Master in Data Science
We know the amount of data produced globally on a daily basis is 2.5 quintillion bytes which is enormous and is expected to only keep on increasing as the world’s population gets more access to internet. The data is now considered as a commodity that is more valuable than oil and buried in these data are answers to countless questions. Data science helps to deal with these vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. When the opportunity in data science comes knocking at your door, will you be ready?

Monthly Recurring Batch

Program Duration

12 Months

Learning Format

Blended Learning

Program Fees


Course Overview

The Master in Data Science program aims to prepare students with a well-rounded education in different areas of Data Science to prepare themselves to become successful data scientists. The curriculum is made up of 4 core modules and 2 specialization modules providing in-depth knowledge in the field of Data science and its industrial application. Students will learn how to work with data to solve complex problems. Topics covered include Python, Data Science Algorithms, Data Analytics in Business & Data Mining. In the master’s program students will also learn two specialization modules in (I) Statistical Data Modelling and (II) Applications of Data in Artificial Intelligence & Block Chain. At the end of course modules, all students are required to undertake a capstone project in Data Science to have hands-on working experience by solving real-world problems.

Training Key Features

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    What you will learn

    About UCAM

    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.

    Airtics Education’s Masters Program is certified by UCAM University.

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    Skills Covered

    Python for data science

    Data analysis

    Data mining

    Data Analytics in Business

    Algorithms in Data Science

    Data in AI & Blockchain

    Who Can Apply for the Course?

    Tools/ Frameworks/ Libraries

    Scripting tools

    Pandas, numPy, seaborn, matplotlib, cufflinks, scikit, NLTK, CoreNLP, spaCy, PyNLP, Tensorflow, Keras, Open CV, Power BI, Excel

    IDE shell

    Application And Use Cases

    Traffic Management
    Data Science can identify cause of congestion & manage traffic effectively
    Road Safety
    Data Science can help us identify accident hotspots & recommend safety measures
    Data Science can gain behaviour patterns & provide recommendations to customers
    Data Science can handle customer data, detect fraud, manage credit risk in allotting loans
    Marketing & Sales
    Data Science helps businesses to market & sell product to the right audience.
    Health Care
    Data Science is used for drug discovery, predict anomalies, monitor patient health
    Data Science can be used to predict future happenings by analysing historical data.
    Data Science can automate large scale processes & speed up implementation time
    Data Science can help demand forecasting, pricing decisions & optimise product placement


    • Bachelor’s Degree from a recognized University
    • Proficiency in English language


    Due to its involvement in modern Machine Learning algorithms with maths and programming, candidates having knowledge of linear algebra, probability and calculus could be a plus.

