Course Information

Course Description

The Diploma in Data Science with a focus on Probability and Statistics for Data Analysis aims to equip students with the necessary knowledge, skills, and understanding of fundamental probabilistic and statistical concepts in the context of data science. The course is designed to provide students with a comprehensive understanding of probability theory, statistical methods, and their applications in data analysis. It encourages critical thinking and practical application of probabilistic and statistical techniques to solve real-world data problems.

COURSE OUTLINE

Introduction to Probability Theory

  • Basic concepts of probability
  • Probability distributions
  • Conditional probability and independence
  • Random variables and expectation

 

Fundamental Statistics for Data Analysis

  • Descriptive statistics
  • Statistical inference
  • Hypothesis testing
  • Regression analysis

 

Probability and Statistics in Data Science

  • Bayesian statistics
  • Machine learning algorithms based on probabilistic principles
  • Data visualization techniques

 

Practical Applications and Case Studies

  • Hands-on exercises using statistical software tools (e.g., R, Python)
  • Real-world case studies demonstrating the application of probability and statistics in data science projects

 

STUDENT ACQUISITIONS

Upon completing the course, students will:

  • Understand fundamental concepts of probability theory and statistical methods for data analysis.
  • Apply probability and statistical techniques to analyze and interpret data effectively.
  • Evaluate data-driven decisions using probabilistic reasoning and statistical inference.
  • Utilize statistical software tools to conduct data analysis and visualization.
  • Apply probabilistic models and statistical techniques to solve real-world data science problems.
  • Stay updated with emerging trends and advancements in probability and statistics for data analysis.

 

LEARNING METHODOLOGIES

The course employs a variety of learning methodologies, including:

  • Engaging lectures and discussions to build foundational knowledge of probability and statistics.
  • Hands-on exercises and assignments to reinforce theoretical concepts and develop practical skills.
  • Real-world data analysis projects to provide practical experience in applying probability and statistics to solve data science problems.
  • Case studies and examples from diverse industries to illustrate the relevance and application of probability and statistics in various contexts.
  • Interactive sessions and group activities to promote collaboration and critical thinking skills.
  • Regular feedback and assessment to monitor student progress and facilitate continuous improvement.