I have just finished my degree and want to spend the next six months turning classroom knowledge into real-world, interview-ready expertise. I’m looking for a mentor who can guide me across the full data-science spectrum—mathematics, coding and project development—so that I can step confidently into technical interviews at the end of our contract. Here 1. Mathematics & Statistics (Foundation) Linear Algebra Calculus Probability Statistics Why first: Math is the backbone of algorithms, ML, and deep learning. 2. Programming & Core Tools Python R Git Data Structures & Algorithms SQL Why: Programming skills and understanding data structures are essential to implement data solutions and work with databases. 3. Data Preparation & Visualization Pandas NumPy Matplotlib Seaborn Why: Before modeling, you need to clean, manipulate, and visualize data effectively. 4. Business Intelligence Tools Tableau Power BI Why: Understanding dashboards and reporting helps communicate insights to stakeholders. 5. Machine Learning Scikit-learn TensorFlow PyTorch Why: Learn supervised, unsupervised, and reinforcement learning for predictive modeling. 6. Deep Learning Neural Networks Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Why: For advanced pattern recognition, image, text, and sequence modeling. 7. Specialization Natural Language Processing (NLP) Computer Vision Social Network Analysis Why: Focus on a niche in data science for higher career impact. 8. Big Data & Distributed Computing Hadoop Spark Why: Handling very large datasets efficiently in real-world scenarios. The work will be structured around weekly video sessions, targeted exercises and project critiques. On the math side, I need you to revisit core probability, statistics, linear algebra and any calculus concepts that routinely appear in data-science screening tests. Coding practice must revolve around Python, R and SQL, with equal attention on writing idiomatic code, optimising performance and explaining solutions aloud. Finally, I’d like to walk through end-to-end mini-projects: scoping a question, wrangling data, building and validating models, then packaging the results into concise reports or dashboards. Deliverables • A six-month study roadmap with weekly objectives • Live lessons (minimum one per week) plus recorded walkthroughs of key topics • Three polished portfolio projects demonstrating EDA, modelling and communication skills • Monthly progress reports and a final mock interview with detailed feedback Acceptance criteria • All scheduled sessions completed within the six-month window • Portfolio projects stored in a shared Git repository, each passing your own code-review checklist • Mock interview scorecard shows clear, actionable feedback and at least 80 % of common interview topics mastered If you have a track record of moving learners from academics to industry roles—and you’re comfortable teaching Python, R, SQL and the foundational maths that power them—let’s discuss how we can make these next six months count.