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
Explore More Learning Paths
Enhance your Python and data science skills with these carefully selected courses designed to help you tackle real-world projects and strengthen your analytical capabilities.
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Data Science Methodology Course – Learn the end-to-end data science workflow, including methodology and best practices for real-world project execution.
Tools for Data Science Course – Master essential tools and technologies for data analysis, visualization, and workflow optimization.
Related Reading
What Is Data Management – Understand how effective data management supports analytics, project execution, and decision-making in data-driven organizations.
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.
