SQL100: How data lives in tables, why databases beat raw files, and how keys link info.

SQL200: Filter and sort the right rows; use simple rules and labels to clean results.

SQL300: Use subqueries to answer deeper questions; summarize correctly with groups.

SQL400: Combine tables the right way (inner/left/full); avoid duplicate-row surprises.

SQL500: Make queries faster with indexes/partitions; read an EXPLAIN like a pro.

SQL600: Create/update tables safely; understand Spark tables, Delta Lake, and lakehouses.

PY100: Write and run simple programs; understand basic types and input/output.

PY200: Work with tables in Python (Pandas); select, filter, and summarize like SQL.

PY300: Use if/else and loops; write clean, short code with list/dict tricks.

PY400: Make clear charts that tell a story; compare groups and trends.

PY500: Bundle logic into functions; use small helpers and handle errors nicely.

PY600: Read/write files, parse JSON, call simple web APIs, and scrape pages politely.

PY700: Think in objects (small re‑usable parts) to keep code organized.

PY800: Connect Python to databases, run queries, and save results safely.

PY900: Engineer features with Pandas; windows, categories; intro to PySpark for scale.

PY1000: Build a simple regression in Python and check if the story makes sense.

STATS100: Averages and spread in plain language; spot skew and tell the ‘shape’ of data.

STATS200: What ‘chance’ means; common patterns; compare groups fairly.

STATS300: When is a change real? Intro to tests, p‑values, CLT, and independence checks.

STATS400: Correlation vs causation; simple regression and common misreads to avoid.

STATS500: p‑values in context; time‑series basics; what ‘panel/mixed models’ roughly mean.

STATS600: Compare many groups (ANOVA), non‑parametric tests, and plan fair experiments.

EXL100: Pivot tables, lookups, and charts that highlight the ‘so what’ without jargon.

BA100: What a Business Analyst does; funnels, metrics, anomalies, and safe A/B basics.

BA200: Map customer journeys, estimate LTV, and understand marketing terms that matter.

BA300: Pick a North‑Star metric, run multivariate tests, and avoid peeking mistakes.

BA400: Forecast run‑rate, use simple time‑series, and spot unusual behavior in numbers.

DS100: What a data scientist actually does day‑to‑day; how teams work together.

DS200: Keep repos clean, use environments, and run short sprints with clarity.

MLOPS101: Pipelines that move data reliably; quick UIs to show results.

MLOPS200: Cloud basics for ML (compute, storage, IAM) and cost awareness.

MLOPS300: Package models in Docker, serve via FastAPI, add health checks.

MLOPS400: Track experiments (MLflow), promote versions, and keep history tidy.

MLM100: Gentle intro to functions, gradients, and why models ‘climb’ to better answers.

MLM200: How iterative methods improve; cost functions and smarter gradient steps.

MLM300: Matrices and vectors explained; distances and rotations with pictures.

MLM400: The simple maths behind decision trees (entropy, splits) and friends.

ML100: Regularization and gradient descent in practice—make models behave.

ML200: Split data the right way; pick fair metrics and handle class imbalance.

ML300: Group things without labels using K‑Means and DBSCAN; check cluster quality.

ML400: Reduce dimensions (PCA) and fix common linear regression issues.

ML500: Teach yes/no decisions with Logistic Regression and Decision Trees.

ML600: Boost and bag for better accuracy; Random Forests without math overload.

ML700: SVM and k‑NN in plain terms; explain results with SHAP/LIME pictures.

ML800: Ship a mini ML app with Flask/Streamlit and basic monitoring.

ML900: Tune hyperparameters and cross‑validate so results are trustworthy.

ML1000: Capstone: end‑to‑end churn project with demo and documentation.

DL100: Neural networks explained simply: layers, activations, and training basics.

DL200: How learning flows backward (backprop) and how to keep it stable.

DL300: Pick the right loss and optimizer; tips to avoid over/under‑fitting.

DL400: Train with Keras/TensorFlow/PyTorch; save checkpoints like a pro.

DL500: CNNs for images: what ‘convolution’ does and how to regularize.

DL600: Stand on giants: fine‑tune pretrained models safely and quickly.

DL700: Work with sequences: LSTM/GRU to understand text/time order.

DL800: Autoencoders & embeddings; a small image/text mini‑project.

DL900: Modern CNN families (ResNet/Inception) and simple ways to ‘explain’ them.

DL1000: Capstone: end‑to‑end deep learning project with a live demo.

AI100: Fix and understand images with OpenCV; intro to multimodal ideas.

AI200: Detect and classify objects; how accuracy is measured in practice.

AI300: Text understanding (intent/NER) and how text‑to‑speech works at a high level.

AI400: Embeddings and BERT in simple words; fine‑tune a small model.

AI500: Sentiment analysis project from baseline to modern models.

AI600: Hugging Face & open LLMs; light‑weight fine‑tuning and serving choices.

AI700: RAG: search + AI answers that show sources and stay grounded.

AI800: Agentic AI with code: tools, memory, teamwork, and guardrails.

AI900: Agentic AI without code: n8n/Zapier/UiPath flows with approvals.

AI1000: Grand capstone: finalize, evaluate, document, and present.

SS100: Communicate clearly, present work well, and craft impact‑first resumes.

SS200: Plan capstones, form teams, set milestones, and get CI running.

SS300: Mock interview for Business Analyst: SQL/Excel/stats/product, with feedback.

SS400: Mock interview for Data Scientist: coding, ML/stats, SQL, design, product.

SS500: Mock interview for ML/AI Engineer: coding, RAG/serving, MLOps, debugging.

SS600: Interview prep + salary negotiations with scripts and examples.

SS700: Buffer session: Q&A, doubt‑clearing, recap, readiness plans.

SS800: Cohort wrap‑up, community, next steps, and graduation checklist.