Social Media Analytics

1. Course Overview

Social Media Analytics introduces learners to the fundamentals of collecting, analysing, and interpreting data from platforms such as Facebook, Instagram, LinkedIn, YouTube, TikTok, and X. The course builds digital analytics literacy and prepares learners to measure performance, track engagement, understand audience behaviour, and generate insights that support marketing and business decisions.

2. Course Outcomes

By the end of this course, learners will be able to:

• Understand key social media metrics, data sources, and platform analytics tools
• Analyse engagement, reach, impressions, and audience behaviour
• Use dashboards and analytics tools to interpret social performance
• Apply basic data cleaning and preparation techniques for social datasets
• Build simple social media reports using spreadsheets or BI tools
• Identify KPIs for different social media goals (brand awareness, engagement, conversions)
• Communicate insights using clear, evidence‑based narratives
• Understand ethical considerations in social data collection and usage

3. Target Audience

• Social media executives and digital marketers
• Content creators and brand managers
• Early‑career analysts entering digital or marketing analytics
• Entrepreneurs managing their own social channels
• Anyone who wants to build foundational skills in social media analytics

4. Level

Beginner: No prior analytics experience required.

5. Pre‑Requisites

• Basic computer literacy (Excel/Sheets helpful but not mandatory)
• Familiarity with social media platforms (Instagram, Facebook, LinkedIn, etc.)
• Curiosity about data, content performance, and digital behaviour

6. What You Should Be Able to Do After Completing This Course

Learners will be able to:

• Work with social media datasets confidently
• Calculate and interpret foundational social media metrics (reach, impressions, engagement rate, CTR)
• Clean and prepare raw social data for analysis
• Build simple dashboards or reports using Excel/Sheets or BI tools
• Identify data quality issues and apply basic governance principles
• Support marketing decision‑making with evidence rather than intuition
• Progress into advanced analytics courses (paid ads analytics, influencer analytics, sentiment analysis, AI in marketing)