Master in Data Science

Advance your career with our comprehensive 12-month Master in Data Science program, certified by Universidad Católica de Murcia (UCAM). This rigorous program prepares you with in-depth knowledge in Python programming, data analytics, machine learning algorithms, and AI applications through 200 hours of live instructor-led training and hands-on industry projects.

Globally Recognized & Accredited:

15000

Students Trained Globally

25

Industry-Aligned Programs

70

Countries with Active Alumni

87

Career Transition Success

  • Industry-Standard Technology Stack

Tools & Technologies That Power Success

Master the complete data science ecosystem with hands-on experience using industry-leading tools and frameworks. Our Advanced Program in Data Science equips you with practical skills in Python programming, data manipulation, visualization, and machine learning – preparing you for real-world data challenges.

  • Production-Ready Skills
  • Industry-Relevant Tools
  • Hands-On Learning

Python

MySQL

learn anaconda

Anaconda

Jupyter Hub

Pandas

NumPy

Seaborn

Matplotlib

Excel

GIT

HTML

CSS

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  • Industry-Standard Technology Stack

Tools & Technologies That Power Success

Master the complete data science ecosystem with hands-on experience using industry-leading tools and frameworks. Our Master in Data Science program equips you with practical expertise in Python programming, statistical analysis, data visualization, and machine learning libraries – preparing you for real-world data science challenges.

  • Production-Ready Skills
  • Industry-Relevant Tools
  • Hands-On Learning

Python

Keras

TensorFlow

Scikit-learn

Pandas

NumPy

Seaborn

Matplotlib

Excel

Open CV

Power Bi

Spacy

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Universidad Católica de Murcia, Masters Degree

Universidad Católica de Murcia (UCAM), founded in 1996, is a fully-accredited European University based out of Murcia, Spain. With learning centres in the Middle East and Southeast Asia, UCAM aims to provide students with the knowledge and skills to serve society and contribute to the further expansion of human knowledge through research and development. The university offers various courses, including 30 official bachelor’s degrees, 30 master’s degrees and ten technical higher education qualifications through its Higher Vocational Training Institute, in addition to its in-house qualifications and language courses. The programmes offered are distinguished in Europe and worldwide, with good graduate employability prospects as well. UCAM is accredited by ANECA (National Agency for Quality Assessment and Accreditation of Spain) and the Ministry of Education regarding 17 of its undergraduate degrees.

Eligibility

Eligibility

This master’s program is designed for graduates and working professionals seeking to advance their careers in data science. Candidates must hold a bachelor’s degree from a recognized university, demonstrate proficiency in English language, and possess at least 2 years of professional experience in any field to qualify for this comprehensive data science program.

Prerequisites

Strong foundation in mathematics and basic programming concepts is recommended for success in this program. Knowledge of linear algebra, probability, and calculus will be advantageous due to the program’s focus on machine learning algorithms and statistical modeling techniques.

Course Modules

Module 1
Working with data
Module 2
Data Analytics in Business
Module 3
Data Mining techniques
Module 4
Algorithms
Specialization Module 1
Data Modelling
Specialization Module 2
Application of Data in Ai and Blockchain
Research
Research Methods & Dissertation
Foundations of Data Analysis and Preparation

This module builds a strong foundation for working with data by introducing essential techniques for data cleaning, manipulation, and analysis. Learn the key programming languages, tools, and frameworks essential for any data scientist, with a focus on practical implementation.

Exploratory Data Analysis and Visualisation

Gain the ability to summarise, transform, and visualise data using Python and its popular libraries. Develop fluency in handling variable types, names, and values—while mastering operations with dates, strings, and advanced charting methods like histograms, scatter plots, and boxplots.

Hands-on Data Research Skills

Through exploratory data analysis, learners will enhance their ability to generate insights from datasets, apply statistical concepts, and chain multiple tidying operations using the pipe operator. These skills will empower learners to approach real-world data challenges confidently.

