The Research Blueprint: Excelling in Data science, Data Analysis and AI
- 1. Double Diverge Consult l.tohomdet@doublediverge.com.ng Excelling in Data Science, Data Analysis and Al Loreta K. Tohomdet AI Scientific Researcher March 17th, 2025, at 4:00 P.M. Google Meet
- 2. Webinar Ground Rules While waiting for others to come in, here are some rules and reminders to keep in mind. Please mute your mics during the session Raise your e-hand to answer or ask a question Do not use abusive words on anyone 1.
- 3. Core Topics for Discussion What we're learning about Research Blueprint Understanding Core Concepts The Research Process Blueprint Key Skills for Excellence Tools and Technologies Ethical Considerations and Best Practices 2.
- 4. Hi! My name is Loreta. A lecturer at AFIT and an IT consultant with Double Diverge, specializing in AI research, policy analysis, and digital innovation. Also, the GDG Lead for Jos and a Women WTM Ambassador, committed to advancing technology education. A proud Plateau State native, a wife, and a passionate community builder - serving as a Google Women Techmakers Ambassador, GDG Jos Lead, and founder of Jostechgirls. Enjoys writing, reading, and engaging with local communities. Personal Background Professional Background 3.
- 5. INTRODUCTION In today's world, data drives decision-making across industries. Understanding and mastering Data Science, Data Analysis, and AI are crucial for solving complex problems, enhancing efficiency, and unlocking new possibilities. Objective: This presentation will provide a structured guide to excelling in these fields, covering key skills, tools, research processes, and best practices. 4.
- 6. Understanding Core Concepts D for Data D for Data A for Artificial S for Science A for Analysis I for Intelligence 5.
- 7. Data Science An interdisciplinary field focused on extracting insights and knowledge from structured and unstructured data using algorithms, statistical models, and computational methods. Example: Analysing customer to improve business strategies. 6.
- 8. Data Analysis The systematic approach of examining, cleaning, transforming, and interpreting data to discover useful information. Example: Identifying sales trends to optimize inventory management. 7.
- 9. Artificial Intelligence The simulation of human intelligence by machines to perform tasks like learning, reasoning, and decision- making. Example: Using chatbots to provide customer service 24/7. 8.
- 10. Think & Apply How can understanding these three fields together improve decision-making in a business setting? Its time for you to think and apply! 9.
- 11. — Clive Humby "Data is the new oil." 10.
- 12. Key Skills for Excellence Skills you require and Why it matters! 11.
- 13. Programming Proficiency: Master languages like Python (for data manipulation), R (for statistical computing), and SQL (for database management). Statistical Analysis & Mathematics: Understand core concepts like linear algebra, probability, regression analysis, and hypothesis testing. 12.
- 14. Data Visualization: Present insights clearly using tools like Tableau, Power BI, and libraries like Matplotlib and Seaborn. Machine Learning & AI Frameworks: Gain expertise in frameworks like TensorFlow (for deep learning), Scikit-learn (for traditional ML), and PyTorch (for advanced research). 13.
- 15. — W. Edwards Deming "Without data, you're just another person with an opinion." 14.
- 16. "Master Programming to Stay Ahead" 57% software developers worldwide use Python. https://www.weforum.org/publications/the-future-of-jobs- report-2025/in-full/3-skills-outlook/ 15.
- 17. "Sharpen Your Analytical Thinking" 70% Approximately 70% of companies consider analytical thinking essential for their workforce. https://www.adelaide.edu.au/pace/news/list/2025/02/25/the- most-in-demand-skills-in-2025 16.
- 18. "Embrace AI and Machine Learning" 25% 25% of U.S. tech job listings in 2025 require artificial intelligence skills. https://www.wsj.com/tech/ai/how-the-ai-talent-race-is- reshaping-the-tech-job-market-93df0615 17.
- 19. "Excel in Data Analysis for Future Opportunities" 34% The U.S. Bureau of Labor Statistics predicts a 34% increase in the data science job market between 2022 and 2032. https://365datascience.com/career-advice/the-future-of-data- science/ 18.
