Social Media Data Analytics

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Social Media Data Analytics

Coursera · Intermediate ·📊 Data Analytics & Business Intelligence ·3mo ago

Key Takeaways

Extracts insights from social media data using data analytics techniques

Original Description

Mastering Social Media Data for Business Insights is an intermediate-level course designed for marketing professionals, data analysts, and business intelligence specialists who want to transform social media noise into strategic business intelligence. In today's digital landscape, organizations need professionals who can extract meaningful insights from the 2.5 quintillion bytes of social media data generated daily. This course equips you with the technical skills and strategic mindset to collect, analyze, and visualize social media data effectively using Python, APIs, and advanced analytics tools. You'll learn to identify trends across Twitter, Instagram, and LinkedIn, create compelling data visualizations that drive action, and implement predictive models that anticipate customer behavior. Through real-world case studies from companies like Amazon, McDonald's, and educational institutions, you'll discover how social media analytics drives measurable business outcomes. The course combines video lessons, hands-on coding exercises, and a comprehensive capstone project where you'll develop a complete social media analytics strategy. Whether you're looking to enhance your current analytics capabilities or transition into social media intelligence, this course provides the foundation to turn social media data into competitive business advantages.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
Tracking Macroeconomic Indicators with the Finance Toolkit
Learn to track macroeconomic indicators using the Finance Toolkit and understand its importance in global economic trends
Dev.to · Jeroen Bouma
📰
Pydantic for Data Engineering: Schema Validation in ETL & Pipeline Contracts
Use Pydantic for schema validation in ETL pipelines to ensure data consistency and quality
Dev.to · Gowtham Potureddi
📰
Half of Data Engineering Jobs on LinkedIn Aren't Real
Understand the discrepancy between reported data engineering job growth and actual job availability on LinkedIn
Dev.to · DataDriven
📰
Evolutionary Data Through Schemaboi: Achieving Forward, Backwards, and Sideways Compatibility
Learn how Schemaboi achieves forward, backwards, and sideways compatibility for evolutionary data through self-contained schemas in file headers
InfoQ AI/ML
Up next
6-Phase SQL Roadmap 2026 | Data Analytics & Engineering | #shorts
SCALER
Watch →