Management Information Systems Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

YBS077

Course Name:

Seismic Events and Data Science

Semester:

Fall
Spring

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Dr. Öğr. Üy. MEHMET SİNAN ÖZTÜRK

Course Lecturer(s):

Dr. Öğr. Üy. MEHMET SİNAN ÖZTÜRK

Course Assistants:

Course Objective and Content

Course Objectives:

The purpose of the "Seismic Events and Data Science" course is to enable students to learn all stages within the lifecycle of a data science project and to understand how data from seismic events is transformed into valuable insights. In addition to developing data science models and evaluating the results obtained from these models, students will gain detailed and practical knowledge of the entire process—from the collection of seismic data to the sharing of results with end users—covering data collection, cleaning, analysis, visualization, and reporting. The course includes lab sessions in which students will gain hands-on experience using Microsoft SQL Server and its components (SSIS, SSAS, SSRS), as well as modern tools such as Azure and Power BI.

By the end of the course, students will have the knowledge and technical skills needed to manage a data science project from start to finish, along with practical experience in the field of seismic data science.

Course Content:

The course "Seismic Events and Data Science" will comprehensively and technically examine all stages of data from seismic events within the lifecycle of a data science project. Starting with the collection of seismic event data, the course will cover fundamental data engineering steps, including storage in databases, big data, and cloud technologies; ETL (Extract, Transform, Load) processes; and data cleaning and quality control, addressing each step in detail within the data science process. Students will gain hands-on experience in time series analysis, spatial analysis, and map-based visualization of seismic data using Geographic Information Systems (GIS), as well as interpret the data through statistical analyses, data science models, and trend analyses. The course will also provide insights into how business intelligence reporting tools can visualize seismic event data and present it to end-users, along with the application of artificial intelligence and machine learning models for seismic predictions.

Throughout the course, laboratory exercises will incorporate modern data science and business intelligence tools such as Microsoft SQL Server (SSMS, SSIS, SSAS, and SSRS), Power BI, Azure ML, Snowflake, ThoughtSpot, and Coalesce, allowing students to develop technical skills and gain extensive experience in seismic data science. The course will thoroughly address each step of the seismic data science process—from data collection, storage, cleaning, processing, and analysis to visualizing results, reporting, and delivering insights to the end user through business intelligence applications. By understanding the structure of seismic data, students will learn to apply data science methods such as time series analysis, statistical analysis, GIS-based seismic data visualization, and machine learning through practical applications. With the use of current technologies like Power BI, Azure ML, SQL Server components (SSIS, SSAS, SSRS), Snowflake, ThoughtSpot, and Coalesce in laboratory exercises on seismic data sets, students will acquire the skills to manage data science projects focused on seismic events, conduct analysis, build data models, and evaluate the outcomes.

