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Data Science and Analytics Lecturer / Staff Student Public

Data Science professional

#Data #Science #Data Science #Professional
University of Technology and Entrepreneurship, Phnom Penh 0 / 30 enrolled
About this programme

Course outline.

The Data Science Professional course is a comprehensive, industry-oriented program designed to prepare learners for professional roles in data science. Participants will gain expertise in data analysis, statistics, machine learning, and deep learning using industry-standard tools such as Python, Excel, R, and multiple database systems. This course emphasizes hands-on projects, real-world datasets, and end-to-end data science workflows.

What you'll get

Programme highlights.

Industry-led teaching

Live materials from practitioners working in the field today.

Hands-on exercises

You'll apply what you learn through structured workshops and case studies.

Mentor access

Personal contact with the instructor for questions and feedback.

UTE Certificate

A signed certificate of completion you can add to your CV.

Course outline

What we'll cover.

  1. 01

    Introduction to Data Science

    • • Data Science Lifecycle • Roles and Responsibilities of a Data Scientist • Tools and Technologies Overview
  2. 02

    Data Analysis with Excel and Python

    • • Data Cleaning and Analysis with Excel • Python for Data Analysis (NumPy
    • Pandas) • Exploratory Data Analysis (EDA)
  3. 03

    Statistical Analysis with R and Python

    • • Descriptive and Inferential Statistics • Probability and Distributions • Hypothesis Testing
  4. 04

    Databases for Data Science

    • • SQL Fundamentals • Working with MySQL and PostgreSQL • Introduction to NoSQL Databases
  5. 05

    Data Visualization and Reporting

    • • Data Visualization Principles • Visualization with Python and R • Dashboarding Concepts
  6. 06

    Machine Learning Techniques

    • • Supervised and Unsupervised Learning • Model Training and Evaluation • Feature Engineering
  7. 07

    Deep Learning Fundamentals

    • • Neural Networks Basics • Introduction to Deep Learning Frameworks • Practical Deep Learning Applications
  8. 08

    Capstone Project and Professional Practice

    • • End-to-End Data Science Project • Model Deployment Concepts • Ethical and Responsible Data Science