MODULE 1: RESEARCH DESIGN AND PLANNING

Conceptualising field research Research ethics and IRB/ethics committee application preparation Project management for research timelines and deliverables Defining problem statements and identifying relevant research questions Selecting appropriate research methodologies for different project types

MODULE 2: DATA COLLECTION AND ANALYSIS

Data collection and analysis Qualitative research (interviews, focus groups, etc.) Using platforms like Qualtrics or SurveyMonkey to create and manage surveys Building respondent pools and tracking engagement metrics Evaluating data validity and reliability across sources AI tools, workflows and automation for transcription and analysis

MODULE 3: LITERATURE REVIEWS AND ACADEMIC RESEARCH SUPPORT

Preparing comprehensive literature reviews Conducting structured literature reviews and quantitative meta-analyses (PRISMA) Performing pre-publication peer review Synthesizing academic arguments and identifying key gaps Creating annotated bibliographies using citation managers

MODULE 4: WRITING, FORMATTING, AND SUBMISSIONS

Meeting citation standards and stylesheet guidelines for journals Manuscript formatting and submission for target journals Preparing manuscripts in LaTeX Refining and copy-editing manuscripts for clarity and scholarly tone Aligning content to specific journal scopes and editorial requirements

MODULE 5: GRANT WRITING AND BUDGETING

Writing grant proposals to meet technical requirements Grant budget development and financial reporting Reviewing funder guidelines and formatting proposals accordingly Preparing executive summaries and logic frameworks for donors

MODULE 6: DATA TOOLS AND STATISTICAL TECHNIQUES

Statistical analysis and data visualization (using AI) Research software proficiency (SPSS, R, NVivo, ATLAS.ti) Coding and analyzing qualitative/textual data Integrating AI tools with traditional software to enhance outputs Performing cross-tabulation, correlation, and regression analysis

MODULE 7: ADVANCED AUTOMATION AND CODING SKILLS

Writing code in Python or MATLAB to automate data analysis Employing tools like Jupyter Notebooks or R Markdown Machine learning algorithms and data mining techniques Building statistical models and simulations (Monte Carlo, etc.) Scraping online databases and academic archives using code How to use AI for automation, data scraping and mining

MODULE 8: RESEARCH PRESENTATION AND VISUALIZATION

Creating publication-quality graphs, tables, and infographics (Excel, Tableau, Python/R) Designing diagrams and models using Adobe Illustrator or CorelDRAW Creating effective research posters and academic conference presentations Embedding visual storytelling in research deliverables Using AI to design slide decks and visual assets quickly

MODULE 9: REFERENCE AND VERSION MANAGEMENT

Proficiency in Zotero, Mendeley, EndNote Managing bibliometric indicators and impact factors Collaborative writing and version control (Git, Google Docs) Setting up shared research folders with synced citation managers Automating reference updates across long manuscripts

MODULE 10: DATA PREPARATION AND COMPLIANCE

Organizing, cleaning, and transforming raw datasets Managing large datasets with SQL, Hadoop, Spark Anonymizing sensitive data (GDPR/IRB compliance) Using AI for transcription and analysis of interviews Understanding legal considerations around data ownership and sharing

Course Objective

Syllabus

Course Plan

AI-Driven Automation, Data Analysis & Research for Academic Writers

Enrollment Closed