Learning Outcomes

The students who have succeeded in this course;
1) 1. Managing the Data Science Project Lifecycle: - Students will be able to manage all stages of the data science project lifecycle (data collection, processing, analysis, visualization, and reporting) for transforming seismic event data into insights. - They will be able to plan and implement steps such as developing data science models, evaluating model outputs, and delivering results to end users throughout the project.
2) 2. Data Collection and Data Engineering Skills: - Students will learn the process of collecting and storing seismic data on big data and cloud technologies and will be able to apply essential data engineering steps to process data. - They will gain competencies in data cleaning, ensuring data integrity, quality control, and database management through ETL (Extract, Transform, Load) processes.
3) 3. Time Series Analysis and Spatial Analysis: - Students will be able to perform time series analysis on seismic data and examine it using spatial analysis techniques within Geographic Information Systems (GIS). - They will develop the ability to interpret data through map-based visualization techniques and will refine data with various analytical methods.
4) 4. Fundamental Statistical Analysis and Data Science Model Development: - Using statistical analysis techniques, students will be able to interpret seismic data and prepare suitable datasets for modeling. - They will gain experience in trend analysis, building data science models, and evaluating the results obtained from these models.
5) 5. Machine Learning and Artificial Intelligence Techniques: - Students will learn to apply machine learning and artificial intelligence algorithms for seismic predictions, gaining the skills to implement and evaluate various modeling techniques. - They will develop expertise in applying AI algorithms to seismic event data and assessing model performance.
6) 6. Using Business Intelligence and Data Visualization Tools: - Students will acquire skills in visualizing, analyzing, and reporting seismic data effectively using tools such as Microsoft SQL Server (SSMS, SSIS, SSAS, SSRS), Power BI, and Azure ML. - They will be able to optimize data analysis processes using modern data science and business intelligence platforms, presenting meaningful results to end users.
7) 7. Using Cloud and Big Data Technologies: - By gaining experience in managing seismic data with big data and cloud technologies, students will be able to use cloud-based data management and analysis tools such as Snowflake and ThoughtSpot. - They will acquire the competence to work with large volumes of data and ensure data security within cloud environments.
8) 8. Reporting and Presentation of Results: - Students will develop skills in visualizing seismic event data, reporting analytical results, and delivering insights to end users using business intelligence reporting tools. - They will be able to communicate insights from seismic data effectively using advanced data visualization techniques.
9) 9. Data Science Project Experience: - Through laboratory exercises on seismic event data, students will gain hands-on experience in conducting real-world data science projects, enhancing their project management skills. - By the end of the course, they will be able to manage all stages from data collection to analysis and reporting effectively, gaining practical experience.
10) 10. Comprehensive Technical Skills and Analytical Thinking Competence: - Students will gain advanced technical knowledge and analytical thinking skills in processing, interpreting, analyzing, and presenting data, enabling them to make data-driven decisions specific to seismic events. - With the technical skills developed throughout the course, students will have the capacity to independently conduct seismic data science projects.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction and Overview of Data Science Definition and Scope of Data Science An introduction to what data science is and its scope. Examination of how data science is applied across various fields and its contributions to data-driven decision-making processes. Role of Data Science in Earth Sciences The importance of data science in earth sciences and its potential benefits in these fields will be discussed. Examples will illustrate how studies on seismic data and earth science data contribute to data-driven analysis processes. Fundamental Processes in Data Science Step-by-step examination of the core processes in data science projects: Data Collection: Overview of data sources, data types, and data collection methods. Data Processing: Introduction to preparing raw data for analysis, including cleaning and processing. Data Analysis: Basic analysis methods and steps for deriving insights from data will be introduced. Introduction to the Data Science Project Lifecycle A general overview of the data science project lifecycle, covering how each stage progresses and complements the next. The scope of the project lifecycle, to be explored in detail in later weeks, will be introduced.	Students are advised to familiarize themselves with the terms "data science" and "earth sciences" before the class. Basic knowledge about data collection, processing, and analysis processes would be beneficial. An Introduction to Data Science, (Jeffrey Stanton, 2013)
2)	Data Collection and Management in Earth Sciences Structure of Seismic and Other Earth Science Data General information will be provided about the types, characteristics, and structures of seismic data and other earth science data. The process of how data is collected and stored, including data formats and organizational structures, will be discussed. Data Management Systems The definition and importance of data management systems will be covered. Detailed information about different types of data management systems (databases, data lakes) will be presented, along with an examination of their advantages and disadvantages. The role of data management systems in data collection and analysis processes in earth sciences will be discussed. Application with Microsoft SQL Server An application on data storage and management using Microsoft SQL Server (SSMS) will be conducted. Practical exercises on basic SQL commands and database management skills will be performed. Examples will be provided on how to store, organize, and query data in SQL Server. Best Practices in Data Management Information will be provided about best practices and standards to consider in data management. Strategies for data security, data quality, and data integration will be discussed.	Students are encouraged to have basic knowledge of data management systems and databases. It would be beneficial to review fundamental SQL concepts related to Microsoft SQL Server. An Introduction to Data Science, (Jeffrey Stanton, 2013) Introducing Microsoft SQL Server, (Ross Mistry and Stacia Misner)
