Data Science Professional with Python & Applied Analytics

Career Outcomes

After completing this course, students will be able to:

• Work as Junior Data Scientist, Data Analyst, or Business Intelligence Analyst
• Build end-to-end data science projects from data collection to deployment
• Apply statistical analysis and machine learning to real business problems
• Create professional data visualizations and dashboards
• Build GenAI-powered chatbots and data assistants
• Communicate data insights effectively to stakeholders

Tools & Technologies

| Category | Tools |

|----------|-------|

| **Programming** | Python 3.11+ |

| **Data Manipulation** | NumPy, Pandas |

| **Visualization** | Matplotlib, Seaborn, Plotly |

| **Machine Learning** | Scikit-learn, XGBoost, LightGBM |

| **GenAI & LLMs** | OpenAI API, Claude API, LangChain (basics) |

| **Databases** | PostgreSQL, SQLite, SQLAlchemy |

| **Development** | Jupyter Notebook, VS Code, Git, GitHub |

| **Deployment** | Streamlit, Flask (basics) |

| **Environment** | Conda, pip, venv |

| **Collaboration** | Google Colab, Kaggle Notebooks |

Career Path After Completion:

1. Entry-Level Roles

• Junior Data Scientist
• Data Analyst
• Business Intelligence Analyst
• Junior ML Engineer
• Analytics Associate
• AI/GenAI Application Developer

2. Freelancing Scope

• Data analysis projects on Upwork/Fiverr
• Dashboard development for small businesses
• Kaggle competitions for portfolio building
• Data cleaning and preparation services
• GenAI chatbot development for businesses

Course Timeline Summary

| Month | Focus | Key Deliverable |

|-------|-------|-----------------|

| **Month 1** | Python Foundation + Math/Stats | Statistical analysis project |

| **Month 2** | Data Manipulation + Visualization | Customer segmentation report |

| **Month 3** | Machine Learning Fundamentals | Loan prediction model |

| **Month 4** | GenAI/LLMs + SQL + Capstone | GenAI Chatbot + Deployed retail solution |

Next Learning Steps

• **Deep Learning & Neural Networks** — TensorFlow, PyTorch
• **Big Data Technologies** — Spark, Hadoop basics
• **Cloud Platforms** — AWS/GCP/Azure ML services
• **Advanced GenAI** — RAG, Agents, Fine-tuning
• **MLOps** — Model monitoring, CI/CD for ML
  

Recommended Certifications (Optional)

• Google Data Analytics Professional Certificate
• IBM Data Science Professional Certificate
• AWS Certified Machine Learning – Specialty (after more experience)