Online Data analysis, Data Science, Semester machine and deep learning project

We are looking for a tutor for Wharton data science competition Learn the Core Data Science Skills You don't need advanced machine learning, but you should be comfortable with: Data cleaning Descriptive statistics Probability Regression Classification models Data visualization Spreadsheet analysis (Excel/Google Sheets) Python (Pandas, NumPy, Matplotlib) Wharton's own Data Science Academy curriculum emphasizes statistics, regression, classification, visualization, and data wrangling. Wharton Global Youth Program 2. Practice Sports Analytics Recent competitions involved: Soccer playoff predictions Basketball tournament predictions Ice hockey performance predictions Students were asked to: Rank teams Predict match outcomes Create performance metrics Build visualizations Explain their reasoning clearly Wharton Sports Analytics +1 A good practice project: Download IPL, NBA, EPL, or Cricket World Cup datasets from Kaggle. Predict match winners. Build team rankings. Create visual dashboards. 3. Master Feature Engineering Winning teams often create new variables instead of relying only on raw data. Examples: Point differential Home-field advantage Possession-adjusted statistics Recent form metrics Offensive/defensive ratings Wharton's competition playbooks specifically highlight feature engineering as a key differentiator. Wharton Sports Analytics +1 4. Learn These Models Start with: Linear Regression Logistic Regression Elo Rating Systems Random Forests XGBoost (optional) Many successful teams use relatively simple models but explain them well. Wharton Sports Analytics +1 5. Build Presentation Skills The competition has multiple rounds: Initial analysis Slide presentation Live finalist presentation to judges Wharton Sports Analytics Judges care about: Clear storytelling Visualizations Reasoning behind decisions Team communication Level: Beginner Teacher's Gender: Any Meeting options: Available online - via skype etc. We are looking for a tutor for Wharton data science competition Learn the Core Data Science Skills You don't need advanced machine learning, but you should be comfortable with: Data cleaning Descriptive statistics Probability Regression Classification models Data visualization Spreadsheet analysis (Excel/Google Sheets) Python (Pandas, NumPy, Matplotlib) Wharton's own Data Science Academy curriculum emphasizes statistics, regression, classification, visualization, and data wrangling. Wharton Global Youth Program 2. Practice Sports Analytics Recent competitions involved: Soccer playoff predictions Basketball tournament predictions Ice hockey performance predictions Students were asked to: Rank teams Predict match outcomes Create performance metrics Build visualizations Explain their reasoning clearly Wharton Sports Analytics +1 A good practice project: Download IPL, NBA, EPL, or Cricket World Cup datasets from Kaggle. Predict match winners. Build team rankings. Create visual dashboards. 3. Master Feature Engineering Winning teams often create new variables instead of relying only on raw data. Examples: Point differential Home-field advantage Possession-adjusted statistics Recent form metrics Offensive/defensive ratings Wharton's competition playbooks specifically highlight feature engineering as a key differentiator. Wharton Sports Analytics +1 4. Learn These Models Start with: Linear Regression Logistic Regression Elo Rating Systems Random Forests XGBoost (optional) Many successful teams use relatively simple models but explain them well. Wharton Sports Analytics +1 5. Build Presentation Skills The competition has multiple rounds: Initial analysis Slide presentation Live finalist presentation to judges Wharton Sports Analytics Judges care about: Clear storytelling Visualizations Reasoning behind decisions Team communication Level: Beginner Teacher's Gender: Any Meeting options: Available online - via skype etc.