3)	Data Engineering and ETL Processes for Earth Sciences 1. Importance of ETL (Extract, Transform, Load) Processes Information will be provided about the definition of ETL processes and their role in data engineering. The use of ETL processes to enhance data quality and achieve data integration will be explained. The significance of ETL processes in data science projects and their application areas will be discussed. 2. ETL Processes for Seismic Data Examination of the specific requirements and challenges for ETL processes concerning seismic data. Analysis of example scenarios for extraction, transformation, and loading stages of seismic data. Information on data collection from seismic data sources, data transformation techniques, and data loading processes will be provided. 3. Application with Microsoft SQL Server Application of ETL processes using Microsoft SQL Server (SSMS, SSIS, and SSAS). Practical exercises on creating data integration and data flows using SQL Server Integration Services (SSIS). Hands-on examples will be presented on data analysis and reporting using SQL Server Analysis Services (SSAS). 4. Best Practices in ETL Processes Information will be provided on best practices and strategies to consider in ETL processes. Recommendations will be presented regarding performance optimization, error handling, and ensuring data quality.	Students are encouraged to have knowledge of fundamental data engineering concepts and data management systems. Familiarity with Microsoft SQL Server and a review of basic ETL concepts will be beneficial. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Introducing Microsoft SQL Server, (Ross Mistry and Stacia Misner) 4. SQL Server Integration Services Cookbook 5. SQL Server Analysis Services Cookbook
4)	Data Cleaning and Quality Control Data Cleaning: Managing Missing, Erroneous, or Redundant Data The definition and importance of data cleaning will be discussed. Information will be provided on identifying missing data and how to manage it. Methods and strategies for correcting erroneous data and cleaning redundant data will be explained. Importance of Data Quality in Earth Sciences The role and significance of data quality in earth science research will be debated. Criteria and standards necessary for obtaining high-quality data will be addressed. The negative consequences of data quality issues in the field of earth sciences will be examined. Application with Microsoft SQL Server Execution of data cleaning and quality control processes using Microsoft SQL Server (SSMS, SSIS, and SSAS). Practical exercises will be conducted on performing data cleaning operations with SQL Server Integration Services (SSIS) and ensuring data quality. Example projects will be presented on creating reports and conducting analyses with cleaned data using SQL Server Analysis Services (SSAS). Best Practices in Data Cleaning and Quality Control Discussion on best practices and strategies to consider in data cleaning and quality control processes. Information on methods and tools to enhance data quality will be provided.	Students are advised to review fundamental concepts related to data cleaning and quality control. Familiarity with the basic features of Microsoft SQL Server and data management systems will be beneficial. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Introducing Microsoft SQL Server, (Ross Mistry and Stacia Misner) 4. SQL Server Integration Services Cookbook 5. SQL Server Analysis Services Cookbook
5)	Preliminary Data Examination and Data Exploration in Seismic Data Preliminary Data Examination: Determining data types and dimensions as the first step to exploring the fundamental characteristics of the seismic dataset. Identifying missing data and discussing how to manage it. Detecting anomalous values (outliers): Evaluating data quality and analyzing the impacts of these values. Data Exploration: Examining the distribution of seismic data: Analyzing fundamental statistics (mean, median, variance, etc.). Investigating the temporal changes of events using time series graphs. Analyzing various subgroups of the seismic dataset and observing differences between these groups. Analysis of Data Relationships: Performing correlation analysis to examine the relationships between different variables in seismic events. Identifying similarities and differences between various types of seismic events. Discovering patterns in the relationships within the dataset. Application: Data visualization and reporting using Microsoft SQL Server, Power BI, and Python. Executing data extraction and preprocessing processes from SQL Server. Creating interactive graphs and preparing reports using Power BI. Analyzing and visualizing data using Python: Analyzing the dataset and presenting it with graphs using libraries like Pandas and Matplotlib. Best Practices in Data Analysis: Discussion on best practices and strategies to consider during preliminary examination and exploration processes. Information on effective methods and techniques for data visualization will be provided.	Students are advised to review fundamental statistical concepts and familiarize themselves with the tools used for data analysis. Familiarity with the basic features of tools like Microsoft SQL Server and Python will be beneficial. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Introducing Microsoft SQL Server, (Ross Mistry and Stacia Misner) 4. Introducing Microsoft Power BI 5. Microsoft Power BI Cookbook 6. Microsoft Learn
