I am passionate about tackling challenging problems through innovative experimental designs & methodologies to achieve cost-effective solutions. During my academic career, I studied and researched nanotechnology and its novel applications in oil & gas; where my fascination with developing and testing hypotheses through data manipulation and visualization led me to pursue a second career in data science and analytics.➤ Project Portfolio: https://muhammad-adil86.github.io/muhammad-adil.github.io/Achievements:➤ Demonstrated 5+ years of proficiency in material characterization and statistical & visualization tools, with an interact in data analytics for crude oil depletion solutions➤ Exhibited transferable communication, organizational, and collaboration skills by publishing research findings in 14 high-impact journals and drafting 3 research proposals➤ Secured a research grant of 45K USD through effective presentation skills, including visualizing data with charts & graphs, with internal and external stakeholders of PETRONAS➤ Volunteered to peer-review more than 20 scientific articles on a regular basis and chaired conference sessionsCompetencies:➤ Languages: SQL, Python (numpy, pandas, scikit-learn, tensorflow)➤ Data Management Tools: Microsoft Excel, Azure Sandbox, Google Workspace, RDBMS, Microsoft SQL Server, SQLite, ➤ Visualization Tools: Tableau, PowerBI, IBM Cognos Analytics, Matplotlib, Seaborn➤ Project Management Fundamentals: Agile, Waterfall, Scrum, Kanban, Trello, Gantt Charts➤ Analysis: EDA, Parameter tuning and/or feature engineering for validation, Multivariate analysis, Spectral analysis, Mathematical fitting e.g., Exponential fitting, Gaussian fitting➤ Machine Learning: Multiple regression, Classification, Supervised Learning: Linear Models, Logistic Regression, Support Vector Machines, Stochastic Gradient Descent, KNN, Naive Bayes, Decision Trees, Random Forest, XGBoost, Unsupervised Learning: K-Means Clustering, Principal Component Analysis➤ Deep Learning: Image Recognition, Neural Networks (CNN + RNN) , Natural Language Processing using TensorFlow➤ Material Simulation & Characterization Tools: Accelrys material studio, Rame-Hart DROPimage software, PANalytical X'Pert HighScore Plus