    Course Modules

    This module inculcates practical understanding and a framework that allows the execution of essential analytics actions such as extracting, cleaning, changing, and analysing data. In this module, learners grasp the knowledge of programming languages, tools, frameworks, and libraries utilised throughout the course to acquire and model data sets. Data analysis is accomplished through visualising, summarising, and developing rudimentary data handling abilities by paying attention to variable types, names, and values. In addition, managing data using dates, strings, and other elements, enhances learners’ abilities to perform data research and generate visualisations.
    • Analyse information using data visualisation, summary, and counting tools.
    • Acquire rudimentary skills in data handling, focusing on variable types, names, and values.
    • To learn how to use the pipe operator to combine numerous tidying operations in a chain.
    • The ability to work with data that includes dates, strings, and other variable
    Content Covered
    • Data Cleaning Techniques
    •  Data Preprocessing
    •  Data Manipulation
    •  Core Python Programming
    •  Data Visualisation using Matplotlib
    •  Linear Algebra
    •  Statistics and Probability
    •  Exploratory data analysis
    •  Variance, Standard Deviation, Median
    •  Bar charts and Line charts
    •  Python libraries and framework in data analysis
    •  2D Scatter Plot
    •  3D Scatter plot
    •  Pair plots
    •  Univariate, Bivariate, and Multivariate
    •  Histograms
    •  Boxplot
    •  IQR (InterQuartile Range)
    •  Data analysis with Pandas
    This module addresses the principles of creating reliable spreadsheet models, translating conceptual models into mathematical models, and applying them in spreadsheets. It also demonstrates a knowledge of three analytic tools in Excel, Excel functions, and the process of auditing spreadsheet models to assure accuracy. Additionally covered in this module are Decision analysis, Payoff Tables, and Decision Trees. Microsoft Power BI helps users derive practical knowledge from data to solve business concerns, bringing analytical models to corporate decision-making. Learners acquire insight into advanced analytic features of Power BI, such as prediction, data visualisations, and data analysis expressions.
    • Critically analyse the use of business data in an organisational decision-making context.
    • Demonstrate a critical understanding of business analytics principles in management functions.
    • Apply appropriate data management and analysis techniques to retrieve, organise and manipulate data.
    • Apply appropriate statistical data analysis methods and visualisation techniques to make sound business decisions.
    Content Covered
    •  Creating Spreadsheet models
    •  What-If analysis
    •  Functions for modelling
    •  Auditing Spreadsheet models
    •  Predictive and Prescriptive Spreadsheet models
    •  Problem Identification
    • Decision Analysis
    •  Decision Analysis with or without Probabilities
    •  Computing Branch Probabilities
    •  Utility Theory
    •  Data streaming in Power BI
    •  Visualisation in Power BI
    •  Data Analysis expressions
    •  Report Views in PowerBI
    •  Data Sorting
    •  Data Transformation
    The data mining process includes collecting necessary information from enormous databases that help make a knowledgeable decision. The module demonstrates data mining techniques like data processing, pattern discovery, and trends in information. These methods are employed to obtain the skills and abilities for applying data integration, cleansing, selection, and transformation on tables and graphs for knowledge discovery. Python matrix libraries allow learners to construct some realistic representation of text mining by executing tasks such as classification, estimation, segmentation, forecasting, sequence, and data association.
    • Understand the fundamentals of text mining and analysis, including identifying exciting patterns, extracting helpful knowledge, and supporting decision-making.
    • Explore fundamental principles of text mining and essential algorithms and some of their practical applications.
    • Be able to apply the learned knowledge and skills to implement scalable pattern discovery techniques on large volumes of transactional data
    • Engaging in meaningful discussions about pattern evaluation metrics and investigating techniques for mining various patterns, including sequential and sub-graph patterns.
    Content Covered
    •  Introduction to Data mining
    •  Data Mining in a Python-based environment
    •  What is a data warehouse
    •  How to find patterns?
    •  Affinity Analysis
    •  Product rесоmmendаtіоn
    •  Introduction to Database Mining
    •  Databases and SQL
    •  DDL, DML, Joins, and Schemas
    •  How to use Python Matrix Libraries on Datasets.
    •  Lоаd the Dataset with NumPy
    •  Mining-friendly data representations
    •  Text Representation for Data Mining.
    •  Why is text complex?
    •  Text mining
    •  Data Modelling, Evaluation, and Deployment in Text Mining
    •  Exemplary techniques: Bag of words representation in Text Mining
    •  Frequent Subgraph Mining
    • Data Filtering
    • Power Query Editor
    •  Risk Analysis
    •  Sensitivity Analysis
    This module provides extensive knowledge of splitting data into training, validating, and creating test sets. Develop and assess predictive mining models by integrating a framework and practical perception. There are numerous performance metrics for estimation and categorization systems presented. The most prevalent predictive modelling approaches, including artificial neural networks, support vector machines, k-nearest neighbour, Bayesian learning, ensemble models, and different decision trees, are reviewed in this module, along with their internal workings, capabilities, and applications. Most of these strategies can tackle prediction difficulties of the classification and regression kinds. They are commonly employed to address challenging prediction challenges when other, more traditional approaches fail to deliver results.
    • Introduce the fundamental algorithmic concepts, including sorting and searching, divide and conquer, and complex algorithms.
    • Sort data and use it for search; break down a huge problem into smaller ones and answer them recursively; apply dynamic programming to genomic research; and more.
    • Discuss and construct the most often used data structures for modern computing
    • To be able to use the most industry-used data structures in modern computing
    Content Covered
    •  Static Holdout Method
    •  k-Fold Cross-Validation
    •  Class Imbalanced Data
    •  Evaluating the Classification of Categorical Outcomes
    •  Evaluating the Estimation of Continuous Outcomes
    •  Logistic Regression
    •  K-nearest Neighbour
    •  Nearest Neighbor Method for Prediction
    •  Classification and Regression tree
    •  Support Vector Machines
    •  Process-Based Approach to the Use of SVM
    •  Naïve Bayes Methods
    •  Bayesian Networks
    •  Neural Network Architectures
    •  Ensemble Modelling