6)	Data Analysis, Time Series, and Geographic Information Systems (GIS) in Seismic Data Time Series Analysis: What is a time series? Fundamental information about data analysis and time series analysis in seismic events. Temporal data modeling in geosciences: Analyzing changes in seismic events over time using time series data. Identifying trends and seasonal variations in time series data. Geographic Data Analysis: Analyzing geographic data: Examining seismic events in a geographic context. Visualizing the local distributions of seismic events on maps. Utilizing Geographic Information Systems (GIS): Integrating seismic data for geographic analysis. Data Exploration and Relationship Analysis: Exploring the fundamental characteristics of seismic data: Detecting missing data and anomalous values. Analyzing relationships between different variables: Correlation and distribution studies. Visualizing and interpreting relationships within seismic data. Application: Data visualization and reporting using Microsoft SSRS, Power BI, and Python. Extracting and preprocessing data from SQL Server: Preparing data for the analysis of seismic data. Creating interactive graphics and map-based data visualizations in Power BI. Analyzing data using Python: Analyzing the dataset and presenting it with graphs using libraries like Pandas and Matplotlib. Best Practices in Time Series and Geographic Analysis: Discussion on best practices and strategies to consider during time series and geographic data analysis. Information on effective methods and techniques for data visualization will be provided.	Students are advised to review fundamental concepts related to time series analysis and geographic data analysis. Familiarity with the basic features of tools like Microsoft SQL Server, Power BI, and Python will be beneficial. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Introducing Microsoft SQL Server, (Ross Mistry and Stacia Misner) 4. Introducing Microsoft Power BI 5. Microsoft Power BI Cookbook 6. Python Basics: A Practical Introduction to Python 3 7. Microsoft Learn
7)	Statistical Analysis of Seismic Data and Data Visualization Techniques Basic Statistical Concepts: Introduction to basic statistical concepts such as mean, standard deviation, and correlation. Trend analyses in seismic data: Examining time series data and identifying trends. Statistical hypothesis testing: Applying significance tests in seismic data. The Importance of Data Visualization: The role and significance of data visualization in conveying information in geosciences. Introduction to various data visualization techniques and tools: Types of graphs, maps, and interactive reports. Visualization strategies: Presenting data appropriately for the target audience and narrative techniques. Visualization Applications: Statistical Analysis in Power BI: Using Power BI for calculating and reporting basic statistics. Examining the dataset: Identifying basic statistics and trends. Creating Advanced Charts and Reports in Power BI: Developing interactive and meaningful charts using DAX and Power Query. Creating and interpreting different types of graphs (scatter plots, bar charts, etc.). Application: Conducting statistical analysis and visualization of seismic data using Power BI: Creating graphs: Analyzing the dataset using various visualization techniques. Preparing reports: Applying user-friendly visualization techniques and creating interactive reports. Optimizing visualizations based on user feedback. Communicating Through Data Visualization: Effectively presenting and narrating visualized data: Bridging the gap between the audience and the data. Contributing to decision-making processes through visualization: The importance of data-driven decision-making.	Students are advised to have prior knowledge of basic statistical concepts. Familiarity with Power BI and DAX will be beneficial for the application. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Introducing Microsoft SQL Server, (Ross Mistry and Stacia Misner) 4. Introducing Microsoft Power BI 5. Microsoft Power BI Cookbook 6. Python Basics: A Practical Introduction to Python 3 7. Microsoft Learn
8)	Business Intelligence and Geosciences Business Intelligence Concepts: Definition and key components of business intelligence: Data mining, reporting, analytics. Steps in the business intelligence process: Data collection, processing, analysis, and reporting. Applications of business intelligence in geosciences: Data-driven approaches in decision-making processes. Decision Support Systems: The importance of decision support systems in geosciences: Strategic decision-making through data analysis. Different types of decision support systems: Database systems, reporting tools, and analytics platforms. Integration of real-time data analysis with decision support systems. Application: Preparing Business Intelligence Reports with Microsoft Power BI: Introduction to the basic features and interface of Power BI. Connecting data sources to Power BI and creating a data model. Creating interactive business intelligence reports: Adding charts, tables, and indicators. Sharing reports and integrating user feedback. Data Analysis and Reporting: Utilizing different data analysis techniques: Slicing, dicing, and analytical displays. Creating data stories with business intelligence reports: Developing content suitable for the target audience. Visualizing reports: Preparing user-friendly and interactive graphics. Business Intelligence Strategies in Geosciences: Improving decision-making processes in geosciences through data analytics. Generating strategic insights through processing and analyzing seismic data. Supporting data-driven decision-making processes in geosciences through the integration of business intelligence tools.	Students are advised to have knowledge of basic business intelligence concepts and data analysis methods. Familiarity with Microsoft Power BI will be beneficial for the application. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Introducing Microsoft Power BI 4. Microsoft Power BI Cookbook 5. Python Basics: A Practical Introduction to Python 3 6. Microsoft Learn
