Python Mentor for Data Science
Budget: ₹12,500 – ₹37,500 INR
I’m guiding a small group of advanced Python users who now want to master Data Science and Machine Learning. They already write clean, object-oriented code, but need structured help turning that skill into solid analytical practice.
Your role is to meet them online, break down real-world datasets, and walk them through both the theory and hands-on implementation of:
• Statistical analysis & data visualisation: pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly, plus the “why” behind each chart or test.
• Core machine-learning algorithms: from linear and logistic regression through tree-based models, clustering, model evaluation and cross-validation using scikit-learn.
Weekly rhythm I have in mind is one live workshop (60–90 min) and one code-review/drop-in clinic. I’ll share the syllabus and sample datasets; you bring clear explanations, practical demos in Jupyter or VS Code, and short homework that builds a portfolio piece each week.
Please be comfortable translating the math into intuitive visuals, answering tough follow-up questions, and giving constructive feedback on notebooks or GitHub PRs. If you can occasionally touch on deep-learning concepts, great—but the immediate priority is statistical thinking and classical ML.
When you reply, let me know your preferred teaching tools, any past mentoring experience, and a short note on how you’d structure the first session around exploratory data analysis.
Your role is to meet them online, break down real-world datasets, and walk them through both the theory and hands-on implementation of:
• Statistical analysis & data visualisation: pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly, plus the “why” behind each chart or test.
• Core machine-learning algorithms: from linear and logistic regression through tree-based models, clustering, model evaluation and cross-validation using scikit-learn.
Weekly rhythm I have in mind is one live workshop (60–90 min) and one code-review/drop-in clinic. I’ll share the syllabus and sample datasets; you bring clear explanations, practical demos in Jupyter or VS Code, and short homework that builds a portfolio piece each week.
Please be comfortable translating the math into intuitive visuals, answering tough follow-up questions, and giving constructive feedback on notebooks or GitHub PRs. If you can occasionally touch on deep-learning concepts, great—but the immediate priority is statistical thinking and classical ML.
When you reply, let me know your preferred teaching tools, any past mentoring experience, and a short note on how you’d structure the first session around exploratory data analysis.
Related categories:
Python
Machine Learning (ML)
Data Mining
SPSS Statistics
Data Science
NumPy
SciPy
Data Visualization