Data Science Fundamentals Using AI

The one-day Data Science Fundamentals course offers participants a comprehensive introduction to the core principles and techniques of data science.

Data science is an interdisciplinary field that combines statistical analysis, machine learning, and domain knowledge to extract insights and solve complex problems from data.

This course aims to equip participants with a solid foundation in data science and its practical applications.

The course begins with an overview of data science, exploring its role and significance in various industries. Participants will understand the data science workflow, from data acquisition and preprocessing to analysis and visualisation.

They will gain insights into the key skills and roles within the field of data science. Next, participants will learn about the fundamental concepts of statistics and probability.

They will explore descriptive statistics, data distributions, hypothesis testing, and statistical inference. These statistical concepts are crucial for understanding and interpreting data in a meaningful way.

The course then delves into machine learning, covering both supervised and unsupervised learning techniques. Participants will gain an understanding of popular algorithms such as linear regression, decision trees, clustering, and dimensionality reduction. They will learn how to train, evaluate, and interpret machine learning models for various data-driven tasks.

By the end of the course, participants will have a solid foundation in data science principles and techniques. They will be equipped with the necessary knowledge and skills to approach data-driven problems, perform statistical analysis, apply machine learning algorithms, and communicate findings through data visualisation.

Course Content

• Introduction to Data Science

• Data Science Workflow

• Statistical Concepts in Data Science

• Descriptive Statistics

• Probability and Distributions

• Hypothesis Testing and Statistical Inference

• Introduction to Machine Learning

• Supervised Learning Algorithms (e.g., Linear Regression, Decision Trees)

• Unsupervised Learning Algorithms (e.g., Clustering, Dimensionality Reduction)

• Model Training and Evaluation

• Data Visualisation Principles and Techniques

• Effective Data Visualisation Tools

• Ethical Considerations in Data Science

• Privacy and Security in Data Science

• Case Studies and Practical Applications

Who is this course for?

This course is suitable for professionals and individuals interested in gaining a foundational understanding of data science.

It is beneficial for aspiring data scientists, data analysts, business professionals, and researchers who want to leverage data-driven insights for decision-making.

The course caters to both beginners in data science and those with some familiarity with the subject, providing essential knowledge and skills to kickstart their data science journey.