9)	Data Engineering and Fundamentals of Machine Learning in Seismic Data Introduction to Data Engineering: Azure Data Factory: Data integration processes: Collecting and processing data from various sources. Managing workflows: Automating and monitoring data flows. Preparing seismic data: Cleaning, transforming, and making data suitable for modeling. Azure Synapse: Data analytics and data warehousing solutions: Big data analysis and warehouse structures. Analyzing seismic data: Data modeling, querying processes, and data integration. Querying methods: Data extraction and analysis using T-SQL. Fundamentals of Machine Learning: What is Machine Learning? Definition and key components of machine learning: Data, algorithms, and modeling. The role of machine learning in seismic data analysis: Prediction and classification processes. Supervised and Unsupervised Learning Methods: Supervised learning: Data labeling and model training. Unsupervised learning: Discovering data clusters and analyzing relationships. Advantages of both methods and their applications to seismic data. Application: Basic Machine Learning Models with Power BI and Azure ML: Creating basic machine learning models using Azure ML: Classification and regression models. Model evaluation: Using metrics such as accuracy, precision, and false positive rates. Visualizing and Reporting Results with Power BI: Analyzing model outputs: Interpreting and visualizing predictions. Integrating with decision support systems: Incorporating analysis results into business intelligence reports. Relationship Between Data Engineering and Machine Learning: The impact of data engineering on machine learning: Data quality, data processing, and model success. Best practices in seismic data engineering: Managing data sources and optimizing data flows.	Students are advised to have knowledge of basic data engineering and machine learning concepts. Familiarity with Azure platforms and Power BI will be beneficial for the application. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Introducing Microsoft Power BI 4. Microsoft Power BI Cookbook 5. Python Basics: A Practical Introduction to Python 3 6. Microsoft Azure Essentials 7. Fundamentals of Azure 8. Microsoft Learn
10)	Big Data and Cloud Technologies Concept of Big Data: Definition of Big Data: What is big data? Explanation with the 3Vs (Volume, Velocity, Variety). The role and importance of big data in the field of Earth sciences: The impact of big data on Earth science research. Characteristics of Big Data: Size and diversity of data: Different data sources and formats. Management of data volume: Challenges in processing and storing big data. Cloud Computing Technologies: What is Cloud Computing? Fundamental components of cloud computing: Infrastructure, platform, and software services (IaaS, PaaS, SaaS). The impact of cloud computing on data science: Scalability, flexibility, and cost-effectiveness. Cloud Solutions for Big Data: Cloud-based data processing and storage solutions: Azure, AWS, and Google Cloud. Use of cloud technologies in Earth sciences: Storing and processing data in the cloud. Application: Big Data Analysis on Azure and Fabric: Big data analytics on the Azure platform: Data processing with Azure Synapse and Azure Data Lake. Data integration and analysis processes using Fabric: Managing data flows and obtaining analysis results. Practical examples: Real-time data analysis and analysis conducted on large data sets. The Future of Big Data and Cloud Technologies: The evolution of big data and cloud computing technologies in the future: Innovations and advancements in data management. The potential of big data and cloud solutions in Earth sciences: New research opportunities and data analytics.	Students are encouraged to have a basic understanding of cloud computing and big data concepts. Familiarity with the Azure platform will be beneficial during the application process. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Introducing Microsoft Power BI 4. Microsoft Power BI Cookbook 5. Python Basics: A Practical Introduction to Python 3 6. Microsoft Azure Essentials 7. Fundamentals of Azure 8. Microsoft Fabric: The Essential Guide for Decision Makers 9. Microsoft Learn
11)	Big Data Management with Snowflake and Data Engineering with Coalesce Big Data Management with Snowflake: Snowflake Platform: Cloud-based data management architecture: Fundamentals and components of Snowflake's architecture. Advantages for data storage and analysis: Scalability, performance, data sharing, and security features. Use of Big Data in Earth Sciences: The importance of Snowflake for analyzing and managing seismic data: Storing and processing seismic data. Application areas: Use of Snowflake for data analytics and reporting in Earth science research. Application: Implementing Data Storage and Management Processes on Snowflake: Database creation: Setting up databases and schemas in Snowflake. Querying: Examples of querying data using SQL. Data loading processes: Methods for loading data into Snowflake from various data sources. Data Engineering with Coalesce: Coalesce Platform: Modern solutions and workflows in data engineering: Tools and methods offered by Coalesce. Importance of data management and modeling processes: Ensuring data quality and ETL processes. Data Workflows and Data Models: Designing data flows: Designing and managing ETL (Extract, Transform, Load) processes. Application: Creating data flows with Coalesce and utilizing the application's interface. Additional Applications: dbt and Astronomer.io: If time permits, a brief look at creating data models with dbt: Features and use cases of dbt. A short overview of managing data workflows with Astronomer.io: Optimizing data workflows with Astronomer.io capabilities.	Students are encouraged to have a basic understanding of Snowflake and Coalesce platforms. Familiarity with SQL will be beneficial during the application process. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. https://docs.snowflake.com/ 4. https://docs.coalesce.io/docs/get-started/quick-start/