Learning Outcomes
Module Duration: 4–6 weeks

By completing this module, you will:

  • Analyse datasets using visualisation, counting, and summary tools
  • Acquire foundational skills in variable manipulation and data formatting
  • Learn how to chain multiple data tidying operations using the pipe operator
  • Work effectively with different data types, including dates and strings
  • Apply core concepts of statistics, linear algebra, and Python in real scenarios
  • Master widely-used Python libraries such as Pandas, Matplotlib, and Seaborn
Excel-Based Analytical Modelling

Learn to build robust spreadsheet models by translating business scenarios into structured mathematical representations. Understand core Excel functions, auditing techniques, and the use of What-If analysis for model reliability and prediction.

Decision Analysis and Analytical Reasoning

Explore tools like payoff tables, utility theory, and decision trees to structure decision-making under uncertainty. Learn to compute probabilities and apply both predictive and prescriptive modelling techniques to solve real-world business challenges.

Business Intelligence with Microsoft Power BI

Gain hands-on experience with Power BI to manage data, perform advanced visualizations, and apply analytical expressions (DAX). Learn how to transform raw data into meaningful dashboards and actionable insights for strategic decision-making.

Learning Outcomes
Module Duration: 4–6 weeks

By completing this module, you will:

  • Critically analyze business data in the context of strategic decision-making
  • Understand how business analytics supports core management functions
  • Retrieve, organize, and manipulate datasets using spreadsheet models
  • Apply statistical analysis and data visualization for data-driven decisions
  • Use Power BI to build interactive dashboards and derive insights from data
  • Incorporate predictive models and structured decision tools into business processes
Foundations of Data Mining & Pattern Discovery

Gain essential skills in extracting meaningful insights from massive datasets using data mining techniques. Learn to identify patterns, correlations, and trends that drive better business decisions and strategic outcomes.

Hands-On Data Processing with Python

Explore practical data integration, cleansing, and transformation using Python libraries like NumPy and matrix-based operations. Understand how to represent complex datasets through tables, graphs, and text formats for effective analysis.

Text Mining, Pattern Recognition & Evaluation

Dive deep into the world of text mining, frequent subgraph mining, and classification. Use tools like Power Query Editor for advanced filtering and evaluate risks, sensitivity, and outcomes through modern data modeling techniques.

Learning Outcomes
Module Duration: 4–6 weeks

By completing this module, you will:

  • Understand the core principles of data and text mining, including key patterns and knowledge extraction methods
  • Learn how to preprocess and transform data for mining using Python and structured databases
  • Apply data mining models to real datasets for classification, prediction, and forecasting
  • Discover scalable methods for extracting sequential, associative, and subgraph patterns
  • Engage in practical data modeling, evaluation, and deployment processes
  • Analyze risk and sensitivity using predictive metrics and intelligent data filters
Predictive Modeling and Algorithmic Intelligence

Dive deep into the foundational algorithms used across data science—focusing on their role in building powerful predictive models. Understand the principles behind classification, regression, and ensemble learning, and how they are applied when conventional techniques fall short.

Model Evaluation and Practical Implementation

Learn how to structure data through splitting techniques like holdout and k-fold cross-validation. Evaluate performance with categorical and continuous outcomes, and gain hands-on knowledge in managing class imbalances—an essential skill for real-world datasets.

From Theory to Industry-Level Solutions

Explore the mechanisms, strengths, and applications of various algorithms, from logistic regression to neural networks. Understand how to use the right model for the right challenge, whether it’s SVMs for margin-based classification or ensemble models for boosting accuracy.

Learning Outcomes
Module Duration: 4–6 weeks

By completing this module, you will:

  • Understand and apply key algorithmic concepts such as divide-and-conquer, sorting, searching, and dynamic programming
  • Evaluate and select appropriate data structures to enhance computational efficiency in data science workflows
  • Design and assess predictive models using logistic regression, k-NN, SVMs, neural networks, and Bayesian methods
  • Implement strategies to handle imbalanced datasets and choose appropriate validation techniques
  • Gain proficiency in building classification and regression trees and applying ensemble learning for performance enhancement
  • Translate algorithmic understanding into real-world data science solutions
Predictive Modelling with Regression Techniques

Master foundational and advanced regression techniques including linear, logistic, and multiple regression. Understand how to model real-world relationships and use statistical inference to derive actionable insights.

