| Recommended or required reading | Required textbooks: – “Web Analytics 2.0” by Avinash Kaushik – “Social Media Analytics: Effective Tools for Building, Interpreting, and Using Metrics” by Marshall Sponder – “Social Media Analytics in Predicting Consumer Behavior” by Selay Ilgaz Sumer, Nurettin Parilti Recommended resources: Johannes Ledolter, Data Mining and Business Analytics with R: https://catalyst.library.jhu.edu/catalog/bib_4637122 Resourses for workshops: Tural Naghiyev https://rpubs.com/QemiloLink for presentatitions: https://www.canva.com/Link for workshop Datasets: https://github.com/ Course reading is composed of articles, laws as well as book chapters. Additional information will be distributed either electronically or delivered in printed forms. |
| Planned learning activities and teaching methods | Classroom lecturing, case study discussions and brainstorming, feedback and presentation sessions, discussion sessions, gamification on survey, Dashboards with Excel, R, Tableau, Power BI, Tableau, OrangeTool Data Mining | ||
| Language of instruction | English |
| Course Contents: | |
| 1. | Introduction to Social and Web Analytics Course Introduction and Importance of AnalyticsCourse overview, objectives, and expectationsImportance of analytics in the digital ageThe Web Analytics Career Laboratory work: Analyze various websites Working tools: RStudio, MS Excel, Power BI |
| 2. | Basics of Social Media Metrics Understanding Social Media Platforms and MetricsUnderstanding key social media platformsIntroduction to metrics like likes, shares, and commentsHow to set measurable goals for social media. Laboratory work: AB Testing Working tools: RStudio, Excel, PowerBI |
| 3. | Data Collection and Sources Data Collection Methods and SourcesMethods for collecting data from social media and websitesIntroduction to APIs for data retrievalEthical considerations in data collection. Laboratory work: Data collection via API (with API key and without API key) Working tools: RStudio, Excel, PowerBI |
| 4. | Analytics Tools and Software Introduction to Analytics ToolsIntroduction to analytics tools like Google Analytics, Facebook InsightsSetting up analytics accountsNavigating and using analytics dashboards. Laboratory work: Rstudio, Python with Visual Code, Tableau, OrangeTool Data Mining tools introduction Working tools: Rstudio, Python with Visual Code, Tableau, OrangeTool |
| 5. | Advanced Social Media Analytics Advanced Metrics and Sentiment AnalysisDeeper dive into metrics like engagement rate, reach, and impressionsSignificance and interpretation of advanced metricsTechniques and tools for sentiment analysis. Laboratory work: Workshop I: Sentiment Analysis Workshop II: Social listening tools Workshop III: Make dashboard for website data Workshop IV: Top 8 Sentiment Analysis Datasets in 2023 and research Working tools: Rstudio, Python with Visual Code, Tableau |
| 6. | Competitor Analysis and Social Listening Tools Competitor Analysis and Social ListeningStrategies for analyzing competitors’ social media strategies and performance.Practical competitor analysis exercisesIntroduction to social listening tools. Laboratory work: Workshop I: Competitor Analysis in Action Workshop II: Social Listening practice Workshop III: Strategy Development Workshop IV: SWOP analysis Working tools: Rstudio, Python, Tableau, OrangeTool |
| 7 | Web Analytics and SEO Introduction to Web Analytics and SEO FundamentalsBasics of web analyticsIntroduction to tools like Google AnalyticsSetting up web analytics for a websitePrinciples of Search Engine Optimization (SEO) Laboratory work: Workshop I: List all google products and dive deep Google Analytcis Workshop II: Making google Analytics account Workshop III: Making dashboard with sample google analytic account Working tools: Rstudio |
| 8. | MIDTERM EXAM |
| 9. | Keyword Research and Content Optimization Keyword Research and Content OptimizationPerforming keyword research.Integrating keywords with web analytics data.Content optimization for search engines. Data Visualization and Reporting Data Visualization Tools and DashboardsIntroduction to data visualization tools like Tableau, Power BI, etc.Creating compelling visualizations.Building interactive dashboards for data presentation. Laboratory work: Workshop I: Data visualization with Tableau, Power BI Workshop III: Making dashboard with R Flexdashboard library Working tools: Rstudio, Tableau, Power BI |
| 10. | Report Writing and Presentation Report Writing and PresentationWriting clear and concise reports from analytics dataEffective communication of analytics findingsPresenting data to stakeholders. Laboratory work: Sample Report Writing |
| 11. | Real-world Case Studies and Ethics Real-world Case Studies and Ethical ConsiderationsAnalyzing real-world case studies of successful analytics implementationsDrawing insights from case studies.Ethical implications of data collection and analysis. Laboratory work: Semantic tree analysis Working tools: Rstudio, OrangeTool Data Mining |
| 12. | Emerging Trends in Analytics AI and Machine Learning in Analytics and Future Trends Gartner Identifies the Top 10 Data and Analytics Trends for 2023 How AI and machine learning are transforming analyticsEmerging trends in social and web analyticsPreparing for the future of analytics. Laboratory work: Social influencer identification Working tools: Rstudio, R |
| 13. | Group Projects and Guest Speakers Group Projects and Guest SpeakersAssigning group projects for applying analytics knowledge.Inviting industry professionals as guest speakers to share insights.Group project brainstorming and planning. |
| 14. | Group Project Workshops and Final Preparations Group Project Workshops and Final PreparationsHands-on workshops to guide students in their group projects.Instructor feedback on project proposals.Group project progress updates and discussions.Final exam preparation and study session. |
| 15. | GROUP ASSIGNMENT AND PRESENTATIONS |
| FINAL EXAM |