Master In Data Science

Program Overview

The Master in Data Science program is designed to equip students with the necessary skills to become successful data scientists. The program consists of two fundamental subjects: Computer Science and Statistics, which will teach students how to use Python, statistics, and algorithms to solve intricate problems involving data. Students have the option of selecting two elective courses based on their prior academic background and personal interests. For example, those with a background in statistics may choose to take Data Modeling in Statistics as an elective, whereas those with a computer science background may prefer Artificial Intelligence and Blockchain. All students are required to complete a data science capstone project to gain practical experience in solving real-world issues.

Flexible
12 Months

Certification
UCAM University, Spain

Blended Learning
Live classroom and Live online class.

Fees
$8700

+971 6 5310 843
(09:00am - 17:30pm)

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Key Features

200 hours of live instructor-led training
3 industry-based projects, 6 assignments
24/7 support and LMS Access
Hands-on experience with the latest tools & applied projects
Live engagement classes by seasoned academics and professionals
Internship/Projects
Flexible timing for working professionals
EMI option

What You Will Learn

You will learn essential mathematics for Data Science such as linear algebra, statistics & probability.
You will learn Python programming & libraries (NumPy, Pandas & Matplotlib) essential for data science
You will learn Data Analysis in the business processes using spreadsheets and Power BI
You will learn Data mining, Text mining, Data mining extensions, DML, DDL, SQL & Database
You will learn an elective of your choice: Statistical Data Modelling or AI & Blockchain

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?

Any graduate who has a keen interest in Data Science.
Professionals looking to learn about data analytics.
Business person trying to acquire technical skills to solve their data problems
Professionals aiming to upskill their career for better job opportunities
Professionals who wish to transition to roles such as Data Scientist.

Tools/ Frameworks/ Libraries

Scripting Tools

Python

Tools/Libraries

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

IDE Shell

Jupyter Notebook, google colab, pycharm, visualstudio code

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
eCommerce: Data Science can gain behaviour patterns & provide recommendations to customers
Banking: 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
Forecasting: Data Science can be used to predict future happenings by analysing historical data.
Manufacturing: Data Science can automate large scale processes & speed up implementation time
Retail: Data Science can help demand forecasting, pricing decisions & optimise product placement

Eligibility

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

Prerequisites

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.

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

What is included in this course?

Live training from experts

Industry-based projects

Curriculum designed by industry experts

Internship/Projects

Internationally valued certification

Partners of this Programme

UCAM University, Spain

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.

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.

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.

Why this Course ?

Course

Choosing a course of study where you are strongly inclined to pursue a European qualifying degree or for a skill set is a good start in pursuing your educational goals. At ECX, you would be empowered to lead the world.

Place of Study

To pursue your dream education, the key factor is that the students need ease in accessing the centre. At ECX, we come to your nearest city to overcome any challenges faced in commuting or travelling abroad without compromising on the quality of education.

Affordable Fee

Quality education abroad is highly expensive. At ECX, you benefit from enrolling on an affordable course with flexible payment options.

Academic Support

You get enrolled on a European degree, with blended teaching methodology and 360-degree academic assistance through our faculties with international standards for attaining a business management degree.

Career Opportunities

You become professionals in your respective field of study on completion of the degree as it brings in more of a realistic pursuit, thus transforming you with the better skill sets to approach the career market further.

Course Resources

For more detailed information about the course, please click on the links below.

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Program Details

A three-year, practical-based degree applicable to a wide range of sectors. It equips you with the opportunity to develop the IT skills required to manage and improve your performance in a business environment.

Learning Path

Module 1

Working with Data

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. Learning Outcomes L01: Analyse information using data visualisation, summary, and counting tools. L02: Acquire rudimentary skills in data handling, focusing on variable types, names, and values. L03: To learn how to use the pipe operator to combine numerous tidying operations in a chain. L04: 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

Module 2

Data Analytics in Business Processes

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. Learning Outcomes LO1: Critically analyse the use of business data in an organisational decision-making context. LO2: Demonstrate a critical understanding of business analytics principles in management functions. LO3: Apply appropriate data management and analysis techniques to retrieve, organise and manipulate data. LO4: 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

Module 3

Data Mining Techniques

Module Description 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. Learning Outcomes LO1: Understand the fundamentals of text mining and analysis, including identifying exciting patterns, extracting helpful knowledge, and supporting decision-making. LO2: Explore fundamental principles of text mining and essential algorithms and some of their practical applications. LO3: Be able to apply the learned knowledge and skills to implement scalable pattern discovery techniques on large volumes of transactional data LO4: 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

Module 4

Algorithms in Data Science

Module Description 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. Learning Outcomes LO1: Introduce the fundamental algorithmic concepts, including sorting and searching, divide and conquer, and complex algorithms. LO2: 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. LO3: Discuss and construct the most often used data structures for modern computing LO4: 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

Module 5

Specialisation Modules - Statistical Data 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. Learning Outcomes LO1: Differentiate between various types of predictive models and Master linear regression LO2: Understand the inner workings through algorithms of different models LO3: Analyse and explore the results of logistic regression and understand when to discriminant analysis LO4: 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

Module 6

Specialisation Modules - Applications of Data in Artificial Intelligence & Blockchain

Course Description 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. Learning Outcomes LO1. 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. LO2. Understand & create an AI implementation plan for a business setup through recognition of suitable model parameters LO3: To further explore the components of Blockchain and understand Distributed Ledger Technology (DLT) concept, features, benefits, and relevance in application LO4: 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

Module 7

Capstone Project - Research Methods & Dissertation

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. Learning Outcomes LO1: Conduct independent Research and Development within the context of a Data Science Project LO2: Developing the ability to independently solve problems using analytics and data science LO3: Communicate technical information clearly and succinctly to a broad, non-specialist audience. LO4: 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.

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