Academix University

Masters in Data Science
Master’s in Data Science | Academix University

Master’s in Data Science

Program highlights

  • 12-month practical online degree — built for working professionals
  • Project-first curriculum with a final capstone
  • No entrance test for eligible applicants
  • Industry mentors and live supervised labs
  • Cloud & big-data tool training (AWS / Spark / SQL)
  • Portfolio-ready projects for job applications

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About the program

Academix University’s Master’s in Data Science teaches you to turn raw data into decisions. The course blends statistics, programming and applied machine learning with practical labs and a supervised capstone to build a job-ready portfolio.

Delivered fully online over 12 months, the program focuses on hands-on experience: data cleaning, modelling, model evaluation, deep learning basics, NLP and deploying models to production environments.

Program objectives

Practical skills

Apply statistical and ML techniques to real business problems using Python and standard libraries.

Communicate insights

Present analysis and dashboards so stakeholders can act on the results.

Toolchain mastery

Work with SQL, Spark, cloud platforms and ML frameworks for scalable solutions.

Portfolio & placement

Finish with tangible projects and guidance to approach employers or freelance clients.

This program combines live instruction, recorded materials and hands-on projects. Labs and mentor sessions ensure you can build, test and deploy models.

Program details

  • Eligibility: Bachelor's degree (any discipline) — bridging modules available
  • Duration: 12 Months
  • Enrollment: Online — rolling intakes

Why choose Academix?

Industry-aligned curriculum

Modules built around real-world problems and contemporary toolchains employers expect.

Mentors from industry

Guidance from practitioners who’ve shipped analytics and ML at scale.

Fully online

Attend live sessions or catch recordings — no residency needed.

Project-first learning

Build multiple hands-on projects culminating in an industry-relevant capstone.

No entrance test

Direct admission routes for qualified applicants; admissions focus on readiness and background.

Credit recognition

Prior coursework may be considered for transfer credit — contact admissions to check eligibility.

Program structure — Master’s in Data Science

Semester 1: Foundations

Introduction to Data Science
  • Overview, data lifecycle, ethics
  • Intro to Python, R, SQL
Programming & Data Wrangling
  • Data cleaning, Pandas, feature engineering
Mathematics & Probability
  • Linear algebra, calculus essentials, basic stats

Semester 2: Core ML & Visualization

Supervised Learning
  • Regression, classification, tree-based models, evaluation
Unsupervised Learning
  • Clustering, dimensionality reduction, anomaly detection
Data Visualization
  • Dashboards, storytelling with Plotly/Streamlit

Semester 3: Advanced Topics

Deep Learning
  • CNNs, RNNs, transfer learning basics
NLP & Transformers
  • Text processing, embeddings, introductory transformers
Big Data & Cloud
  • Spark, distributed processing, cloud basics

Semester 4: Capstone

Capstone Project
  • End-to-end project with final report and presentation

Electives (examples)

Computer Vision
  • Image models, detection, segmentation basics
AI for Healthcare
  • Healthcare analytics use-cases and privacy considerations

Enroll now — next intake open