What will you learn in Data Science Projects with Python Course
Gain hands-on experience exploring, cleaning, and visualizing real-world datasets with pandas and Matplotlib
Build and evaluate logistic regression models, addressing overfitting through regularization and cross-validation
Train and tune decision tree and random forest classifiers to improve predictive accuracy
Master gradient boosting with XGBoost and interpret model outputs using SHAP values
Program Overview
Module 1: Introduction
⏳ 30 minutes
Topics: Role of ML in data science; essential Python libraries (pandas, scikit-learn)
Hands-on: Get set up in Jupyter, load the case-study data, and verify basic data integrity
Module 2: Data Exploration & Cleaning
⏳ 4 hours
Topics: Data-quality checks, handling missing values, categorical encoding
Hands-on: Perform end-to-end data cleaning and exploratory analysis on the credit dataset
Module 3: Introduction to scikit-learn & Model Evaluation
⏳ 3.5 hours
Topics: Synthetic data generation, train/test splitting, evaluation metrics (accuracy, ROC)
Hands-on: Train logistic regression, compute confusion matrix and ROC curve
Module 4: Details of Logistic Regression & Feature Extraction
⏳ 4 hours
Topics: Feature-response relationships, univariate selection (F-test), sigmoid function
Hands-on: Implement feature selection, plot decision boundaries, and interpret coefficients
Module 5: The Bias-Variance Trade-Off
⏳ 3.5 hours
Topics: Gradient descent optimization, L1/L2 regularization, cross-validation pipelines
Hands-on: Apply regularization techniques and hyperparameter tuning in scikit-learn
Module 6: Decision Trees & Random Forests
⏳ 3.25 hours
Topics: Tree-based learning, node impurity, hyperparameter grid search, ensemble methods
Hands-on: Train and tune decision tree and random forest models; visualize performance
Module 7: Gradient Boosting, XGBoost & SHAP Values
⏳ 3 hours
Topics: XGBoost hyperparameters (learning rate, early stopping), SHAP interpretability
Hands-on: Perform randomized grid search and generate SHAP explanations for case-study data
Module 8: Test-Set Analysis, Financial Insights & Delivery
⏳ 2.5 hours
Topics: Probability calibration, decile cost charts, business-impact analysis
Hands-on: Derive financial metrics (cost savings, ROI) and prepare client-ready deliverables
Module 9: Appendix – Local Jupyter Setup
⏳ 15 minutes
Topics: Recommended environment setup, Anaconda installation
Hands-on: Create and configure a local Jupyter Notebook for offline work
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Job Outlook
Median annual wage for data scientists in the U.S.: $112,590
Projected data science job growth of 36% from 2023 to 2033, far outpacing average for all occupations
Roles include Data Scientist, ML Engineer, and Analytics Consultant across finance, healthcare, and tech
Expertise in end-to-end ML workflows unlocks opportunities in startups and enterprise data teams
Specification: Data Science Projects with Python Course
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FAQs
- Python can process real-time data streams using libraries like Kafka-Python or PySpark Streaming.
- Integrates with dashboards to visualize live data for business insights.
- Supports predictive analytics on-the-fly using trained ML models.
- Can automate alerts based on threshold breaches in financial or operational data.
- Scalable for IoT or online transaction monitoring projects.
- Hands-on projects help translate complex data into actionable insights.
- Visualizations in Matplotlib or Seaborn enhance audience understanding.
- Real-world datasets provide context for decision-making scenarios.
- Learning to explain model results builds client-ready presentation skills.
- Encourages interpreting metrics like ROI, cost savings, and predictive accuracy effectively.
- Projects demonstrate end-to-end handling: cleaning, modeling, and reporting.
- Showcase mastery of Python libraries such as pandas, scikit-learn, and XGBoost.
- Include visual and interactive outputs to impress potential employers.
- Highlight experience in real-world datasets, increasing credibility.
- Can be hosted on GitHub or personal websites as tangible evidence of skills.
- SHAP values and feature importance enhance trust in model predictions.
- Enables ethical and transparent AI deployment in finance, healthcare, and tech.
- Helps justify decisions to non-technical stakeholders.
- Improves debugging and fine-tuning of predictive models.
- Increases employability in roles demanding responsible AI knowledge.
- Data Scientist roles focusing on end-to-end ML workflows.
- Machine Learning Engineer building predictive models for businesses.
- Analytics Consultant advising clients using data-driven strategies.
- Business Intelligence Developer converting data into actionable insights.
- Roles in finance, healthcare, and startups requiring project-based data expertise.
