Data Analytics in Modern Corporate Business

Entry Requirements

- Basic familiarity with computer use (no certification required)

- Holders of a higher education degree

- High school graduates (e.g., current students)

Programme's Goal

Data Analytics in Modern Corporate Business is a comprehensive course designed to provide participants with the essential skills and knowledge needed to harness the power of data analytics within a corporate setting. This course covers a broad spectrum of topics, from the fundamentals of data analytics existence, data analytics roles, data collection and processing to advanced techniques in data visualization and business intelligence. Participants will gain hands-on experience with industry-leading tools and technologies, preparing them to drive data-driven decision-making and innovation in their organizations. 

This course is ideal for participants that want to come into the modern data analytics world, business professionals who need to enhance their data skills or switch to their first data analytics role and aspiring data analysts who wish to enhance their analytical skills and apply them in a corporate setting. Through a blend of theoretical knowledge and practical application, participants will be well-prepared to leverage data analytics to improve business outcomes and drive organizational success.

In this course you will study about five core topics of a modern data analytics stack:

1. Intro to Data Analytics and Data Roles
2. Databases & SQL
3. Data Warehousing
4. Git
5. Data Visualization

Furthermore, you will simulate a real-world scenario where a company needs to streamline its data operations to gain valuable business insights. The company's transactional data is stored in a PostgreSQL database, which needs to be continuously replicated to BigQuery using Google Datastream. The data will be transformed and analyzed in BigQuery, with Python scripts handling additional ETL tasks and automation.The data team will use Git to track changes in the codebase, collaborate, and maintain version control of the scripts and configurations. Finally, the processed data will be visualized using Tableau Cloud and Metabase to facilitate data-driven decision-making.

Educational Objectives

By the end of the course, participants will be able to:

1. Understand and implement both data analytics fundamentalsand advanced data analytics concepts:
   • Grasp the core principles of data analytics, including data collection, processing, and analysis techniques.
   • Use advanced techniques in data manipulation and visualization.
2. Utilize Industry-Leading Analytical Tools:
   • Proficiently use SQL for database management and querying.
   • Employ Python for data manipulation, ETL processes, and automation.
   • Leverage BigQuery for large-scale data storage and advanced analytics.
   • Create interactive and insightful visualizations using Tableau Cloud and Metabase.
3. Design and Execute Data Analytics Projects:
   • Develop end-to-end data analytics pipelines from data ingestion to visualization.
   • Implement real-time data replication and synchronization using Google Datastream.
   • Optimize data workflows for efficiency and performance.
4. Communicate Insights Effectively:
   • Develop compelling data visualizations and dashboards that convey actionable insights.
   • Use data storytelling techniques to communicate findings to stakeholders clearly and persuasively.
5. Collaborate Using Version Control:
   • Manage project codebases effectively using Git.
   • Collaborate with team members through branching, merging, and pull requests.

By achieving these outcomes, participants will be equipped with the skills and knowledge needed to leverage data analytics to enhance business performance, drive strategic decisions, and create competitive advantages in the corporate world.

Contact Info 

The program’s contact details are those of the Scientific Director.

Teaching Staff

All members of the teaching staff must:

• Hold a relevant master’s degree
• Be proficient in the use of Information and Communication Technologies (ICT)

Suggested Instructors: Antonios Angelakis

Teaching Units - Duration

Teaching Units Presentation

Module 1: Intro to Data Analytics and Data Roles
This module aims to introduce the broader field of data analytics in the modern corporate setting. It provides participants with a thorough understanding of what data analytics entails, why it’s important, and how it applies across various business contexts. Key learning points include:

• Data Analytics Steps: Participants will learn the foundational steps of data analytics, including:
  • Data Collection: Gathering data from various sources.
  • Data Cleaning: Removing errors and irrelevant records.
  • Data Analysis: Applying statistical techniques and processing.
  • Data Interpretation: Deriving significant insights.
  • Data Visualization: Presenting data in graphical form.
• Types of Data Analysis: Participants will explore different types of data analysis:
  • Descriptive Analysis: What happened?
  • Diagnostic Analysis: Why did it happen?
  • Predictive Analysis: What will happen?
  • Prescriptive Analysis: What should we do?
• Data Teams and Career Development: The module guides participants in understanding the structure and architecture of data teams and offers guidance on:
  • Selecting the Right Data Role: Assessing personal interests and market demand.
  • Creating an Impressive Portfolio: Showcasing skills and projects.
  • Networking and Job Searching: Effective strategies for finding opportunities in data.

