Course Syllabus

Master data analysis, machine learning, and AI fundamentals.

  • Understanding Data Science basics and career paths
  • Workflow and tools introduction (Python, SQL)
  • Python basics and data libraries (Pandas, NumPy)
  • Exploratory data analysis techniques
  • Handling missing data and cleaning datasets
  • Feature engineering for model preparation
  • Correlation and regression fundamentals
  • Probability distributions and hypothesis testing
  • Creating charts and dashboards (Matplotlib, Seaborn)
  • Presenting insights effectively to stakeholders
  • Regression and classification algorithms
  • Model evaluation metrics
  • Clustering and dimensionality reduction
  • Anomaly detection techniques
  • Introduction to neural networks
  • AI and Deep Learning fundamentals
  • Writing queries and performing joins
  • Extracting data for analytical processing
  • Full real-world project development
  • Presentation of findings and model results

Why Learn Data Science?

  • High Salary: Professionals are highly paid worldwide.
  • Portfolio: Build projects that improve job opportunities.