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AI in Data Science

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Description

This AI in Data Science training course teaches you how artificial intelligence is reshaping the way data is collected, analyzed, and turned into insight. You will learn to automate data preparation, build predictive models using machine learning, work with deep learning frameworks, and use AI-powered visualization tools to deliver business value. The course covers ethics, responsible AI, and ends with a capstone project that prepares you for roles like data scientist, ML engineer, and AI analyst.

Course Content

Module 1: Introduction to AI in Data Science

  • How AI differs from traditional data analysis
  • The modern data science workflow with AI integration
  • Tools, frameworks, and platforms shaping the field

Module 2: Data Preparation with AI

  • Automated data cleaning and preprocessing
  • AI-powered feature engineering and selection
  • Handling missing data and outliers intelligently
  • Tools: DataRobot, H2O.ai, Trifacta

Module 3: Machine Learning Fundamentals

  • Supervised, unsupervised, and reinforcement learning
  • Regression, classification, and clustering algorithms
  • Model evaluation: accuracy, precision, recall, F1-score
  • Avoiding overfitting and underfitting

Module 4: Deep Learning for Data Science

  • Neural networks and how they learn
  • Convolutional networks for image and pattern data
  • Recurrent networks for time series and sequences
  • Frameworks: TensorFlow, PyTorch, Keras

Module 5: Predictive Analytics and Forecasting

  • Building predictive models for business outcomes
  • Time series forecasting with AI
  • Customer behavior prediction and churn analysis
  • Real-world case studies across industries

Module 6: Data Visualization and Storytelling

  • AI-assisted visualization tools
  • Building dashboards that communicate insights
  • Storytelling with data for stakeholders
  • Tools: Tableau AI, Power BI Copilot, Plotly

Module 7: Ethics and Responsible AI

  • Bias detection and fairness in models
  • Data privacy and compliance (GDPR, NDPR)
  • Explainable AI and model transparency
  • Building trust in data-driven decisions

Module 8: Capstone Project and Career Paths

  • End-to-end AI data science project
  • Portfolio building and GitHub presence
  • Career roles: data scientist, ML engineer, AI analyst
  • Continuous learning and staying current

Duration: 6 – 8 weeks

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