12)	Data Search and Analysis with Thoughtspot and Alternative Business Intelligence Applications What is Thoughtspot?: Concept of Search-Based Data Analysis: Advantages provided by Thoughtspot: User-friendly interface, quick data analysis, and instant feedback. Functionality: Querying and analyzing data through a search engine-like interaction. Examining Geoscientific Data with Thoughtspot: Search and analysis methods for seismic data: Interaction and analysis processes using seismic data in Thoughtspot. Example scenarios: Practical searches and analyses using different seismic datasets. Alternative Business Intelligence Applications: Tableau: Visual analytics and reporting tools: Features for visualizing data. Creating interactive reports: Designing graphs and dashboards in Tableau. Power BI: Data visualization and business intelligence solutions: Analyzing data using DAX and Power Query. Interactive reports and dashboards: Creating and sharing reports in Power BI. Other Alternatives: Introduction to other popular business intelligence applications like Domo and Looker: Basic features and use cases. Advantages and disadvantages of each tool: When to choose which tool. Application: Data Discovery on Thoughtspot: Data analysis and discovery on Thoughtspot's interface: Process for users to perform search-based data exploration. Example application scenarios with seismic data: Search and analysis examples in Thoughtspot using real-world data. Visualization with Alternative Applications: Visualization using Tableau and Power BI on similar datasets: Creating reports using both platforms on comparable datasets. Visualization applications: Using various graph types for meaningful data presentation.	Students are encouraged to have a basic understanding of Thoughtspot, Tableau, and Power BI. Providing necessary datasets and preparing to work on predefined scenarios will enhance the application process. https://docs.thoughtspot.com/software/latest/index.html https://community.tableau.com/s/question/0D54T00000C5zUlSAJ/tableau-desktop-documentation https://help.tableau.com/current/guides/get-started-tutorial/en-us/get-started-tutorial-home.htm
13)	Applications of Artificial Intelligence in Geosciences Use of Artificial Intelligence and Deep Learning Techniques in Geosciences: Foundations of Artificial Intelligence (AI) and Deep Learning: Definitions of AI and deep learning: Basic principles and operational mechanisms. Application areas in geosciences: Analysis of seismic data, geostatistical modeling, and estimation of groundwater resources. Analysis of Seismic Data: Analysis of seismic data using deep learning methods: Processing data and extracting key features. Geostatistical modeling: Making predictions using statistical methods in geosciences. Estimation of Groundwater Resources: Management of water resources using AI: Estimation and monitoring of water levels. Role of AI in Geoscientific Predictions: Prediction using AI and Machine Learning Methods: Analysis of soil behavior: Predicting soil characteristics under different scenarios. Earthquake prediction: Determining earthquake probabilities using AI techniques. Environmental monitoring: Analyzing and predicting environmental changes using machine learning. Example Projects: Seismic Event Prediction: A model predicting earthquake probabilities through the analysis of seismic data: The process of developing the model using datasets. Geological Mapping: Automatically creating geological maps using deep learning algorithms: Data preparation and modeling processes. Water Resource Management: Estimation and management of water levels with AI-based systems: Project implementation and evaluation of results. Alternative Applications: Azure Databricks: A platform used for big data analysis and AI applications: Spark-based data processing and machine learning applications. Azure Cognitive Services: A platform offering various AI services like image and text analysis: Applications of image analysis in geoscientific data. Azure Synapse Analytics: A service used for data analytics and big data solutions: Processes for data integration and analysis in geosciences. Application: Creating Advanced AI Models: Developing AI projects for geosciences using alternatives specified outside of Azure ML: Project development process. Model training using sample datasets: Preparing data, training models, and analyzing results.	Students are encouraged to have a basic understanding of artificial intelligence and deep learning concepts. Providing necessary datasets and pre-defining example projects will enhance the effectiveness of the application process. 1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers) 2. An Introduction to Data Science, (Jeffrey Stanton, 2013) 3. Microsoft Azure Essentials 4. Fundamentals of Azure 5. Microsoft Fabric: The Essential Guide for Decision Makers 6. Microsoft Azure Machine Learning 7. Microsoft Azure Essentials Azure Machine Learning 8. Microsoft Learn 9. https://learn.microsoft.com/en-us/azure/machine-learning/?view=azureml-api-2
14)	Project Presentations and Evaluation Presentation of Students' Prepared Projects: Presentation Format: Each student or group will present their projects within a designated time frame (e.g., 10-15 minutes). The presentation will highlight the project's purpose, methods used, findings, and conclusions. Content of Presentation: Project Introduction: Background and purpose of the project. Methodology: Data sources, techniques, and tools used. Results: Findings obtained and their interpretation. Future Work: Suggestions for the development or expansion of the project. General Evaluation from the Perspective of Data Science and Geosciences: Evaluation Criteria: Innovation and originality of the project. Appropriateness and accuracy of the methods used. Fluency and clarity of the presentation. Practical applicability of the results obtained. Feedback: Evaluations made between students and faculty members. Constructive feedback and suggestions specific to each project. Conclusions and Closing of the Course: Learnings: Evaluation of the knowledge and experiences gained throughout the course. Importance of the interaction and applications between data science and geosciences. Closure: Reflections on what students learned from the course and areas for development. Future opportunities: Career goals and opportunities for students in the fields of data science and geosciences. Completion of the Course: Distribution of certificates or participation documents. Closing speech: A summary of the course by the instructor and evaluation of the students' achievements.	Arranging necessary materials and technology for student presentations (e.g., projector, computer). Communicating with students to ensure presentations are conducted smoothly and on time.

