Google Sheets Mastery: Essential Skills for Modern Work and Business

This focused 3-hour course equips learners with practical and professional skills to work confidently in Google Sheets, one of the most powerful cloud-based spreadsheet tools. Participants will explore how to organize data, use essential functions, collaborate in real-time, and automate tasks—making them more productive in business, finance, project management, marketing, and more. Ideal for professionals and teams looking to get more out of Google Workspace.

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Navigating the Google Sheets interface

Creating, saving, and sharing files in Google Drive

Keyboard shortcuts and quick tips

Module 1: Getting Started with Google Sheets

Entering and formatting data

Sorting, filtering, and using freeze panes

Key functions: SUM, AVERAGE, IF, VLOOKUP, COUNTIF

Data validation and drop-downs

Module 2: Working with Data and Functions

Creating charts (line, bar, pie)

Conditional formatting and color scales

Real-time collaboration and comments

Version history and protection features

Module 3: Visualization and Collaboration

Importing data from other Sheets or sources

Using Explore and built-in AI features

Intro to macros and basic automation

Templates for business use (budgets, schedules, trackers)

Module 4: Productivity Tools & Automation

Master Google Sheets for work & biz! Learn formulas, data analysis, automation & collaboration. Boost productivity with cloud-based spreadsheet skills.

Google Sheets Pro: Cloud-Based Spreadsheet Skills for Work and Business

Introduction to Cybersecurity

Introduction to Cybersecurity: Protecting the Digital World.
In today’s hyper-connected era, cybersecurity is no longer optional, it’s essential. Introduction to Cybersecurity provides a foundational understanding of digital threats, defense strategies, and best practices to safeguard systems, networks, and data. Whether you're an aspiring IT professional, a business owner, or a concerned digital citizen, this course equips you with the knowledge to navigate the evolving landscape of cyber threats and implement robust security measures.

Lesson 1: Understanding Cybersecurity: Importance and Key Concepts

Lesson 2: History of Cyber Threats: Major Attacks and Lessons Learned

Lesson 3: CIA Triad: Confidentiality, Integrity, and Availability

Lesson 4: Cybersecurity Roles and Career Paths

Activity: Analyze a real-world data breach case study

Module 1: Cybersecurity Fundamentals

Lesson 5: Malware Types: Viruses, Worms, Trojans, and Ransomware

Lesson 6: Social Engineering: Phishing, Vishing, and Baiting

Lesson 7: Network Attacks: DDoS, MITM, and DNS Spoofing

Lesson 8: Web Application Vulnerabilities (SQLi, XSS)

Lab: Simulate a phishing attack in controlled environment

Module 2: Cyber Threats and Attack Vectors

Lesson 9: Firewalls and Intrusion Detection/Prevention Systems

Lesson 10: Encryption Basics: Symmetric vs Asymmetric Cryptography

Lesson 11: Authentication Methods: Passwords, MFA, and Biometrics

Lesson 12: VPNs and Secure Network Architectures

Lab: Configure a basic firewall rule set

Module 3: Defense Strategies and Technologies

Lesson 13: Hardening Windows and Linux Systems

Lesson 14: Patch Management and Vulnerability Scanning

Lesson 15: Secure Coding Principles

Lesson 16: Browser Security and Safe Web Practices

Activity: Perform system hardening on a virtual machine

Module 4: Operating System and Application Security

Lesson 17: TCP/IP Security Fundamentals

Lesson 18: Wireless Network Security (WPA3, Evil Twin)

Lesson 19: Cloud Security Considerations

Lesson 20: IoT Security Challenges

Lab: Analyze network traffic with Wireshark

Module 5: Network Security Essentials

Lesson 21: Security Operations Center (SOC) Overview

Lesson 22: Incident Response Lifecycle

Lesson 23: Digital Forensics Fundamentals

Lesson 24: Disaster Recovery and Business Continuity

Case Study: Examine a real incident response scenario

Module 6: Cybersecurity Operations

Lesson 25: Cybersecurity Laws and Regulations (GDPR, HIPAA)

Lesson 26: Ethical Hacking and Penetration Testing

Lesson 27: Cybersecurity Ethics and Professional Conduct

Lesson 28: Risk Management Frameworks

Activity: Create a basic compliance checklist

Module 7: Legal, Ethical, and Compliance Aspects

Lesson 29: AI in Cybersecurity: Threats and Protections

Lesson 30: Future of Cybersecurity: Quantum Computing and Beyond

Module 8: Emerging Trends and Capstone Project

Learn Cybersecurity basics: threats, defenses & best practices to protect data. Covers hacking, encryption, firewalls & risk management. Stay secure! #CyberSafety