    This module gives learners the insight to apply many prediction models and grasps linear regression. Create predictions based on a group of input variables using regression analysis methods. Learners investigate the way to model an extensive range of real-world interactions using complicated statistical methodologies, such as generalised linear and additive models. This module inculcates intermediate and advanced statistical modelling methodologies. It is specifically created for learners to develop proficiency in linear regression analysis, experimental design, and extended linear and additive models. Based on these skills, interpreting data, discovering links between variables, and generating predictions are made simpler via intuitive representations.


    • Differentiate between various types of predictive models and Master linear regression
    • Understand the inner workings through algorithms of different models
    • Analyse and explore the results of logistic regression and understand when to discriminant analysis
    • Maximise analytical productivity by analysing different models and interpreting their accuracy in a well-organised manner

    Content Covered

    •  Selecting a Sample
    •  Point Estimation
    •  Sampling Distributions
    •  Interval Estimation
    •  Hypothesis Tests
    •  Statistical Inference and practical significance
    •  A Simple Linear Regression Model
    •  Least Square method
    •  Inference and Regression
    •  Multiple Regression Model
    •  Logistics Regression
    •  Predictions with Regression
    •  Model Fitting
    •  Tableau data model
    •  Shape and data transformation using Tableau Query Editor
    •  Tableau Report View
    In this module, learners will better grasp artificial intelligence (AI) applications in business and comprehend AI decision-making. Through breakthroughs in IoT and the emergence of Blockchain, this curriculum prepares students with a broad foundation of AI-enabled software solutions. As learners continue through this module, they become acquainted with the technology that powers the automated world—knowing the sorts of algorithms and how they may be utilised to enhance or replicate human behaviour across diverse applications. This module teaches about AI, IoT, Blockchain, and machine learning components while building on a solid conceptual framework that will present rigorous, hands-on, and step-by-step ways to tackle realistic, complex real-world challenges.
    • Introducing Artificial Intelligence (AI), exploring its features and variants in the business domain. Furthermore, to understand the business context of AI and interpret AI decision-making.
    • Understand & create an AI implementation plan for a business setup through recognition of suitable model parameters
    • To further explore the components of Blockchain and understand Distributed Ledger Technology (DLT) concept, features, benefits, and relevance in application
    • Understanding Hyperledger, Smart Contracts, and IoT (Internet of Things) in applied business models to assess the impact in the long term
    Content Covered
    •  Introduction to Artificial intelligence
    •  AI enables applications
    •  What is Deep Learning
    •  Artificial Neural Networks
    •  Image Processing and OpenCV
    •  Introduction to NLP
    •  Artificial Neural Networks
    •  Text Processing
    •  Text Classification
    •  Topic Modelling
    •  Recurrent Neural Networks
    •  Major components of IoT
    •  Variety of Sensors
    •  Actuators
    •  IoT protocols at various layers
    •  Applications and user interface in IoT
    •  Smart factories of tomorrow and the Industrial Internet of Things
    •  Introduction to Blockchains
    •  Introduction and usage of Hyperledger and Smart Contract
    •  Blockchains Structure
    •  Centralised, Decentralised, and Distributed systems
    •  Introduction to DLT
    •  DLT features, benefits, and usage in Blockchain
    •  Types of Blockchains
    •  Why Blockchain?
    •  Building AI and ML applications using Blockchain technology

