Data Science Foundations


Course Brief

The Data Science Foundation Course is a comprehensive introduction to the field of data science and its application, providing a thorough overview of the fundamentals of data science and the tools and techniques used for data analysis, Visualizations and Modelling.  Participants will learn of the different types of machine learning algorithms.

The course has been developed for Data management professionals, Logging geologists, Exploration geologists, Mapping geologists, Resource Geologists and any other professional interested in the evolving science of Machine learning and Artificial intelligence.

The course is divided into four primary modules:

  1. Introduction to Data Science:

This module provides an introduction to data science, covering its history, purpose, and the main components of the data science process. Participants will learn about the different types of data, the data collection process, data cleaning and preparation, and data visualization techniques.

  1. Data Analysis

This module covers the fundamentals of data analysis, such as descriptive statistics, inferential statistics, and data mining. Participants will learn about the different types of machine learning algorithms, supervised and unsupervised learning, and how to evaluate and optimize models.

  1. Data Visualization:

This module explores the fundamentals of data visualization and how to create compelling visualizations using modern tools such Grammar of Graphics.  Participants will learn about the different types of charts and graphs, and how to apply data visualization techniques to explore and explain data.

  1. Data Science in Action

Data Science in Action: This module focuses on the practical application of data science. This includes topics such as data wrangling, feature engineering, machine learning, data visualization, and more. Participants will learn to apply data science algorithms and techniques to solve real-world problems. Through hands-on programming assignments and projects, Participants will gain experience in using powerful data science tools and techniques.

  1. Predictive Analytics

Six different types of Machine Learning Algorithms (Decision trees, Random Forest, Naïve Bayes, Support Vector Machines, Logistics regression, Neural Networks) will be discussed and their basic setup will be introduced.