Professional Certificate in Data Science (Cybersecurity)

Trainees who have successfully completed the program are eligible to use the post-nominal title PCiDS, subject to verification and upon issuance of the official certificate. Current and potential employers or educational institutions may contact Data Chord to verify the authenticity of the PCiDS certificate and the Accreditation certificate.

Schedule for full certification:

• Date: 18 May - 7 June 2025
• Type: Online
• Commitment:  5 evenings of 3-hours session across 7 Days in a Week. Full program runs across 3 weeks. Remaining hours for self-directed learning.

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This foundational course introduces the core concepts of Artificial Intelligence (AI), Data Science, Personal Data Protection (PDP) & Ethical Analytics, basic data integration (ETL), and fundamental Machine Learning (ML) techniques. It is designed not only to build critical technical skills but also to ensure compliance with industry regulations and quality standards required for ISO certification. The course emphasizes clear learning outcomes, practical exercises, continuous assessment, and quality documentation.

This module introduces trainees to the fundamentals of Artificial Intelligence and Data Science, covering key concepts such as types of AI, real-world applications, and the full Data Science lifecycle—from problem definition to model deployment. It also builds interdisciplinary foundations by introducing essential statistical methods like Bayesian Inference and Markov Chains, which are critical for data-driven decision making. By understanding how AI and Data Science intersect and operate in practical settings, trainees gain the conceptual and analytical grounding needed to navigate modern technology environments. This foundational knowledge not only enhances their technical literacy but also strengthens their ability to engage in data-centric roles across industries, opening pathways to careers in cybersecurity, analytics, and digital transformation. 

Topics Covered:

• Fundamentals of AI:
  • What is Artificial Intelligence?
  • Types of AI (e.g., Narrow AI, General AI, Superintelligent AI)
  • Examples and real-world applications (e.g., recommendation systems, autonomous vehicles)
• Data Science Essentials:
  • Definition and scope of Data Science
  • The Data Science lifecycle—from problem statement to model deployment
  • Key distinctions between Data Science and AI
• Interdisciplinary Foundations:
  • Basic statistics and mathematical methods (e.g., Bayesian Inference, Markov Chains)
  • How these techniques underpin AI and data-driven decision making

Learning Outcomes:

• Articulate basic definitions and applications of AI and Data Science.
• Understand the steps of the Data Science lifecycle.
• Apply fundamental statistical concepts to real-world examples.

This module equips trainees with essential knowledge in Personal Data Protection (PDP), data privacy regulations, and ethics in data analytics. It explores the role of PDP in safeguarding individual rights within today’s data ecosystem and introduces major global regulations such as GDPR, CCPA, and Vietnam’s PDPD. Trainees also learn key ethical frameworks—including utilitarianism, deontology, and virtue ethics—to guide responsible data usage and mitigate biases in analytics. By understanding both legal and moral obligations surrounding data, trainees are better prepared to design and implement analytics solutions that are not only effective but also compliant and ethically sound. These competencies are increasingly critical across industries where data protection and ethical decision-making are integral to building trust and ensuring long-term sustainability. 

Topics Covered:

• Personal Data Protection (PDP):
  • Overview and significance of personal data protection
  • Role of PDP in today’s data ecosystem
  • Key trends such as the emphasis on individual rights and advanced protective technologies
• Data Privacy Laws & Regulations:
  • Examination of prominent laws (e.g., GDPR, CCPA, LGPD, Vietnam PDPD)
  • How these regulations impact data collection, processing, and storage
• Ethics in Data Analytics:
  • Moral principles (utilitarianism, deontology, virtue ethics) governing data use
  • Best practices for ethical decision making in analytics
  • Strategies to identify and mitigate algorithmic biases in data-driven projects

Learning Outcomes:

• Recognize the importance of PDP in protecting individual rights and data integrity.
• Evaluate the impact of key data privacy regulations on organizational practices.
• Integrate ethical considerations into data analytics workflows.

This module provides trainees with foundational skills in data integration and ETL (Extract, Transform, Load) processes, essential for managing diverse data sources in cybersecurity and analytics workflows. Trainees learn to handle different data types—structured, semi-structured, and unstructured—and practice converting and cleaning data through Python-based labs. Emphasis is placed on building automated, error-resistant data pipelines and applying anomaly detection techniques to ensure data quality and reliability. These hands-on skills are directly transferable to roles requiring data preparation and validation, enabling trainees to support accurate analytics and make informed, secure decisions in data-driven environments. 

