MATES ED2MIT Training "Introduction to Data Science & Analytics Foundations for the Maritime Sector"

MATES ED2MIT Training "Introduction to Data Science & Analytics Foundations for the Maritime Sector"

Tutorial 1: Research Methods in Data Science

Self-study course

Topics

• Research methods: Importance for Data Science
• Research methods and Research types
  • Research questions, Hypothesis and Hypothesis testing
• Business research
• CRISP-DM: Model, stages and tasks

Materials

• Practice preparation
• Lecture material on Google Drive

Tutorial 2: Statistical Data Analysis Basics: Data Structures, Statistical Characteristics

Self study course

Topics

• Types of data
  • Quantitative data
  • Qualitative data
• Statistical characteristics
• Distributions
  • Normal distribution
• Measures of data dissimilarity
• Summary and takeaway

Materials

• Lecture material on Google Drive

Tutorial 3: Data Preparation and Processing

Self study course

Topics

• Data Preprocessing: An Overview
• Data Quality
• Major Tasks in Data Preprocessing
• Data Cleaning
• Data Integration
• Data Reduction
• Data Transformation and Data Discretization

Materials

• Lecture material on Google Drive

Tutorial 4: Data Analysis Principles and Techniques Exploratory Data Analysis

Self-study course

Topics

• General aspects of Data Analysis
  • Concepts of Data Analysis
  • Principles of Data Analysis
• Data Analysis techniques
  • Some tips for data analysis
• General aspects of the Exploratory Data Analysis
• Example EDA: Procrastination

Materials

• Lecture material on Google Drive

Course Materials

All lecture and supplementary materials are shared via a shared folder on Google Drive.

Tutorial 5: TBA

Tutorial 6: TBA

Tutorial 7: TBA

Logistics

Course format: Self-study, online. There will be 7 tutorials in total, each consisting of lecture and practice material for self-study.

Practice will include working with Data Analytics tools for data preparation, analysis and reporting, using provided datasets.

Course materials are uploaded in advance on Google Drive.

Expected Outcomes

• Understand the basic concepts and approaches in Data Science and Analytics, data analytics process and stages

• Understand main methods in statistical analysis, data exploration and data preparation

• Understand main methods in machine learning, classification techniques and cluster analysis

Information

Target Participants

Technicians and VET teachers/trainers interested in Big Data and Data Management best practices and applications for maritime and offshore energy sectors. MATES partners and MATES TG experts. Women will be prioritized.

Attendees should have

• Basic knowledge of computer systems and Internet applications.
• Familiarity with Python programming language
• Basic knowledge of statistical methods

Objective

This course aims to provide basic knowledge and hand-on experience on Data Science & Analytics fbasis, methods, technologies, tools & best practices, which are considered as key factors in digital transformation for the enterprises of the future.

Specific Objectives

• Provide a general overview of the necessary competences and skills for data handling in the maritime sector.

• Reviewing the best practices in teaching Big Data technologies for Data Science & Analytics, discussion on specific tasks and requirements for maritime sector.

• Learning about new technologies and tools used for data collection & handling.