I am working on a graduate-level project that involves mixed data types and I need one-on-one guidance to reinforce my skills—especially in data cleaning and preprocessing with Python. The focus will be on the practical, step-by-step application of Pandas, NumPy and Matplotlib while staying fully compliant with academic integrity guidelines; no AI-generated work is permitted. My most urgent challenges include: • Handling missing values • Removing duplicates • Dealing with outliers Beyond cleaning, I will also ask for advice on choosing suitable descriptive statistics, selecting the right statistical tests, and presenting results clearly. Expect questions on relationship analysis and time-series concepts as the project evolves. What I’m hoping for: clear explanations during video or screen-share sessions, brief code walkthroughs I can replicate myself, and quick feedback on my methodology so I can defend every decision academically. Prior tutoring experience is a plus because I learn best through dialogue and iterative practice. If you have a strong statistics background, deep Python competence, and respect strict academic ethics, let’s set up an initial session and outline a learning plan.