, target, frequency) and when each leaks information; scaling and transformation, including why tree models do not care and distance-based models do; and constructing features from dates, text, aggregations and domain knowledge. Then selection — filter, wrapper and embedded methods, multicollinearity, and the discipline of removing features that only look useful. Data leakage is treated as a first-class topic here, because it is the single most common reason a model that scored 0.95 in a notebook fails in production.

scikit-learn 1.5+ as the production-grade ML library it is. Supervised learning — linear and logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM, CatBoost), SVMs. Unsupervised — k-means, hierarchical clustering, PCA, t-SNE. Cross-validation done right (stratified, time-series-aware), hyperparameter tuning with Optuna, and the discipline of train/validation/test splits that prevents the leak-and-overfit failure mode that ends most fresher data-science interviews. We build an end-to-end pipeline using scikit-learn's Pipeline + ColumnTransformer abstractions.

PyTorch 2.4+ as the dominant framework for new deep-learning work in Pune. Build neural networks from scratch (forward/backward pass, autograd intuition), then move to convolutional networks for image work and transformers for NLP. Hugging Face Transformers for pre-trained model usage — sentence embeddings, sentiment classification, named-entity recognition. We deliberately keep this module practical — the goal is to use deep learning effectively, not to derive backprop on a whiteboard.

Time-series breaks the assumptions every other model in the course relies on, so it gets its own module. Decomposition into trend, seasonality and residual; stationarity and why it matters; ACF and PACF for order selection; then the model families in order of pragmatism — naive and seasonal baselines that are surprisingly hard to beat, exponential smoothing, ARIMA and SARIMA, and Prophet. Validation is the part most courses get wrong: you use forward-chaining splits, never random ones, because a random split lets the model see the future.

Three applied areas that come up constantly in Indian analytics and product teams. Explainability first, because a model nobody can interrogate does not get deployed in finance, insurance or healthcare: feature importance, permutation importance, SHAP and LIME, partial dependence, and how to answer 'why was this application rejected?'. Then anomaly detection for fraud, quality and monitoring — statistical methods, Isolation Forest, one-class SVM and autoencoders. Finally recommender systems: collaborative filtering, content-based approaches, matrix factorisation, and the cold-start problem every real deployment hits.

The week most data-science courses skip and most Pune hiring panels test for. Experiment tracking with MLflow (runs, metrics, artefacts, model registry), model serving with FastAPI (Pydantic v2, async endpoints, JWT auth), Docker containerisation of inference services, basic monitoring (Prometheus + Grafana), and a small CI/CD pipeline (GitHub Actions running tests, building containers, deploying to AWS). We deploy at least one model to a live endpoint that we then call from a Streamlit dashboard.

A model nobody can use is worth nothing. Build production-ready dashboards with Streamlit (Python-native, fast iteration) and Power BI (the default in Pune BFSI and large captives). Cover dashboard design principles, KPI selection, drill-down hierarchies, and the discipline of storytelling for non-technical stakeholders. We finish with a 15-minute mock business presentation in front of the class — the soft skill that decides who gets promoted in the first 18 months.

The 2026 differentiator. Use Claude or OpenAI APIs to draft SQL queries, summarise long datasets, generate first-draft dashboards, and ship a small text-to-SQL service against a real database. Cover retrieval-augmented generation (RAG) for analytics over internal documentation, prompt design for analytics workflows, cost / latency / safety trade-offs, and the limits of LLMs for reasoning over numbers. This module is what separates 2026 Pune data-science hiring from 2022 hiring.

Two weeks of full-time capstone work plus structured interview preparation. Pick one of three capstone projects (see Capstone Projects). Mock interviews calibrated for Pune analytics hiring panels — Tiger Analytics, Fractal, ZS, MathCo, Persistent. Includes a SQL mock round (these companies test SQL hard), a case-study round, and a behavioural / domain round. Resume / LinkedIn / GitHub polish included.