Topics Covered:

• Data Integration Fundamentals:
  • Overview of data types (structured, semi-structured, unstructured)
  • Importance and benefits of consolidating data into a centralized repository
• ETL Process Breakdown:
  • Extraction: Accessing data from disparate sources
  • Transformation: Converting semi-structured data into structured formats and cleaning data for consistency
  • Loading: Importing transformed data into target databases/data lakes
• Practical Exercises:
  • Hands-on labs using tools (e.g., Python-based scripts) to convert data files, standardize timestamps, and extract date/time components
  • Developing simple data pipelines that emphasize automation and error checking
• Data Quality and Anomaly Detection:
  • Techniques for identifying data anomalies that could affect downstream analytics

Learning Outcomes:

• Build and execute basic ETL pipelines.
• Develop strategies for data cleaning and transformation to maintain data quality.
• Implement fundamental anomaly detection techniques to secure and validate data.

This module introduces trainees to the core principles and practical applications of Machine Learning (ML) within the broader context of AI. It covers the complete ML lifecycle—from data ingestion and preprocessing to model training, tuning, and deployment—alongside automation techniques that streamline repetitive tasks. Trainees explore different types of ML, including supervised, unsupervised, and reinforcement learning, and learn how these approaches can be applied to detect anomalies and vulnerabilities in cybersecurity settings. By mastering these skills, trainees are equipped to develop intelligent systems that enhance threat detection and decision-making, positioning them for roles in AI-driven cybersecurity and data analytics environments.

Topics Covered:

• Overview of Machine Learning:
  • Role of ML in the broader AI landscape
  • Comparative analysis of AI, ML, and Deep Learning
• ML Workflow Essentials:
  • Data ingestion and preprocessing
  • Model training, experimentation, and deployment
  • Automating repetitive tasks in the ML lifecycle, such as hyperparameter tuning and model serving
• Types of Machine Learning Techniques:
  • Supervised Learning: Classification and regression methods using labeled data
  • Unsupervised Learning: Clustering and association rule learning with unlabeled data
  • Reinforcement Learning: Trial-and-error approaches for optimal decision making
• Application in Cybersecurity:
  • Basic approaches to vulnerability and anomaly detection using ML
  • Examples of how ML algorithms can identify suspicious patterns and reduce risks

Learning Outcomes:

• Understand the complete lifecycle of an ML project and implement a basic ML pipeline.
• Differentiate between supervised, unsupervised, and reinforcement learning techniques.
• Apply ML algorithms to fundamental cybersecurity and data quality challenges.

Learners will take the assessment for Certificate of Completion (Anchor Level).

This Intermediate course introduces essential concepts of graph theory—including classification, representation methods, traversal algorithms, and key graph properties—and applies these principles to real-world cybersecurity challenges. The curriculum combines theoretical instruction with hands-on lab exercises (using tools such as Python’s NetworkX library) and detailed documentation to ensure quality and traceability for ISO audit compliance.

This module provides a comprehensive introduction to graph theory, a critical area in understanding complex relationships in cybersecurity and data science. Trainees learn essential terminology—such as nodes, edges, and weights—and explore various graph classifications, including directed vs. undirected and cyclic vs. acyclic structures. The module also covers practical representation methods (like adjacency matrices and lists), traversal algorithms (BFS and DFS), and pathfinding techniques using Dijkstra’s Algorithm. By mastering these concepts and applying them through hands-on exercises, trainees gain the analytical skills needed to model and analyze interconnected systems, laying the groundwork for advanced applications such as attack graph construction, network analysis, and vulnerability mapping in cybersecurity. 

Topics Covered:

• What is Graph Theory?
  • Definitions: Nodes (vertices), Edges, Weights, and Graph Terminology
  • Basic examples (e.g., the “love triangle” scenario)
• Classification of Graphs:
  • Directed vs. Undirected: Understanding one-way and mutual connections
  • Weighted vs. Unweighted: Importance of edge weights in practical scenarios
  • Cyclic vs. Acyclic: Identifying cycles, Directed Acyclic Graphs (DAGs) for scheduling and blockchain
  • Special Graphs: Trees (rooted trees, binary trees), bipartite graphs, and planar graphs
• Graph Representation Methods:
  • Adjacency Matrix and List: Pros and cons, use cases in computer science
  • Practical comparison using example datasets
• Graph Properties and Metrics
  • Connectedness, path lengths (shortest path), cycles, and centrality measures
  • Introduction to fundamental algorithms: Dijkstra’s Algorithm for pathfinding
• Graph Traversal Algorithms:
  • Breadth-First Search (BFS): Level-order exploration
  • Depth-First Search (DFS): Deep recursive exploration
  • Algorithmic pseudocode and step-by-step examples

