01
Learning Objectives
Provide advanced skills in Python for data science, AI, and software development, from fundamentals to the use of advanced libraries and machine/deep learning techniques.
FINAL EXAM ONLY
PY_DS_AI_DEV_LA
Python for Data Science, Artificial Intelligence and Development – Exam
The course "Python for Data Science, Artificial Intelligence and Development – Exam" provides an advanced mastery of Python focused on applications in data science, AI, and software development. It covers foundational Python language features, environment management, code quality, and testing, as well as advanced data analysis with libraries like NumPy and pandas, and data visualization tools such as Matplotlib and Seaborn. Core topics include exploratory data analysis, preprocessing, and machine learning with scikit-learn, along with an introduction to neural networks and deep learning frameworks like PyTorch and TensorFlow. The course also addresses practical development skills such as scripting, API management, integrating data from various sources, version control with Git, and deployment and packaging techniques, including an overview of Docker containerization. Designed for learners with prior Python experience aiming to build skills for technical roles in data and AI fields, the course concludes with a 60-minute exam requiring a minimum score of 70% to pass.
Exam duration60 min
Pass mark70%
Available questions0
CIWZ PROFESSIONAL ASSESSMENT
High-integrity learning environment
The pathway is designed for managers, executives, function owners, auditors and professionals. Lessons use validated minimum study time and a contextual tutor; assessments use server-side random selection of questions and answers, session-continuity controls, camera and local audio-activity monitoring during protected tests.
Traceability and reliability: integrity events are logged for authorized review; automated signals do not determine the result on their own.
OFFICIAL PATHWAY INFORMATION
Course technical sheet
Purpose, references, delivery method, commercial terms and certificate value, presented clearly and transparently.
02
Skills Acquired
- Effective Python use and environment management
- Data analysis and visualization with NumPy, pandas, Matplotlib, Seaborn
- Machine Learning with scikit-learn
- Introduction to Deep Learning with PyTorch and TensorFlow
- Development of scripts, CLI tools, and APIs
- Data integration from files, databases, and REST APIs
- Version control with Git
- Deployment and packaging, basic Docker knowledge
03
Target Audience
Professionals and students with basic Python knowledge seeking specialization in data science and AI development.
04
Prerequisites
Fundamental Python programming skills.
05
Program
- Python fundamentals and OOP
- Environment and dependency management
- Code quality: PEP8, testing, logging
- Data analysis and visualization
- EDA, preprocessing, feature engineering
- Machine Learning with scikit-learn
- Introduction to Deep Learning
- Application development and automation
- Data integration
- Git and collaboration
- Deployment and packaging
06
Teaching Methodology
Theoretical lessons mixed with exercises and project work.
07
Assessment Method
Written exam lasting 60 minutes; minimum passing score 70%.
08
Duration
60 minutes exam.
09
Certification
Certificate of exam passing (with possible associated fee).
10
Expected Outcomes
Ability to utilize Python in real-world data science, AI, and software development projects, ensuring quality and implementing complex solutions.