Artificial Intelligence and Data Science Engineering course
Artificial Intelligence and Data Science Engineering course
Our Real-World Data Science & AI program is designed for professionals seeking a comprehensive understanding of data science and AI’s practical applications in business. Gain insights into the full spectrum of data-driven projects, from conception to implementation. Discover the opportunities and challenges organizations face when adopting these transformative technologies.
Course Overview
- Duration: 2-days / 16 Hours
- Certification: Participants will receive a Certificate of Completion upon successfully completing the course
- Who Should Attend: Individuals in AI-related industries, Data Scientist, Machine Learning Engineer, Data Engineer, AI Engineer, Business Intelligence Analyst, Product Manager (Tech), Marketing Analyst, Financial Analyst, Operations Research Analyst, Quantitative Analyst, Research Scientist, Academic Researcher, Consultant
Course Objective
You will learn Data Analytics & Data Science will equip you with the knowledge needed for the role as an Data Analyst.
Pre-Requisite
Ample knowledge in Data Analytics is required.
Examination
No Examination Required
Course Outline
- Module 1: Foundations of Data Science and AI
- Introduction to data science and AI
- Data lifecycle: collection, cleaning, exploration, visualization
- Statistical concepts and probability
- Python programming for data science
- Mathematical foundations for data science
- Module 2: Data Exploration and Visualization
- Exploratory data analysis (EDA) techniques
- Data visualization libraries and tools
- Storytelling with data
- Data wrangling and preprocessing
- Module 3: Machine Learning Fundamentals
- Supervised and unsupervised learning
- Regression and classification algorithms
- Model evaluation and performance metrics
- Feature engineering and selection
- Model deployment and monitoring
- Module 4: Deep Learning and Neural Networks
- Introduction to deep learning
- Neural network architectures (CNN, RNN, LSTM)
- Deep learning frameworks (TensorFlow, PyTorch)
- Applications of deep learning (image recognition, natural language processing)
- Module 5: AI and Business
- AI strategy and roadmap
- AI ethics and responsible AI
- AI in different industries (finance, healthcare, marketing)
- Case studies of successful AI implementations
- Challenges and opportunities in AI adoption
- Module 6: Big Data Technologies
- Introduction to big data and Hadoop ecosystem
- Spark for big data processing
- Cloud computing platforms for data science
- Data warehousing and data lakes
- Module 7: Real-World Projects and Capstone
- End-to-end data science project lifecycle
- Team collaboration and project management
- Data science portfolio development
- Capstone project to apply learned skills
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