Data Science Stack Development

Build End-to-End Data Science & Analytics Solutions

Learn Python, data engineering, machine learning, big data and MLOps to build complete data products – from raw data ingestion to deployed, production-ready models and dashboards.

View Course Roadmap Explore Tech Stack

32 Weeks Structured Learning

6+ Major DS Projects

End-to-End Data to Deployment

Why Data Science Stack?

Modern organisations need professionals who can handle the full data lifecycle: ingestion, cleaning, modelling, big data processing and deployment. This program turns you into an end-to-end data professional, not just a model builder.

Python, Pandas & analytical thinking
Machine Learning & Deep Learning foundations
Big Data with Hadoop / Spark
MLOps, APIs & cloud deployment

Course Roadmap

A structured journey from Python basics to big data, machine learning, data engineering and production deployments.

Phase 1 · Python & Data Fundamentals
Weeks 1–4

Python for data: syntax, data types, functions & OOP
NumPy & Pandas for data manipulation
Working with CSV, JSON, APIs & databases
Exploratory data analysis & basic statistics

Phase 2 · Data Cleaning & Visualisation
Weeks 5–7

Handling missing values, outliers & data quality issues
Feature engineering & transformations
Visualisation with Matplotlib, Seaborn & Plotly
Storytelling with charts & dashboards

Phase 3 · Machine Learning Fundamentals
Weeks 8–12

Supervised ML: regression & classification algorithms
Unsupervised ML: clustering & dimensionality reduction
Model evaluation, cross-validation & hyper-parameter tuning
ML pipelines using Scikit-learn

Phase 4 · Deep Learning & Advanced ML
Weeks 13–16

Neural networks & optimisation basics
TensorFlow / Keras for structured & image data
Intro to NLP & time-series forecasting
Model regularisation & performance tuning

Phase 5 · Data Engineering Basics
Weeks 17–20

Relational databases & SQL for analytics
Data pipelines & ETL concepts
Workflow orchestration with Airflow
Data warehousing fundamentals

Phase 6 · Big Data & Distributed Processing
Weeks 21–24

Big Data concepts, Hadoop ecosystem overview
PySpark for large-scale data processing
Batch vs streaming data, Kafka basics
Building scalable analytics pipelines

Phase 7 · BI, Dashboards & Analytics Apps
Weeks 25–26

Designing KPIs & analytical dashboards
Plotly Dash / Streamlit for data apps
Integrating ML models into dashboards
Presenting insights to business stakeholders

Phase 8 · MLOps & Deployment
Weeks 27–29

Packaging models with FastAPI / Flask
Containerisation with Docker
CI/CD for ML systems, versioning & monitoring
Deploying models on cloud platforms

Phase 9 · Capstone Projects
Weeks 30–32

Customer churn prediction pipeline with deployment
Big Data analytics project using PySpark / Hadoop
End-to-end DS project with dashboard & API
Interview-ready portfolio preparation

Technology Stack You’ll Master

A complete data science stack – from Python analytics to big data and production deployment.

Core Data Science

Languages & Libraries:

Python, NumPy, Pandas, Scikit-learn, TensorFlow / Keras, statsmodels and common ML utilities.

Visualisation & Analytics

Tools:

Matplotlib, Seaborn, Plotly, Dash / Streamlit, Jupyter Notebooks and data storytelling patterns.

Data Engineering & Big Data

Platforms:

SQL & PostgreSQL, data warehousing, Hadoop ecosystem, PySpark, Kafka and ETL pipelines.

MLOps & Deployment

Production Tools:

FastAPI / Flask, Docker, Git, CI/CD pipelines, basic Kubernetes, logging & monitoring.

Cloud & Infrastructure

Cloud Platforms:

AWS / Azure / GCP fundamentals for storage, compute, databases and managed ML / data services.

Collaboration & Workflow

Dev Practices:

GitHub, code review, notebooks to scripts, documentation, experiment tracking & reproducibility.

Career Opportunities & Industries

Move into high-impact data roles with a portfolio of real, deployed projects.

Roles You Can Target

Data Scientist
Machine Learning Engineer
Data Engineer
Business / Data Analyst
Analytics Engineer
MLOps Engineer
Decision Science Analyst

Industries Hiring Data Talent

BFSI & FinTech
E-commerce & Retail Analytics
Healthcare & Pharma
Telecom & Customer Analytics
Manufacturing & IoT Analytics
EdTech & SaaS products
Consulting & Market Research

Course Details & Outcomes

Everything you need to know before joining the Data Science Stack Development program.

Course Duration & Format

Duration: 32 weeks (8 months)
Batch Options: Weekday (Mon–Fri, 2–3 hrs/day) or Weekend (Sat–Sun, 5–6 hrs/day)
Mode: Classroom, Online Live, Hybrid & Recorded access

Prerequisites

Basic Python or any programming experience
Comfort with high-school mathematics
Interest in data, numbers & problem solving
No prior ML / big data experience required

Certification

Airocode Data Science Stack Developer Certificate
Project completion certificates for all major projects
Skills report highlighting tools & competencies
Portfolio of deployed data science solutions

Placement Support

1:1 career mentoring & resume optimisation
DS / ML interview preparation & mock interviews
Case studies, SQL & problem-solving practice
Referrals to hiring partners and startups
Support for analytics, DS & data engineering roles