    The purpose of this module is to discuss and explain the role of Data science and its practices in an organisation and their influence on the overall performance and competence of the organisation. This module is designed to develop an understanding of the contemporary practices and competence to develop a research or design question, illustrate how it links to current knowledge and carry out the study in a systematic manner. Learners will be encouraged to pick a research/development project that displays their past learning in the data science domain. It is meant to acquire an understanding of Data Science and the paradigm shift in the approaches and methods related to various functions of DS like data visualisation, probability, inference and modelling, data mining, data organisation, regression, and machine learning to name a few. It also endeavours to highlight the role and significance of data analytics and data modelling during the planning, decision-making, and implementation of change in the organisation. Upon successfully completing the module, the participants will have comprehensive knowledge about the broader data analysis context and a data product to demonstrate their data science expertise to potential employers or educational programs.


    • Conduct independent Research and Development within the context of a Data Science Project
    • Developing the ability to independently solve problems using analytics and data science
    • Communicate technical information clearly and succinctly to a broad, non-specialist audience.
    • Create detailed written documentation to a standard expected of a professional in the field of Data Science & evaluate Project outcomes with reference to key research publications in the relevant field.

    Interested in This Program? Secure your spot now.

    The application is free and takes only 5 minutes to complete.

      Student Reviews

      Veeraiah Yadav Doddaka
      IT Manager, Samsung

      I choose to learn Data Science and explored many options on which institute to join, among that what I found is Airtics as the best in terms of the course curriculum/on line content they designed and most…..

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      Prasad Joshi
      RF Optimization Engineer, Nokia

      I was a Data Science student at Airtics Education, which helped build a solid data science background and sharpen my programming skills…

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      Mohamed Hanan
      Procurement Assistant

      The program in Data Science offered by Airtics Education is rigorous and has provided me with a greater understanding of the data science world…

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      Capstone Projects

      Showcase your capabilities with real-world projects

      Bring Your Own Project
      Learn to solve a problem that you/your organization is facing using Data Science


      Choose From Curated Capstone Projects

      House rental Prediction

      Image Classification

      Business insights reporting

      Career Support

      Frequently Asked Questions

      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.
      We provide you with live recorded classes of the same session to follow up if you end up missing the same.
      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.
      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.
      Aspirants and professionals who are having basic computer programming skills can enroll for the program.
      Basic knowledge of programming logic and technology exposure will be helpful.
      Google Duplex can make phone calls to make restaurant and hair appointments. Google Deep Mind won a global Starcraft game challenge against gaming pros. Amazon uses AI for book and product recommendations. Websites are using chatbots to answer basic customer queries. Airports are using image recognition for staff security. Rolls Royce is using AI for predictive maintenance and servicing of airplane engines. Informatica is using AI for compliance and data gathering and analysis purposes. Fintech is using AI to combine and analyze more diverse datasets. In healthcare, AI can help analyze more data for preventative medicine. Baidu in China is producing self-driving buses for large cities.
      Automated transport, taking over dangerous jobs, robots working with humans, improved elderly care, cyborg (organic/bio-mechanic) organisms, environment monitoring and response to climate change goals.
      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.
      Yes. Many data analysts go on to become data scientists after gaining experience, advancing their programming and mathematical skills, and earning an advanced degree.‎
      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. Whether you study data science or data analytics, you’ll be building skills for an in-demand, high-paying career. ‎
      The technical skills and concepts involved in machine learning and deep learning can certainly be challenging at first. But if you break it down using the learning pathways outlined above, and commit to learning a little bit every day, it’s possible. Plus, you don’t need to master deep learning or machine learning to begin using your skills in the real world.‎
      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. The same study reports a $14,175 salary premium associated with these skills.

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        Master in Data Science

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