Data Science Course in Nagpur

The role

What a data scientist actually does

Three job titles get used interchangeably in job adverts and they are not the same work. Knowing which one you are aiming at decides what you should be studying, and it is the question we ask first when somebody calls.

The syllabus

10 modules, in this order

The order is the argument. SQL is first and assessed hardest because everything downstream assumes it, and statistics comes before machine learning because a model you cannot interrogate is a guess with extra steps.

• SQL and the shape of real data

  First, and assessed hardest. The relational model and why a spreadsheet is not one. SELECT/WHERE/GROUP BY/HAVING and the order the database actually evaluates them. Every join type, and what NULL silently does to each — including the LEFT JOIN that quietly became an INNER JOIN because of a WHERE clause. Window functions: ROW_NUMBER, RANK, LAG/LEAD, running totals, per-group top-N. CTEs, and why a four-level nested subquery is unmaintainable. Reading a query plan well enough to answer "why is this slow". Dates, timezones and month-boundary arithmetic done correctly. Aggregation traps: COUNT(*) vs COUNT(col), AVG over NULLs. DuckDB as a local engine over CSV and Parquet — one binary, no server, files bigger than RAM. And the three ways Excel corrupts data on import.
• The 2026 Python data stack, installed correctly

  Environments with uv or miniforge — the conda-forge toolchain you will meet in restricted enterprise environments. NumPy 2.x: arrays, dtypes, broadcasting, vectorisation over loops, and NEP 50 promotion, where np.float32(3) + 3 now returns float32 — old code runs without error and returns different numbers. pandas 3.x Copy-on-Write, now enforced: df[df.a > 0]['b'] = 1 no longer modifies the frame, and SettingWithCopyWarning no longer exists to warn you. The dedicated str dtype replacing object. datetime64[us] as the default resolution, zoneinfo instead of pytz, and why plain M now raises where ME does not. Parquet as the default interchange format, not CSV. When to reach past pandas: Polars for a slow groupby, DuckDB for data larger than memory.
• Statistics you will be asked to defend

  The module bootcamps skip and interviewers probe — and the one place an AI coding assistant will hand you the wrong test with no error message. Distributions, with "normal" as an assumption you check rather than a fact you assume. Sampling distributions and standard error. Confidence intervals, and the specific wrong sentence that ends an interview. What a p-value measures and the four things people wrongly think it measures. Multiple comparisons — test twenty variants and one comes back significant for free. Effect size versus statistical significance. A/B design with power analysis before the test rather than after it disappoints you. Correlation, causation, confounding and Simpson's paradox on real reversed data. Bootstrapping when no closed form exists. statsmodels for regression with inference, as distinct from scikit-learn's prediction-only view.
• Exploratory analysis and honest visualisation

  The EDA loop — shape, types, ranges, missingness, distributions, relationships, in that order. Missing data: MCAR, MAR and MNAR, and why mean-imputation is usually the wrong reflex. Telling an outlier from a data-entry error from the interesting finding. matplotlib's explicit object API rather than the pylab state machine. seaborn for statistical plots, and choosing a chart from the question rather than from what looks impressive. How charts lie: truncated axes, dual axes, pie charts with nine slices, area encoding radius. Colour that survives colour-blindness and a monochrome printout. And writing the two-sentence finding that goes above the chart.
• Supervised learning: fit, fail, diagnose

  Organised around the fact that your first model overfits, so the skill being taught is diagnosis rather than model selection. Train, validation and test, and the discipline of not touching test until the end. Cross-validation: k-fold, stratified, grouped, time-aware. Leakage — the single most common reason a student's model scores 0.99 and then dies — target, temporal, and fitting the scaler before the split. Linear and logistic regression as baselines that must be beaten before anything fancier is allowed. Regularisation: L1 versus L2 and what each does to coefficients. Bias and variance, learning curves, reading an overfit from the train/validation gap. Trees, random forests, then gradient boosting. Pipeline and ColumnTransformer so preprocessing is fitted inside each fold. Metrics: accuracy is useless at a 2% positive rate — precision, recall, PR-AUC versus ROC-AUC, and choosing a threshold from a business cost rather than from 0.5. Probability calibration, and why "the model said 0.8" usually does not mean 80%. Hyperparameter search, and the second-order overfit where you overfit the validation set by searching it too hard.
• Unsupervised learning and time series

  k-means and its unstated assumptions — spherical clusters, comparable variance, k known — and choosing k honestly rather than by squinting at an elbow. Hierarchical clustering, and DBSCAN for non-convex and noisy structure. Why scaling changes the answer. PCA as a rotation: explained variance, loadings, what a component actually means. UMAP and t-SNE for visualisation only — distances and cluster sizes in those plots do not mean what they appear to mean. Time series: trend, seasonality, decomposition, stationarity, autocorrelation. Why a random train/test split on time series is leakage, and backtesting with expanding windows instead. Naive and seasonal-naive baselines, which beat most elaborate models and must be reported alongside them.
• Deep learning with PyTorch