- 20. — Neil Armstrong "Research is creating new knowledge." 19.
- 21. The Research Process Blueprint Define the Problem Collect Data: Clean & Preprocess Analyze & Model: Interpret & Validate: Clearly identify research questions & objectives. Source reliable data from public datasets, web scraping, or proprietary databases. Handle missing data, remove outliers, and normalize variables for better model performance. Evaluate models using metrics (accuracy, precision, recall) and ensure results align with objectives. Use descriptive statistics, machine learning algorithms, and AI models to uncover patterns. Communicate your Findings 20.
- 22. "Data science is a combination of three things: quantitative analysis (for the rigor required to understand your data), programming (to process your data and act on your insights), and narrative (to help people comprehend what the data means)." — Darshan Somashekar, Co-founder at Unwind Media 21.
- 23. Programming Python (data manipulation) and R (statistical analysis) Data Handling Pandas (dataframes), NumPy (numerical computing) Visualization Seaborn (statistical graphics), Plotly (interactive plots) AI Models LLaMA (large language models), GPT (language processing) Cloud Platforms AWS (scalable computing), Google Cloud (data pipelines) Tools and Technologies 22.
- 24. Best Practices for Effective Research Review Related Works: Study existing literature to understand the foundation of your research area. Set Clear Objectives: Frame your research around well-defined questions. Document Everything: Maintain detailed records for reproducibility. Use Version Control: Collaborate and track changes with Git and GitHub. Validate Models: Employ cross-validation to avoid overfitting. Continuous Learning: Stay informed through journals like "Journal of Data Science" and conferences like DSN. 23
- 25. — Voltaire "With great power comes great responsibility." 24.
- 26. Ethical Considerations in Data Science & AI Data Privacy: Ensure compliance with regulations like GDPR. Bias Mitigation: Regularly audit models for fairness. Transparency: Use interpretable models and provide explanations. Responsible AI: Avoid harm and prioritize user well-being. 25.
- 27. Formal Education Pursue certifications from platforms like Coursera (Machine Learning by Stanford) or Google (Data Analytics Certificate). Hands-on Practice Engage in Kaggle competitions to solve real-world problems. Research & Publications Share findings in peer-reviewed journals to contribute to the community. Pathways to Mastery 26.
- 28. Future Trends in Data Science & AI AutoML: Automated model development and deployment. Federated Learning: Collaborative model training without sharing data. AI-Augmented Research: AI tools assisting human researchers. Explainable AI (XAI): Enhanced transparency in complex AI systems. 27.
- 29. Key Takeaways Master core concepts and essential tools. Follow a structured research blueprint for consistent results. Prioritize ethical considerations and responsible AI practices. Keep learning and engaging with the global community. 28.
- 30. — Tohomdet "Data is a powerful tool - but its true value lies in how we collect, analyze, and use it responsibly to shape a better future." 29.
- 31. Resources & Reference "Data Science from Scratch" by Joel Grus 1. Artificial Intelligence: A Guide for Thinking Humans" by Melanie Mitchell 2. https://www.weforum.org/publications/the-future-of-jobs-report- 2025/in-full/3-skills-outlook/ https://www.adelaide.edu.au/pace/news/list/2025/02/25/the-most-in- demand-skills-in-2025 https://www.wsj.com/tech/ai/how-the-ai-talent-race-is-reshaping-the- tech-job-market-93df0615 https://365datascience.com/career-advice/the-future-of-data-science/ Mathew, D.E., Ebem, D.U., Ikegwu, A.C. et al. Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human. Neural Process Lett 57, 16 (2025). https://doi.org/10.1007/s11063-025-11732- 2 Lopez-Ramos, L. M., Leiser, F., Rastogi, A., Hicks, S., Strumke, I., Madai, V. I., Budig, T., Sunyaev, A., & Hilbert, A. (2024). Interplay between federated learning and explainable artificial intelligence: A scoping review. On behalf of the VALIDATE consortium. https://www.wired.com/story/combining-ai-and-crispr-will-be- transformational/ 30.
- 32. Thank you for listening! Feel free to DM me your questions any time: @loretatohomdet@gmail.com 31.