Learning Outcomes:

• Define key graph theory concepts and apply standard terminology.
• Classify and represent different types of graphs accurately.
• Implement basic traversal algorithms (BFS and DFS) and compute the shortest path using Dijkstra’s Algorithm.
• Analyze and evaluate graph properties to support various computing problems.

 This module bridges graph theory with practical cybersecurity applications, equipping trainees to analyze complex digital threats through visual and structural models. It covers how to represent computer networks as graphs, where devices and data flows become nodes and edges, and how to distinguish between directed and undirected connections. Trainees learn to construct attack trees and graphs to map cyberattack scenarios such as phishing or insider threats, and apply graph-based machine learning for anomaly detection. With hands-on use of Python’s NetworkX library, they gain experience in building and analyzing network structures, calculating centrality, and identifying potential vulnerabilities. These skills enable trainees to visualize cyber risks, strengthen threat modeling capabilities, and contribute to proactive cybersecurity defense strategies. 

Topics Covered:

• Relevance of Graph Theory in Cybersecurity:
  • Modeling complex cyber relationships—attack paths, threat structures, and network vulnerabilities.
  • Applications include threat detection, intrusion detection, fraud identification, and blockchain security.
• Representing Computer Networks as Graphs:
  • Modeling network devices (e.g., computers, routers, IoT devices) as nodes and communication links as edges.
  • Differentiating directed graphs (to show data flow) versus undirected graphs (for mutual connectivity).
• Threat Modeling and Attack Graphs:
  • Attack Trees vs. Attack Graphs:
    • Hierarchical representation of attacker goals and detailed steps in a cyberattack.
    • Case studies covering advanced persistent threat (APT) scenarios and social network fraud detection.
• Graph-Based Machine Learning in Cybersecurity:
  • Applying graph neural networks (GNNs) and centrality analysis for threat prediction and anomaly detection.
• Hands-On: Building Cybersecurity Graphs:
  • Exercise 1: Draw simple network graphs representing real-world communication networks with undirected/directed edges.
  • Exercise 2: Construct attack graphs to model phishing attacks, insider threats, or ransomware scenarios.
  • Exercise 3: Utilize Python’s NetworkX library to develop, analyze, and visualize cybersecurity graphs (including computation of centrality measures and shortest path analysis).
  • Code Walkthrough: Detailed step-by-step demonstration of scripts that build a network graph, detect vulnerable nodes, and highlight suspicious data flows.

Learning Outcomes:

• Explain the role of graph theory in analyzing complex cybersecurity scenarios.
• Construct both directed and undirected graphs to accurately model computer networks.
• Develop attack trees and graphs to represent potential cyberattacks and threat scenarios.

Utilize graph analytics (centrality measures, shortest path algorithms) using Python to identify vulnerable nodes and potential attack paths. 

Learners will take the assessment for Certificate of Completion (Intermediate Level).

This course equips learners with the skills to transform complex datasets into insightful visual representations while also developing expertise in merging and analyzing cybersecurity data. The first module introduces key principles and techniques for effective data visualization, and the second module focuses on advanced data analytics tailored to cybersecurity challenges. The curriculum integrates theoretical instruction with practical hands‐on exercises (using tools such as Power BI and Python) and detailed documentation to meet ISO audit compliance through traceable, well-documented processes.

This module introduces trainees to the fundamentals of data visualization, emphasizing its role in simplifying complex datasets for clearer interpretation and decision-making. It covers a variety of visual formats—such as scatter plots, bar charts, line charts, heat maps, and histograms—and explains how to choose the right visual based on the data and analytical goals. Trainees gain hands-on experience with tools like Power BI, learning how to load data, build dashboards, and apply best practices for clarity, accessibility, and impact. By mastering these techniques, trainees enhance their ability to communicate data-driven insights effectively—an essential skill in cybersecurity, business analytics, and beyond. 

