Postgraduate Diploma in Data Science Diploma

Core Courses / Highlights

  • Fundamental of Data Science
  • Programming for Data Scientists (Python & R)
  • Probability & Statistics for Data Science
  • Exploratory Data Analysis (EDA)
  • Machine Learning Fundamentals
  • Advanced Modeling Techniques
  • Digital Image Processing
  • Big Data & Cloud Tools
  • Data Storytelling & Communication.

Description

The Postgraduate Diploma in Data Science is an intensive one-year (two-semester) program that provides students with both theoretical foundations and hands-on experience in data analysis, statistical modeling, and machine learning. The curriculum balances mathematics, computer science, and applied problem-solving, equipping learners to work with real-world datasets and generate actionable insights. Students will gain proficiency in programming (Python, R, SQL), data visualization, database management, supervised and unsupervised machine learning, and deployment of models in practical environments.

Program Goals

  • Apply statistical and probabilistic reasoning to analyze real-world datasets.
  • Demonstrate proficiency in data cleaning, transformation, and preparation using modern tools.
  • Build, evaluate, and interpret machine learning models for classification, regression, and clustering tasks.
  • Communicate data insights effectively through visualization, dashboards, and written reports.
  • Design and execute end-to-end data science projects, from problem definition to reporting or deployment.

Acquiring Skills

  • Technical Proficiency
  • Machine Learning Expertise
  • Data Visualization & Communication
  • Ethical & Legal Awareness
  • Collaboration & Teamwork
  • Research & Innovation Skills

Career Opportunities

  • Data Scientist: designing and implementing predictive models.
  • Machine Learning Engineer: building and deploying ML systems.
  • Data Analyst: interpreting data and creating actionable insights.
  • Business Intelligence Analyst: developing dashboards and reports for decision makers.
  • Big Data Engineer: managing and processing large scale datasets.
  • Computer Vision Specialist: working on image recognition and AI vision systems.
  • AI Research Assistant: supporting research in advanced modeling and AI applications.
  • Cloud Data Engineer: leveraging cloud platforms for scalable data solutions.
  • Quantitative Analyst: applying statistical models in finance or risk analysis.
  • Academic/Trainer: teaching or developing curricula in data science and AI.

Admission Requirements

  • Bachelor’s degree (recognized university) in a related subject.
  • Certified transcript of your Bachelor’s studies.
  • Secondary School Certificate or another recognized high school credential.
  • Brief statement of purpose or research interest. (some programmes).
  • Demonstrate English language proficiency.
  • Two recent photos & the online form.
  • 30USD Non-refundable admission fee
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