Research, Data Management and Data Science

Here's what you'll learn:

Module 1: Introduction to HR Metrics and Analytics

Module 2: Data Cleaning and Preparation

Module 3: Key HR Metrics

Module 4: Data Visualization Techniques

Module 5: Advanced Data Analysis Techniques

Module 6: Data Interpretation and Reporting

Module 7: Employee Survey Data Analysis

Module 8: Talent Management Analytics

Module 9: Compensation and Benefits Analysis

Module 10: Practical Applications and Case Studies

Here's What You'll Learn

• Develop mathematical models for vaccine-preventable diseases using R.
• Understand disease transmission dynamics and reproductive number (R0).
• Estimate model parameters and perform sensitivity analysis.
• Design and run simulations to explore vaccination strategies.
• Apply advanced modeling techniques, including age-structured models and stochastic approaches.
• Conduct scenario analysis and interpret model outcomes.
• Validate model predictions and perform uncertainty analysis.
• Create effective data visualizations and reports for model results.
• Link models to public health policy and evaluate vaccination program impacts.
• Stay updated with emerging trends and advances in disease modeling.

Here's What You'll Learn

• Analyze marketing data using R, including data acquisition, cleaning, and preprocessing.
• Conduct exploratory data analysis to identify patterns and trends in marketing data.
• Perform customer segmentation and profiling using cluster analysis and RFM analysis.
• Analyze marketing campaign performance, including attribution modeling and ROI analysis.
• Create effective data visualizations with Tableau, connecting R and using advanced visualization techniques.
• Apply predictive analytics techniques, such as churn prediction and customer lifetime value prediction.
• Analyze social media data with sentiment analysis and social network analysis.
• Conduct web analytics and optimize conversion rates using A/B testing.
• Perform sales data analysis, including sales forecasting and market basket analysis.
• Integrate multiple data sources and create interactive dashboards in Tableau.
• Apply advanced statistical methods, including hypothesis testing, multivariate analysis, and time series analysis.
• Build machine learning models for marketing applications using R.
• Use Geographic Information Systems (GIS) for marketing analysis and create spatial visualizations.
• Analyze real-world case studies and develop project plans for marketing analytics.
• Conduct scenario analysis and develop decision support systems in R.
• Understand data ethics, privacy regulations, and implement ethical considerations in data analysis.
• Utilize advanced Tableau techniques, including custom calculations and advanced chart types.
• Complete a capstone project that integrates all learned concepts, with data analysis and visualization.

Here's What You'll Learn:

• Module 1: Introduction to PowerBI
• Module 2: Data Modeling
• Module 3: DAX Functions and Expressions
• Module 4: Creating Reports
• Module 5: Dashboards and Sharing
• Module 6: Advanced Data Visualization Techniques
• Module 7: Data Insights and Analytics
• Module 8: PowerBI Service
• Module 9: Best Practices for Data Visualization
• Module 10: Case Study and Final Project

Here's What You'll Learn:

Module 1: Introduction to Survey Data and SPSS

Module 2: Data Preparation and Management in SPSS

Module 3: Descriptive Statistics for Survey Data

Module 4: Visualizing Survey Data

Module 5: Hypothesis Testing Fundamentals

Module 6: Comparing Means (T-tests and ANOVA)

Module 7: Analyzing Relationships Between Categorical Variables (Chi-Square)

Module 8: Correlation and Regression Analysis

Module 9: Reliability and Factor Analysis

Module 10: Advanced Topics and Reporting Survey Findings

Here's What You'll Learn:

Data management techniques, including importing, exporting, cleaning, and preparing datasets.

• Descriptive statistics and data exploration methods.
• Performing regression analysis, panel data analysis, and time series analysis.
• Conducting hypothesis testing and using advanced programming techniques in STATA.
• Applying econometric models and advanced data analysis techniques.
• Creating and customizing data visualizations.

Here's What You'll Learn:

• Develop effective research questions and hypotheses.
• Structure and write compelling manuscripts in the IMRaD format.
• Identify relevant journals and understand journal publication policies.
• Navigate the submission and revision process, including responding to reviewer comments.
• Understand ethical considerations in research and publication, such as authorship and plagiarism.
• Promote research publications and maximize their impact and visibility.
• Write different types of research publications, including review articles and grant proposals.
• Communicate scientific research effectively through oral presentations, posters, and outreach.

Here's What You'll Learn:

• Introduction: Differences between systematic and non-systematic reviews, formulating research questions, and identifying literature.
• Systematic Reviews: Steps, protocols, search strategies, and study selection.
• Non-Systematic Reviews: Types (narrative, scoping), search strategies, and strengths/limitations.
• Data Extraction: Techniques and tools for managing data.
• Critical Appraisal: Assessing study quality and addressing biases.
• Synthesis: Qualitative/quantitative methods, meta-analysis, and meta-synthesis.
• Reporting: Writing review reports, PRISMA guidelines, and ethical considerations.
• Advanced Topics: Living reviews, grey literature, and software tools.
• Applications: Using reviews in policy, practice, and guidelines.
• Future Trends: Innovations and emerging methodologies in literature reviews.

Here's What You'll Learn:

Module 1: Introduction to Genomics and Bioinformatics

Module 2: R Programming Fundamentals for Bioinformatics

Module 3: Working with Genomic Data Formats in R

Module 4: Quality Control and Pre-processing of Genomic Data

Module 5: Differential Expression Analysis (RNA-Seq)

Module 6: Introduction to Variant Calling and Annotation

Module 7: Population Genetics and GWAS (Conceptual)

Module 8: Introduction to Epigenomic Data Analysis

Module 9: Pathway and Network Analysis

Module 10: Introduction to Data Visualization with Tableau

Module 11: Connecting Genomic Data to Tableau

Module 12: Building Basic Visualizations in Tableau

Module 13: Advanced Visualizations for Genomic Data in Tableau

Module 14: Creating Interactive Dashboards in Tableau

Module 15: Statistical Plotting and Interpretation in R

Module 16: Best Practices for Reproducible Genomic Analysis

Module 17: Case Studies in Genomic Data Analysis

Module 18: Project Work and Presentation

Here's What You'll Learn:

• Data fundamentals: management, collection, cleaning.
• Visualization: creating dashboards and reports.
• Statistical analysis: applying methods for outbreak data.
• Modeling: building predictive models.
• Geospatial analysis: mapping and analyzing spatial data.
• Ethical considerations: data privacy and security.
• Real-world application: case studies and practical sessions.
• Emerging technologies: big data, AI, and data integration.
• Decision-making: using data for crisis response and surveillance.

Here's What You'll Learn:

• Data Handling: SQL for extraction and cleaning.
• Analysis: EDA, time series, risk, and performance analysis.
• Modeling: financial modeling, portfolio optimization.
• Visualization: creating charts and dashboards.
• Advanced Techniques: machine learning, scenario analysis.
• Ethics: data governance and privacy.
• Practical Application: hands-on exercises and a capstone project.

Here's What You'll Learn:

• Data Governance: frameworks, quality, security, compliance.
• Data Integration: ETL, data warehousing, data lakes.
• Python Analytics: data preprocessing, EDA, advanced techniques.
• PowerBI Visualization: dashboards, advanced techniques.
• Data Management Best Practices: lifecycle, metadata, archiving.
• Big Data: technologies, use cases in oil and gas.
• Real-Time Processing: stream processing, real-time analytics.
• Digital Oilfields: IoT integration, operational efficiency.
• Data Security: risk assessment, cybersecurity, incident response.
• Emerging Trends: cloud computing, AI, blockchain.
• Practical Application: hands-on sessions, real-world examples.