Discovering Ethical AI

Discovering Ethical AI: Principles, Challenges, and Responsible Innovation.
Artificial Intelligence (AI) is transforming industries, from healthcare to finance, but its rapid advancement raises critical ethical concerns. Discovering Ethical AI is an in-depth exploration of the moral, social, and technical challenges surrounding AI development and deployment. This course equips learners with the knowledge to build fair, transparent, and accountable AI systems while addressing biases, privacy risks, and societal impacts.
Enroll now to shape the future of ethical AI!

Lesson 1: What is Ethical AI? Definition and Importance

Lesson 2: Historical Context: AI’s Evolution and Ethical Concerns

Lesson 3: Key Ethical Frameworks (Utilitarianism, Deontology, Virtue Ethics)

Case Study: Real-world AI failures (e.g., biased hiring algorithms, facial recognition controversies)

Module 1: Introduction to Ethical AI

Lesson 4: Fairness – Preventing Discrimination in AI Systems

Lesson 5: Transparency & Explainability (XAI)

Lesson 6: Accountability & Responsibility in AI Decision-Making

Lesson 7: Privacy & Data Protection in AI (GDPR, CCPA)

Workshop: Analyzing an AI model for ethical risks

Module 2: Core Principles of Ethical AI

Lesson 8: Types of Bias (Data, Algorithmic, Human)

Lesson 9: Measuring and Mitigating Bias in Machine Learning

Lesson 10: Fairness Metrics (Statistical Parity, Equal Opportunity)

Hands-on Lab: Detecting bias in a dataset using Python (Fairness Indicators, AIF360)

Module 3: Bias and Fairness in AI

Lesson 11: The "Black Box" Problem in AI

Lesson 12: Techniques for Explainable AI (LIME, SHAP, Decision Trees)

Lesson 13: Regulatory Requirements for AI Transparency

Case Study: Medical AI – Why doctors need explainable models

Module 4: AI Transparency and Explainability

Lesson 14: Data Ethics – Consent, Ownership, and Anonymization

Lesson 15: Secure AI – Preventing Adversarial Attacks

Lesson 16: Federated Learning & Differential Privacy

Debate: Should AI have access to personal data?

Module 5: Privacy, Security, and Trust in AI

Lesson 17: Global AI Regulations (EU AI Act, U.S. Executive Orders)

Lesson 18: Corporate AI Ethics Boards & Best Practices

Lesson 19: Ethical AI in Military, Surveillance, and Autonomous Weapons

Guest Lecture: AI Policy Expert on Future Regulations

Module 6: AI Governance and Policy

Lesson 20: How AI Can Address Climate Change, Healthcare, and Poverty

Lesson 21: Ethical Challenges in Humanitarian AI

Project: Propose an AI solution for a social issue (e.g., disaster response, education)

Module 7: AI for Social Good

Lesson 22: AI and Human Rights – Long-term Risks & Safeguards

Lesson 23: Artificial General Intelligence (AGI) – Ethical Implications

Lesson 24: Building a Career in Ethical AI (Roles, Certifications, Organizations)

Module 8: The Future of Ethical AI

Case Study Analysis (Group discussions

Hands-on Labs (Bias detection, Explainability tools)

Certification (Awarded upon completion)

Assessment & Certification

Explore Ethical AI principles, biases, fairness, and accountability in AI systems. Learn responsible AI development for a trustworthy future. #AIethics

Programming for Data Science with Python

Data Science is a rapidly growing field that leverages programming, statistics, and domain expertise to extract insights from data. Python has emerged as the leading language for Data Science due to its simplicity, versatility, and powerful libraries. This course provides a comprehensive introduction to Python programming tailored for data analysis, manipulation, and visualization.
By the end of this course, you'll have the skills to manipulate, analyze, and visualize data efficiently, making you ready for advanced Data Science and Machine Learning challenges.

Welcome to the Program

Introduction to SQL

Command Line Essentials

Introduction to Python

Learn Python programming for Data Science! Master data analysis, visualization, and manipulation with libraries like Pandas, NumPy, and Matplotlib.