Sources

Course Notes / Textbooks:

1. Data Science for the Geosciences, (Lijing Wang , David Zhen Yin, Jef Caers)
2. An Introduction to Data Science, (Jeffrey Stanton, 2013)
3. Art of Data Science, (Roger D. Peng & Elizabeth Matsui, 2015)
4. The Data Science Handbook, (Carl Shan, Henry Wang, William Chen, & Max Song, 2015)
5. Data Driven: Creating a Data Culture, (Hilary Mason & DJ Patil, 2015)
6. Building Data Science Teams, (DJ Patil, 2011)

References:

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5	6	7	8	9	10
Program Outcomes
1) It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering.
2) Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming.
3) Uses different information technologies and systems for understanding and solving various business problems.
4) Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods.
5) Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database.
6) Gains technical and managerial contributions to IT projects and takes responsibility.
7) Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively.
8) Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education.
9) Develops teamwork, negotiation, leadership and entrepreneurship skills.
10) Has universal ethical values, social responsibility awareness and sufficient legal knowledge.
11) Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them.
12) Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms.
13) It uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering.
2)	Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming.
3)	Uses different information technologies and systems for understanding and solving various business problems.
4)	Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods.
5)	Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database.
6)	Gains technical and managerial contributions to IT projects and takes responsibility.
7)	Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively.
8)	Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education.
9)	Develops teamwork, negotiation, leadership and entrepreneurship skills.
10)	Has universal ethical values, social responsibility awareness and sufficient legal knowledge.
11)	Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them.
12)	Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms.
13)	It uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	5	% 10
Midterms	1	% 30
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100