PG Diploma in Data Science and Artificial Intelligence

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Program Overview

Course Overview
Postgraduate Diploma in Data Science and Artificial Intelligence is a detailed course made for students who want to learn about data science, starting from the very basics and going up to advanced levels. The curriculum includes learning about different types of data, basics of programming, and techniques to handle large volumes of data. The course further extends into complex topics like predictive analytics, where students learn to make informed predictions using data. An integral part of the course is data visualization, which helps students understand and present data in a more insightful and accessible way. This comprehensive course provides a complete overview of the exciting field of data science, data analytics, and visualization.

Program Duration
6 Months
Learning Format
Blended Learning
+971 6 5310 843
(09:00am - 17:30pm)

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    Key Features

    Training Key Features

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

    This course is well suited for participants of all levels of experience because of the high demand for Data Science with Python programmers. Data Science with Python is beneficial for analytics professionals interested in Python, software and IT professionals interested in Analytics, as well as anyone with a genuine interest in Data Science.

    Good to have familiarity with basic concepts of mathematics and programming knowledge. Basic knowledge of Database tools and workflow will be a plus.

    Skills Covered

    • Python Programming Concepts
    • Data Wrangling
    • Data Visualization
    • Mathematical Computing
    • Model Building And Fine Tuning
    • Database Management, SQL
    • Supervised And Unsupervised Learning
    • Business Intelligence
    • Exploratory Data Analysis
    • Analytical Libraries

    Partners of this Programme

    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.

    Why this 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

    This Section Provides Details Of The Structure, Content, And Learning Outcomes Of Core Modules In This Qualification.

    Learning Path

    Module 1
    Core And Advance Python

    This module introduces students to Python, one of the key programming languages in data science. The journey begins with foundational Python knowledge, installation and setup of the programming environment, and a look into Python's simple syntax. Students learn about Python's variables, data types, and operators, essential for manipulating and storing data. The second part of this module explores control flow tools, and functions in python. Students get hands-on experience with lists, tuples, dictionaries, conditional statements, loops, and functions. Complementing the Python-specific curriculum, the module also intertwines mathematical concepts like number theory, vectors, and matrices, enabling students to use advanced Python libraries like NumPy, effectively for complex calculations and data analytics.

    • Students will be able to recall and explain the basic syntax, variables, data types, and operators in Python.
    • Demonstrate comprehension of Python's primary data structures (lists, tuples, dictionaries) and control flow tools (conditional statements, loops) by explaining their usage and significance in data manipulation and storage.
    • Apply the knowledge of Python in creating and modifying different programs.
    • Utilize Python for problem-solving and data analytics, integrating the mathematical concepts of number theory, vectors, and matrices to perform complex computations and analyses.
    Module 2
    Math And Stat For Data Science

    This module delves into the mathematical and statistical methodologies necessary for data treatment, underscored with the application of Python libraries - NumPy, Pandas, and Matplotlib. NumPy is introduced for numerical computations, followed by Pandas for data manipulation. Later, students get hands-on experience with Matplotlib, a potent tool for data visualization in Python. In sync with the Python libraries, this module unpacks foundational statistics, dividing them into descriptive and inferential branches. Hypothesis formulation and testing are introduced, empowering students to make data-based decisions. The module wraps up with a comprehensive look into probability theory and Bayes' theorem, Correlation and regression analysis providing students with a holistic understanding of mathematical and statistical data treatments.

    • Students will comprehend the functionality and usage of Python libraries such as NumPy, pandas, and matplotlib for data treatment.
    • Apply descriptive and inferential statistical methodologies to analyze and interpret data.
    • Formulate and test hypotheses, using these as a foundation for making data-driven decisions.
    • Implement probability theory and Bayes' theorem to evaluate the uncertainty and update probabilities based on evidence in data science scenarios.
    Module 3
    Data Analytics With Business Intelligence Tools

    This module delves into the pivotal skill of data visualization, which transforms complex data into insightful visuals that drive informed decision-making. The course highlights popular data visualization tools such as PowerBI and Tableau, equipping students with practical skills to navigate these platforms effectively. Throughout the module, students will understand the significance of effective data visualization, learn to choose the right visual representation for their data, and get hands-on experience in using PowerBI/Tableau. They will learn to manipulate data, create interactive dashboards, and customize visual reports that can communicate complex datasets in a clear, concise, and visually appealing manner.

    • Students will comprehend the importance of data visualization and the role of tools like PowerBI and Advanced Excel in this process.
    • Apply the knowledge to select the appropriate visual representation for various datasets.
    • Create interactive dashboards and visual reports using PowerBI/ Advanced Excel.
    • Analyze and interpret data visualizations to derive meaningful insights and aid decision-making.
    Module 4
    Database Management And Data Mining

    This module highlights the importance of database management in today's data-driven world and learn to design, create, and manage databases to store and retrieve data efficiently and securely. This module takes a deep dive into Data mining to discover patterns in large data sets. The course provides an opportunity to learn concepts, principles, and skills to practice and engage in scalable pattern discovery methods on massive data; discuss pattern evaluation measures; study methods for mining diverse kinds of frequent patterns, sequential patterns, and sub-graph patterns; and study constraint-based pattern mining, pattern-based classification, and explore their applications. Following the preprocessing, the course advances into Data cleaning and preprocessing are essential steps in the data mining process. Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the data to improve its quality. It includes tasks such as removing duplicates, filling in missing values, and correcting invalid data. Preprocessing, on the other hand, involves transforming raw data into a format that is suitable for analysis.

    • Students will be able to create and manage databases, tables, and other database objects.
    • Apply various data preprocessing techniques and manage data quality issues effectively.
    • Develop Competency in Various Types of Data Cleaning and Preprocessing
    • Acquire Practical Experience in Managing Numerical and Text Data
    Module 5
    AI & ML Essentials

    This module provides an in-depth understanding of established methods of artificial intelligence and machine learning techniques that enable computers to learn without being explicitly programmed. The module discusses various parts of artificial intelligence, which include ML (Machine Learning) and aims to explain real-world application. The module provides foundational understanding of AI concepts and terms, be able to describe several issues and ethical concerns surrounding AI, and articulate advice from experts about learning and starting a career in AI. Following the preprocessing, the course advances into algorithms such as linear regression, k-NN, decision trees, random forest, etc. for machine learning by supervised and unsupervised learning.

    • Understand Artificial Intelligence and Machine Learning fundamentals.
    • Understand the nature of the dataset and the methods for pre-processing it for machine learning.
    • Introduction to Supervised and Unsupervised Machine Learning Algorithms
    • Demonstrate a deep critical understanding of algorithms and mathematics behind established ML approaches
    Module 6
    Industry Based Capstone Project

    In this capstone project, students will be required to demonstrate the application of data science and analytics skills learned throughout the course. The project involves the end-to-end process of addressing a real-world problem using data analytics from various professional backgrounds. This includes data gathering, cleaning, exploratory data analysis, visualization, and applying machine learning algorithms for predictive analysis.

    Objectives: Formulate a real-world problem that can be addressed using data analytics. Gather and clean data relevant to the problem. Conduct exploratory data analysis to understand trends, patterns, and anomalies in the data. Visualize data in a way that is meaningful and helps in decision-making. Apply appropriate machine learning algorithms to make predictions or draw conclusions. Evaluate the effectiveness of your solution. Communicate your results effectively to both technical and non-technical audiences.

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