Best Data Science Courses at lowest price on Learneur

Data Science & AI Fundamentals: From Zero to Pro

This foundational course introduces learners to the world of Data Science and Artificial Intelligence (AI) from the ground up. Without requiring any prior technical background, the course guides participants through essential concepts, tools, and workflows used by data professionals. By the end, learners will understand how to collect, analyze, and draw insights from data, and how AI and machine learning are applied in real-world scenarios. The course includes hands-on demonstrations and interactive exercises using Python and open-source libraries.

What is Data Science? What is AI?

Data vs. Information vs. Knowledge

Real-world use cases and impact

Module 1: Introduction to Data Science & AI

Data lifecycle: Collection, Preparation, Analysis, Modeling, Deployment

Introduction to Jupyter Notebook & Python

Overview of pandas, NumPy, and Matplotlib

Module 2: Data Science Workflow & Tools

Loading datasets (CSV, Excel)

Data exploration and cleaning

Descriptive statistics and data summaries

Module 3: Working with Data in Python

Creating bar charts, histograms, scatter plots

Identifying trends and outliers

Telling a story with data

Module 4: Visualizing Data for Insight

What is machine learning?

Supervised vs. unsupervised learning

Building a simple model (e.g., linear regression or classification) with scikit-learn

Evaluating model accuracy and making predictions

Module 5: Basics of Machine Learning & AI

Ethics in AI and responsible use

Data bias and fairness

Careers in data science & AI

Resources for continued learning

Module 6: Ethics, Careers, and Next Steps

Data Science & AI Fundamentals: From Zero to Pro is a comprehensive course designed to equip learners with essential skills in data analysis, machine learning, and AI concepts.

Cybersecurity Beginner Course – 6-Hour Intensive

This 6-hour beginner-level course provides a practical and foundational understanding of cybersecurity. Designed for those new to the field, it covers the essential concepts, threats, tools, and best practices for staying safe in a digital world. The course combines theory with real-life examples to help participants recognize common cyber threats and learn how to protect personal and organizational data effectively.

What is cybersecurity and why it matters

Brief history of cyber threats

Overview of cyberattack types and trends

Module 1: Introduction to Cybersecurity

Malware, viruses, ransomware, spyware

Phishing and spear phishing

Social engineering attacks

Module 2: Understanding Common Threats

Importance of software updates and antivirus

Creating strong passwords and using password managers

Safe browsing and secure internet habits

Module 3: Basic Cyber Hygiene

Securing home and office Wi-Fi

Two-factor authentication (2FA) basics

Using VPNs and firewalls

Module 4: Device and Network Security

Data privacy and GDPR basics

Safe use of social media and public Wi-Fi

Backups and recovery planning

Module 5: Protecting Personal Data

Real-world scenarios and how to respond

Building a personal or small business security checklist

Final Q&A and recap of key takeaways

Module 6: Practical Tips & Final Recap

The Cybersecurity Beginner Course is a 6-hour intensive program designed to equip participants with foundational knowledge and skills in cybersecurity practices.

Technology is Not the Solution for Cyber Crime | Safeguard Against Cyberattack

This course challenges the common misconception that technology alone can solve the growing problem of cybercrime. It explores the critical role of human behavior, policies, and strategic awareness in preventing cyberattacks. Learners will gain a holistic understanding of cybersecurity, focusing not just on tools and systems, but also on governance, risk management, and proactive defense mechanisms. Through case studies, real-world examples, and interactive exercises, participants will learn how to build resilient cybersecurity frameworks beyond mere technological solutions.

Definitions and myths about cybercrime

Why technology alone fails

Cybercrime trends and threat landscape

Module 1: Introduction to Cybercrime Beyond Technology

Social engineering and phishing attacks

Insider threats and employee negligence

Behavioral psychology of users

Module 2: The Human Element in Cybersecurity

Poor governance and lack of policies

Risk management frameworks (NIST, ISO 27001)

Role of leadership and corporate culture

Module 3: Organizational Vulnerabilities

Security training and education programs

Incident response planning and drills

Case study: Human-driven breach incidents

Module 4: Building a Culture of Cybersecurity Awareness

Multi-layered defense strategy

Module 5: Integrating Technology with Policy and Practice

Technology alone cannot prevent cybercrime; effective safeguarding requires a combination of awareness, training, and robust security practices to mitigate risks.

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