Applied Statistical Techniques for Real-World Scenarios

Explore generalised linear and additive models to capture complex interactions in data. Use these methods to model experimental designs and discover meaningful relationships between variables.

Data Interpretation and Visualisation with Tableau

Bridge the gap between raw data and decision-making using Tableau. Learn to shape, transform, and visually present your statistical models for clear and intuitive communication.

Learning Outcomes
Module Duration: 4–6 weeks

By completing this module, you will:

  • Differentiate between predictive models and master the concepts of linear regression
  • Understand model algorithms and apply them to real-world datasets
  • Analyse outputs from logistic regression and understand when to use discriminant analysis
  • Evaluate models for accuracy and interpret results in a meaningful, structured way
  • Use Tableau to create and interpret data models, reports, and visual narratives
AI in Action: From Theory to Real-World Impact

Understand how artificial intelligence transforms modern business with intelligent decision-making systems, automation, and predictive insights. Learn the foundational tools behind AI, including neural networks, deep learning, and natural language processing.

Unlocking the Power of IoT and Blockchain

Dive into how IoT and Blockchain are reshaping industries—from smart factories to secure, decentralized systems. Gain knowledge on sensors, actuators, protocols, and real-world IoT implementations. Explore blockchain architecture, smart contracts, and Hyperledger to understand secure data exchanges.

Integrating Intelligent Systems for the Future

Build AI and ML applications supported by blockchain technology. Learn to design intelligent business models using distributed ledgers and digital logic to meet modern enterprise needs with transparency, scalability, and automation.

Learning Outcomes
Module Duration: 4–6 weeks

By completing this module, you will:

  • Grasp the fundamentals of AI and its business applications, including AI-driven decision-making
  • Develop AI implementation strategies tailored to business environments
  • Understand the structure, purpose, and potential of Blockchain and Distributed Ledger Technologies
  • Explore Hyperledger, smart contracts, and their integration into real-world business scenarios
  • Discover the role of IoT in modern operations through hands-on sensor, actuator, and protocol knowledge
  • Learn to build intelligent, future-ready solutions using AI, IoT, and Blockchain technologies
Research-Driven Approach to Data Science

Dive deep into the core of independent research practices in data science. This module equips learners to craft research or design questions, relate them to existing knowledge, and carry out rigorous investigations that demonstrate analytical depth and academic maturity.

From Concept to Execution

Explore modern methods in data science—ranging from probability, inference, and modelling to data visualization, mining, and regression. Understand how to leverage these tools for real-world organizational challenges and innovations.

Capstone Project for Real-World Impact

Learners will undertake a data science project or dissertation that showcases their cumulative knowledge. This final project serves as a portfolio piece that reflects competence in analytics, decision-making, and change implementation within a business context.

Learning Outcomes
Module Duration: 4–6 weeks

By completing this module, you will:

  • Conduct independent research or development projects within a data science framework
  • Apply analytical and machine learning techniques to solve practical problems
  • Present technical findings clearly to both expert and non-specialist audiences
  • Develop detailed professional documentation for data projects
  • Evaluate outcomes based on current academic research and industry standards
  • Produce a final data product that can be showcased to employers or academic institutions

Your Success Story Starts Here

Every image here tells a story of transformation, dedication, and success. Be the next to wear the cap and gown. Enroll today, and let your journey begin.

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Student Reviews

Every student has a story—of ambition, of challenge, of growth. In their own words, they share how Airtics became a turning point in their learning journey and helped them move closer to their goals.

Mohammed Ajmal

Airtics offers a truly transformative learning experience. The course content is up-to-date, the mentors are incredibly supportive, and the flexibility of the online platform made it easy to balance with my work. Highly recommended for anyone looking to upskill!

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Mohammed Ajmal

Airtics offers a truly transformative learning experience. The course content is up-to-date, the mentors are incredibly supportive, and the flexibility of the online platform made it easy to balance with my work. Highly recommended for anyone looking to upskill!

  • Verified Review

Mohammed Ajmal

Airtics offers a truly transformative learning experience. The course content is up-to-date, the mentors are incredibly supportive, and the flexibility of the online platform made it easy to balance with my work. Highly recommended for anyone looking to upskill!