Topics Covered:

• What is Data Visualization?
  • The practice of representing complex data with visual elements to enable easy interpretation and insight discovery.
  • Overview of transforming datasets into visual representations such as charts, graphs, maps, and dashboards.
• Advantages of Data Visualization:
  • Enhances understanding, enables rapid insight discovery, and supports effective communication of data-driven findings.
• Common Data Visualization Types:
  • Scatter Plots: Display relationships between variables and indicate dominance ratios.
  • Bar Charts: Compare incident types and severity levels (e.g., SIRT case records).
  • Line Charts: Illustrate trends over time (e.g., threat intelligence tracking).
  • Heat Maps and Histograms: Visualize user behavior patterns and distribution of events.
• Data Visualization Tools:
  • Overview of popular tools (such as Power BI Desktop) with guidance on installation and initial configuration.
• Best Practices:
  • Guidelines for clarity, labeling, consistency, and accessibility.
  • Practical steps for data loading, chart selection, and interpretation of visual cues.

Learning Outcomes:

• Define data visualization and explain its role in translating complex data into understandable insights.
• Identify various visualization types and select the appropriate method for different analytical scenarios.
• Utilize data visualization tools to create clear and interactive visual representations.
• Apply best practices to ensure visualizations are effective and communicate the intended messages. 

This module equips trainees with advanced data analytics skills tailored to cybersecurity contexts. It begins by clarifying the distinction between master and secondary data and emphasizes their roles in centralized security analysis. Trainees gain practical experience in ingesting and integrating diverse data sources using ETL techniques and join operations, enabling the transformation of raw logs into structured, actionable insights. The module also introduces key statistical methods—Markov Chain analysis, Bayesian inference, and chi-square testing—for detecting anomalies and modeling cyber threats. Through hands-on use of tools like Power BI, trainees learn to build dynamic dashboards that support real-time monitoring and strategic decision-making. These capabilities are essential for professionals aiming to manage cybersecurity data at scale and contribute to intelligent, evidence-based threat prevention. 

Topics Covered:

• Master Data vs. Secondary Data in Cybersecurity:
  • Definitions, differences, and their roles within a cybersecurity analytics framework.
  • Overview of the security_config file as master data and its importance for centralized security management.
• Data Ingestion and Integration:
  • Ingesting master data (e.g., security_config.csv) into Power BI.
  • Performing ETL processes on unstructured data to generate structured analytic tables.
  • Joining secondary data with the master dataset through various join operations (left, right, inner, full outer, and anti joins).
• Benefits of Data Integration:
  • Simplified management, enhanced automation, improved auditing, consistent enforcement, and better root-cause analysis.
• Advanced Analytical Methods in Cybersecurity:
  • Markov Chain Analysis: Modeling state transitions and behavioral patterns for intrusion detection.
  • Bayesian Inference: Dynamically updating threat probabilities and supporting spam/phishing detection.
  • Chi-Square Testing: Evaluating statistical discrepancies between expected and observed cybersecurity event frequencies.
• ETL for Unstructured Data:
  • Techniques for converting raw log files and unstructured data into a structured format.
  • Practical challenges such as delimiter detection, renaming fields, and data cleansing.
• Visualization and Reporting in Security Analytics:
  • Integrating joined data to generate comprehensive dashboards for monitoring security posture.
  • Using advanced analytics to derive actionable insights and predictive trends.

Learning Outcomes:

• Distinguish between master and secondary data in cybersecurity, and understand their significance in a unified analytic framework.
• Execute ETL processes and join operations to integrate diverse cybersecurity data sources.
• Apply statistical methods such as Markov Chains, Bayesian Inference, and Chi-Square tests to analyze cybersecurity events.
• Develop and present comprehensive reports and dashboards that drive proactive threat management and enhance policy enforcement.

Learners will take the assessment for Certificate of Completion (Advanced Level).

This is the final assessment for the qualification round of the Professional Certificate in Data Science (Cybersecurity). All final projects are reviewed by an Expert Assessment Panel comprising seasoned professionals with over 20 years of experience in data science.

While there are no strict entry requirements, 

• a background in IT or data science would be beneficial, and
• a display of certain level of English proficiency, and
• at least 18 years old

We welcome learners from all backgrounds to join us in this training program.

At this moment, we are preparing for the next level of training: the Professional Advanced Certificate in Data Science.

•  Data Analyst - threathunting 
•  Data Analyst – Cybersecurity (SIEM)