AI in Data Science

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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