  • Verified Review

Mohammed Ajmal

Airtics offers a truly transformative learning experience. The course content is up-to-date, the mentors are incredibly supportive, and the flexibility of the online platform made it easy to balance with my work. Highly recommended for anyone looking to upskill!

  • Verified Review

Real-World Capstone Projects

Apply your data science skills to solve real business challenges through hands-on projects. Choose from our curated capstone projects or bring your own organizational problem to create a portfolio that showcases your expertise to future employers.

 

House Rental Predication

Build a machine learning model to predict house rental prices using market data, property features, and location analysis.
 

Image Classification

Develop a computer vision system using deep learning to automatically classify and categorize images.
 

Business Insights Reporting

Create interactive business dashboards that transform raw data into actionable insights and visual reports.

Learn from Industry Leaders & Experts

Learn from the best in the field. Our faculty combines academic brilliance with industry expertise, featuring PhD holders, senior data scientists, and AI researchers from top organizations.

Ms. PIYALI MONDAL - B&W
Ms. Piyali Mondal Head of Department
Ms. Priti Mondal - B&W
Ms. Priti Mondal Associate Faculty
Dr. Abdullah El Nokiti - B&W
Dr. Abdullah El-Nokiti Professor
Dr. Meraj Inamdar - B&W
Dr. Mohd Merajuddin Inamdar Professor
Dr. Madhavi Vaidya - B&W
Dr. Madhavi Vaidya Professor
Dr. Poonam Chaudhari - B&W
Dr. Poonam Chaudhari Professor
Dr. Anup Maurya - B&W
Dr. Anup Kumar Maurya Professor
Dr. Milan Joshi - B&W
Dr. Milan Amrutkumar Joshi Industry Expert
Dr. Mohamed Elhaw - B&W
Dr. Mohamed Elhaw Industry Expert
Dr. Pradeep Tiwari - B&W
Dr. Pradeep Tiwari Industry Expert
Global Student Community

Students from 60+ Countries Worldwide

Frequently Asked Questions

Find answers to common questions about our Master in Data Science program. Learn about program details, admission requirements, and what to expect from this 12-month master’s degree course.
Why should I join the Data Science program?
Data science isn’t just the way of the future, it’s the way of right now! It is being adopted in all sorts of industries, from health care to route planning, marketing & sales to banking industries and beyond. Even industries such as retail that you might not associate with big data are getting on board. Data science is the fuel of the 21st Century.
What if I fail to attend the classes?
We provide you with live recorded classes of the same session to follow up if you end up missing the same.
How does Data Science differ from Big Data and Data Analytics?
Each of these technologies complements one another yet can be used as a separate entity. Big Data refers to any large and complex acquisition of data. Extracting meaningful information from data is why Data Analytics is used for. While Data Science is a multidisciplinary field that aims to produce broader insights.
What can I expect from the Data Science program?

Accelerated data science career guidance with world-class training on the most in-demand data science and machine learning skills. Training and hands-on experience with key tools and technologies including Python, PowerBi, and concepts of Machine Learning.

Who can join the program?
Aspirants and professionals who are having basic computer programming skills can enroll for the program.
Do I need prior experience in coding to learn the program?
Basic knowledge of programming logic and technology exposure will be helpful.
What is TensorFlow?
TensorFlow is an end-to-end open-source platform for Machine Learning (ML). It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
Can a data analyst become a data scientist?
Yes. Many data analysts go on to become data scientists after gaining experience, advancing their programming and mathematical skills, and earning an advanced degree.
Should I study Data Analytics or Data Science?
Which you choose is largely a matter of preference. If you’re mathematically minded and enjoy the technical aspects of coding and modelling, a data science degree could be a good fit. On the other hand, if you love working with numbers, communicating your insights, and influencing business decisions, consider a degree in data analytics.
Is Machine Learning a good career?
Yes. The average base pay for a machine learning engineer in the US is $123,608, as of April 2022. According to a December 2020 study by Burning Glass, demand for AI and machine learning skills is projected to grow by 71 per cent over the next five years.

Still have questions?

If you have any other questions or need further information about our Master in Data Science program, don’t hesitate to contact us. Our admissions team is here to help you take the next step in your data science career.
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