Social Media Marketing

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1. Basic Computer & Internet Skills

Browsing the web
Creating and managing email accounts
Understanding how websites and browsers work

2. Understanding of Major Social Media Platforms

Facebook, Instagram, Twitter (X), LinkedIn, TikTok, Pinterest, YouTube
Their audience types and content formats (stories, reels, tweets, posts)

3. Good Communication Skills

Clear writing and visual messaging
Understanding audience tone and style
Optional: Basic English proficiency if you’re targeting global platforms

4. Basic Graphic Design Understanding (Optional but Helpful)

Knowing how to create or edit visuals for social media
Tools: Canva, Adobe Express, Figma, or Photoshop

5. Marketing Fundamentals (Helpful but Not Mandatory)

Understanding of:
- Target audience
- Brand positioning
- Marketing funnel (Awareness → Interest → Decision → Action)

6. Analytical Thinking

Ability to interpret social media metrics (likes, engagement, reach, etc.)
Comfort using tools like:
- Facebook Insights
- Instagram Analytics
- Google Analytics
- Meta Business Suite

7. Consistency & Time Management

Regular content posting and planning is crucial
Basic knowledge of content scheduling tools like Buffer, Hootsuite, Later

🧠 Bonus Skills That Boost Success

Knowledge of Social Media Ads (Facebook Ads, Instagram Boosts)

Copywriting Basics

Video Editing (e.g., CapCut, InShot, Adobe Premiere Rush)

Hashtag & Trend Research

About Instructor

Mehran

Dr. Mehran H. Bazargani is a researcher and educator specialising in machine learning and computational neuroscience. He earned his Ph.D. from University College Dublin, where his research centered on semi-supervised anomaly detection through the application of One-Class Radial Basis Function (RBF) Networks. His academic foundation was laid with a Bachelor of Science degree in Information Technology, followed by a Master of Science in Computer Engineering from Eastern Mediterranean University, where he focused on molecular communication facilitated by relay nodes in nano wireless sensor networks. Dr. Bazargani’s research interests are situated at the intersection of artificial intelligence and neuroscience, with an emphasis on developing brain-inspired artificial neural networks grounded in the Free Energy Principle. His work aims to model human cognition, including perception, decision-making, and planning, by integrating advanced concepts such as predictive coding and active inference. As a NeuroInsight Marie Skłodowska-Curie Fellow, Dr. Bazargani is currently investigating the mechanisms underlying hallucinations, conceptualising them as instances of false inference about the environment. His research seeks to address this phenomenon in neuropsychiatric disorders by employing brain-inspired AI models, notably predictive coding (PC) networks, to simulate hallucinatory experiences in human perception.

5 Courses