  Tensors and autograd, and writing one training loop by hand before touching any abstraction. nn.Module, optimiser, DataLoader — the shape of every PyTorch script you will ever read. The head-to-head: run a gradient-boosted tree and a neural approach on more than one dataset and report both accuracies, plus training time, inference latency and the hardware each needs. We do not tell you the result in advance; measuring it is the exercise. CNNs and transfer learning — fine-tune a pretrained backbone, never train from scratch on two thousand images. The transformer block, attention, and embeddings as the bridge into the next module. Dropout, augmentation, early stopping, and reading a divergent loss curve. GPU realities on a student budget. Keras 3 as a multi-backend API over JAX, TensorFlow and PyTorch.
• LLMs as a data scientist's tool, not a job title

  Tokens, context windows, temperature, and why the model is fluently and confidently wrong. The API pattern, and structured output against a JSON schema — what makes a language model usable inside a data pipeline at all. LLMs for the boring eighty per cent: classifying free-text survey responses, extracting fields from messy PDFs, normalising inconsistent product and city names. Building a hand-labelled gold set first, so accuracy is a measured number rather than an impression. Embeddings and vector search: cosine similarity, chunking strategy, and why retrieval quality dominates generation quality. One small retrieval system built end to end so the shape is understood. Evaluation: building an eval set, LLM-as-judge and its known biases. When fine-tuning is the answer and when it is not — usually it is not. Cost and latency arithmetic per thousand records. And privacy: customer names, phone numbers and email addresses cannot be pasted into a third-party API.
• Shipping: notebook to something someone else can run

  git, branches, and a repository someone can clone and run. Getting logic out of the notebook into importable modules, with the notebook as a thin presentation layer. Reproducibility: lockfiles, pinned versions, seeds set everywhere that has randomness. Testing data assumptions with pytest over transformation functions, and schema and range checks. Experiment tracking. Model serialisation and the version-mismatch trap — a pickle written under one scikit-learn and loaded under another. A FastAPI prediction endpoint. Streamlit or Gradio for something a stakeholder can click. Docker to the level of "it runs on the other machine". Batch scoring on a schedule, monitoring, and drift — the model decays quietly and nobody files a ticket. And writing the model's limitations down, in the repo, honestly.
• Capstone and interview readiness

  A project on data you sourced or scraped yourself. Framing the problem with a stakeholder before writing code, and agreeing the metric in advance. The written report: decision, method, result, limitations, and what you would do with another month. A ten-minute defence with hostile questions. A portfolio README that explains why the choices were made rather than what the code does. Whiteboard practice on window functions and statistics fundamentals. Case questions — "this metric dropped 8% last Tuesday, what do you check?" And explaining a gradient boosting model to a manager who does not want to hear the word gradient.

If your target is publishing business reports rather than building models, that toolchain is covered properly on the Power BI course and the Advanced Excel course — we do not teach them here, because covering them alongside this syllabus would mean teaching all of them badly.

Plainly

What this course will not do

Every other result you clicked on today led with a promise. Here is the other half of the conversation, which you would otherwise only get after paying.

• It will not get you a job on its own.

  We help — CV review, mock interviews, practice defences, and an honest opinion on whether a role is worth applying for. That is placement assistance. It is not a placement guarantee, and anybody offering you one is selling you something.

• It will not make you an AI engineer.

  You will learn to use large language models on messy text and, more importantly, to measure whether what they gave you is right. Building production retrieval systems, agents and fine-tuning pipelines is a different job, with a different title and a different hiring bar. Four weeks does not get you there, and we would rather say so than print “with Generative AI” on the brochure.

• It will not work if you have never written code.

  Module 2 moves at a pace that assumes you can already write a loop, a function and an if-statement. Admitting absolute beginners into this syllabus produces dropouts, not graduates.

• It will not teach you Power BI, Tableau or Advanced Excel properly.

  Those are real skills and we teach them — on their own courses, where they get the time they need. Trying to cover them here would mean teaching all of them badly.

• It will not be finished in six weeks.

  Ten modules, run as a regular batch rather than a weekend or crash course, because this material does not compress. Ask us for the length of the batch you would be joining — and if a shorter course is genuinely what you need, say so when you call and we will tell you which one.

• It will not give you a degree.

  We are a training institute, not a university. This is a certificate from a private institute — no private institute in India can award you a degree, and you should be wary of any that implies it can.

• There is no countdown on this page.

  No expiring discount, no “65% off, ends Friday”. Ask us what the course costs and you will be told the same number next week.

Questions

The things people actually ask