Module 2: Databases & SQL
This module introduces databases and SQL, covering basic principles and advanced data management techniques. Key topics include:

• Introduction to Databases: Definition, importance, types of databases, and database components (data, schema, queries, and relationships).
• Introduction to SQL: Definition, significance, types of SQL commands, basic SQL syntax, and advanced SQL techniques (such as Recursive CTEs, Window functions, Pivoting, Full-Text Search, using JSON in SQL, and managing user permissions).

Upon completion, participants will be able to:

• Fully understand database principles.
• Manage a database using SQL.
• Effectively analyze data with SQL.

Module 3: Git
This module covers Git, a distributed version control system widely used for source code management and project collaboration in software development. Key learning areas include:

• Introduction to Git: Definition, usage, installation, basic commands, setup, and working with repositories.

Upon completion, participants will be able to:

• Manage code and track change history.
• Collaborate with other developers/analysts.
• Handle repositories using platforms like GitHub.

Module 4: Data Warehousing
This module provides a comprehensive understanding of data warehouses and their importance in data analysis within corporate environments. Topics include:

• Definition and Role of Data Warehouses: How they support executive decision-making.
• Benefits of Data Warehouses: Analysis of historical data for trend prediction and strategic planning.
• Differences from Traditional Databases: Why they are suitable for complex, multidimensional analyses.
• ETL Processes: Transforming data into an analysis-ready format and loading it into the data warehouse.
• Popular Data Warehouse Technologies: Introduction to Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure Synapse Analytics.
• Best Practices for Data Warehouse Creation and Management

Upon completion, participants will be able to:

• Create, manage, and optimize data warehouses.
• Support data analysis and strategic decision-making in organizations.
• Perform data extraction, transformation, and loading (ETL) processes for business decisions.

Module 5: Data Visualization
This module covers the principles and best practices for creating effective and engaging data visualizations, helping participants understand when and how to use different types of charts and graphs. Topics include:

• Importance of Data Visualization: How it aids in understanding complex datasets, and helps in data storytelling.
• Key Components of Effective Data Visualization
• Principles of Effective Data Visualization: Best practices, focusing on core findings, representing data accurately, and tailoring visualizations to audience expertise.
• Types of Data Visualizations and Usage: Overview of various charts and graphs, including bar charts, line graphs, radar charts, stacked charts, area diagrams, and more.
• Common Pitfalls in Data Visualization

Upon completion, participants will be able to:

• Create effective data visualizations following best practices.
• Communicate findings effectively.
• Understand how data visualization tools work.

Final Project
Participants will complete a capstone project at the end of the course. This project guides participants through a full data analysis workflow, from storing transactional data to final visualization for decision-making. Topics include:

• Creating a Data Analysis Workflow: Using PostgreSQL for data storage, Google Datastream for data streaming to BigQuery, and ensuring seamless data updating for analysis.
• Data Transformation and Analysis in BigQuery: Utilizing Python notebooks for additional ETL tasks and automation.
• Using Git for Code Management: Uploading Python notebooks to GitHub and following best practices for Git use.
• Data Visualization: Using Tableau Cloud and Metabase to visualize processed data for decision-making.

Upon course completion, participants will be able to create, manage, and visualize a full data analysis workflow.

Final Grade Calculation
Grades are determined as follows:

• Assignments: 20% of the total grade (5 multiple-choice assignments per module)
• Capstone Project: 80% of the total grade

Participants must achieve a final score of at least 75 to pass the program. Only scores of 75 or above will result in certification.

Example Grade Calculation
Assignment scores:

• 65/100
• 70/100
• 90/100
• 100/100
• 76/100

The total assignment score is 401 out of 500, averaging 80.2 out of 100. For final grade calculation:
80.2 (assignment average) * 0.2 (20%) = 16.04

Thus, to achieve the required final score of 75, the candidate needs at least:
75 (required final score) - 16.04 (assignment contribution) = 58.96 on the capstone project.

Capstone Project Evaluation Criteria

1. Data Integration (20/100): Effective data flow and synchronization between PostgreSQL and BigQuery.
2. Data Transformation and Git Upload (50/100): Quality and accuracy of data transformations, table creation in staging and reporting schemas, use of Git for project code management, and Python notebooks uploaded to GitHub.
3. Visualization (30/100): Quality, interactivity, and insightfulness of visualizations using Tableau Public and Metabase, with each contributing 20 points across various visualizations.