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Course Outline
Introduction to Data Science/AI
- Knowledge acquisition through data
- Knowledge representation
- Value creation
- Data Science overview
- AI ecosystem and new approach to analytics
- Key technologies
Data Science workflow
- Crisp-dm
- Data preparation
- Model planning
- Model building
- Communication
- Deployment
Data Science technologies
- Languages used for prototyping
- Big Data technologies
- End to end solutions to common problems
- Introduction to Python language
- Integrating Python with Spark
AI in Business
- AI ecosystem
- Ethics of AI
- How to drive AI in business
Data sources
- Types of data
- SQL vs NoSQL
- Data Storage
- Data preparation
Data Analysis – Statistical approach
- Probability
- Statistics
- Statistical modeling
- Applications in business using Python
Machine learning in business
- Supervised vs unsupervised
- Forecasting problems
- Classfication problems
- Clustering problems
- Anomaly detection
- Recommendation engines
- Association pattern mining
- Solving ML problems with Python language
Deep learning
- Problems where traditional ML algorithms fails
- Solving complicated problems with Deep Learning
- Introduction to Tensorflow
Natural Language processing
Data visualization
- Visual reporting outcomes from modeling
- Common pitfalls in visualization
- Data visualization with Python
From Data to Decision – communication
- Making impact: data driven story telling
- Influence effectivnes
- Managing Data Science projects
35 Hours
Testimonials (1)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
