Research, Data Management and Data Science

Here's What You'll Learn:

Module 1: Foundations of HR Metrics and Strategic Analytics

Module 2: Power BI Desktop: Setup, Data Connection, and Interface

Module 3: Data Transformation and Cleaning using Power Query Editor

Module 4: Mastering DAX for HR Calculations and Business Logic

Module 5: Designing Interactive Workforce and Engagement Dashboards

Module 6: Analytics for Recruitment, Talent Acquisition, and Efficiency

Module 7: Learning & Development (L&D) and Performance Analytics

Module 8: Compensation, Equity, and Total Rewards Analysis

Module 9: Advanced Data Visualization and Storytelling in Power BI

Module 10: Report Sharing, Governance, and Introduction to Predictive HR Analytics

Here's What You'll Learn:

Module 1: Introduction to Epidemiological Modeling and R

Module 2: Fundamentals of Deterministic Compartmental Models

Module 3: Implementing SIR/SEIR Models in R

Module 4: Parameter Estimation and Model Calibration

Module 5: Modeling the Impact of Vaccination

Module 6: Advanced Model Structures and Dynamics

Module 7: Stochastic Modeling and Outbreak Simulation

Module 8: Policy Analysis and Economic Evaluation

Module 9: Global Sensitivity and Uncertainty Analysis

Module 10: Case Studies and Advanced Modeling in R

Here's What You'll Learn:

Module 1: Foundations of Marketing Analytics

Module 2: Introduction to R for Data Analysis

Module 3: Data Preparation and Visualization in R

Module 4: Descriptive Marketing Analytics

Module 5: Customer Segmentation and Clustering (R)

Module 6: Predictive Modeling for Marketing (R)

Module 7: Introduction to Tableau for Visualization

Module 8: Connecting Data and Building Dashboards in Tableau

Module 9: Marketing Mix Modeling (R)

Module 10: Web and Social Media Analytics (R)

Module 11: A/B Testing and Experimentation Analysis (R)

Module 12: Customer Lifetime Value (CLV) Analysis (R)

Module 13: Visualizing Campaign Performance in Tableau

Module 14: Advanced Dashboarding Techniques in Tableau

Module 15: Reporting and Storytelling with Data

Module 16: Market Basket Analysis and Recommendation Systems (R)

Module 17: Geospatial Marketing Analysis

Module 18: Course Capstone Project and Next Steps

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:

Module 1: STATA Programming and Data Management Fundamentals

Module 2: Advanced Data Reshaping and Cleaning Techniques

Module 3: Mastering Linear Regression Diagnostics and Extensions

Module 4: Generalized Linear Models (GLM) and Non-Linear Regression

Module 5: Time Series Analysis and Forecasting in STATA

Module 6: Panel Data Econometrics: Fixed and Random Effects

Module 7: Instrumental Variables (IV) and Treatment Effects Modeling

Module 8: Survival Analysis and Event History Modeling

Module 9: Survey Data Analysis and Weighting Methods

Module 10: Automation, Custom Reports, and Reproducible Research

Here's What You'll Learn:

Module 1: Foundations and the Research Lifecycle

Module 2: Crafting Compelling Introductions and Literature Reviews

Module 3: Methodological Rigor and Reporting Standards

Module 4: Presenting Results and Discussion/Conclusion

Module 5: Effective Academic Style and Language

Module 6: Ethics, Integrity, and Authorship

Module 7: Selecting the Right Journal and Targeting Audience

Module 8: Manuscript Submission and the Peer Review Process

Module 9: Revising and Responding to Reviewer Comments

Module 10: Maximizing Visibility and Impact

Here's What You'll Learn:

Module 1: Foundations of Literature Review and Research Questions

Module 2: Developing Comprehensive Search Strategies

Module 3: Database Selection and Search Execution

Module 4: Reference Management and Screening Tools

Module 5: Critical Appraisal and Risk of Bias Assessment

Module 6: Data Extraction and Coding for Synthesis

Module 7: Narrative Synthesis and Thematic Grouping

Module 8: Introduction to Meta-Analysis Principles

Module 9: Reporting Standards and Dissemination

Module 10: Writing, Reviewing, and Utilizing the Review

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:

Module 1: Foundations of Outbreak Data and R Environment Setup

Module 2: Data Import, Cleaning, and Tidy Data Principles in R

Module 3: Descriptive Epidemiology and Measures in R

Module 4: Advanced Visualization for Outbreaks using ggplot2

Module 5: Introduction to Spatial Epidemiology and Mapping in R

Module 6: Time Series Analysis and Epidemic Curves using R

Module 7: Advanced Epidemiological Modeling with R

Module 8: Introduction to Power BI for Data Connection

Module 9: Data Transformation and M-Query in Power BI

Module 10: Data Modeling and Relationships in Power BI

Module 11: DAX Fundamentals for Public Health Metrics

Module 12: Creating Interactive Outbreak Dashboards in Power BI

Module 13: Geospatial Mapping and Visualization in Power BI

Module 14: Integrating R Visuals into Power BI

Module 15: Forecasting and Predictive Analytics in R

Module 16: Ethics, Data Security, and Confidentiality

Module 17: Automating Refresh and Dashboard Publication

Module 18: Final Capstone Project: End-to-End Analytics

Here's What You'll Learn:

 Module 1: Financial Data Analytics in FP&A

 Module 2: SQL Fundamentals: Retrieval and Filtering

 Module 3: SQL Aggregation and Grouping

 Module 4: Joining Financial Datasets

 Module 5: Advanced SQL Set Operations and Subqueries

 Module 6: Common Table Expressions (CTEs) for Multi-Step Logic

 Module 7: Window Functions for Financial Metrics

 Module 8: SQL for Data Cleansing and Transformation

 Module 9: Direct Database Connection and Excel Integration

 Module 10: Mastering Power Query for Data Transformation (ETL)

 Module 11: Advanced Power Query M Language and Custom Functions

 Module 12: Introduction to Data Modeling with Power Pivot

 Module 13: Dynamic Array Formulas for Modern Analysis

 Module 14: Advanced Lookup and Conditional Functions

 Module 15: Scenario and Sensitivity Analysis Tools

 Module 16: Time Value of Money (TVM) and Valuation Functions

 Module 17: Interactive Dashboard Design and Visualization

 Module 18: Automation, VBA, and Strategic Data Storytelling

Here's What You'll Learn:

Module 1: Foundational Data Ecosystems in Oil and Gas

Module 2: Python Fundamentals for Data Ingestion and Wrangling

Module 3: Specialized Data Handling (Time-Series, Well Logs, Seismic)

Module 4: Data Quality, Cleaning, and Feature Engineering with Pandas

Module 5: Automated Reporting and Scripting with Python

Module 6: PowerBI Architecture and Advanced Data Modeling

Module 7: Mastering Data Analysis Expressions (DAX)

Module 8: Geospatial and Visual Analytics for Petroleum Data

Module 9: Designing Interactive and Actionable Business Dashboards

Module 10: Deployment, Security, and Governance