Data Science with Python
Course Overview
This comprehensive course aims to equip students with the necessary skills and knowledge to excel in the field of data science using Python. From the fundamentals of Python programming to advanced topics such as machine learning, data manipulation, and time series analysis, students will gain a deep understanding of key concepts and practical applications in the field of data science.
The purpose of the three-month certificate course in Data Science with Python at KAAF University College is to equip participants with the essential skills and knowledge required to analyse and interpret data using Python programming language. The course aims to provide a comprehensive understanding of data science principles and techniques, as well as practical experience in using Python for data manipulation, visualization, and analysis. Participants will develop the ability to extract insights and make informed decisions based on data, preparing them for a career in the rapidly growing field of data science.
- Introduce participants to the fundamental concepts and principles of data science, including data types, data structures, algorithms, and statistical analysis techniques.
- Develop proficiency in using Python programming language for data manipulation, cleaning, transformation, and analysis.
- Explore data visualization techniques and tools in Python to effectively communicate insights and trends to stakeholders.
- Teach participants how to use libraries and packages such as Pandas, NumPy, Matplotlib, and Seaborn for data analysis and visualization.
- Provide hands-on experience in working with real-world datasets to solve practical data science problems and challenges.
- Introduce participants to machine learning concepts and algorithms, including supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics.
- Develop skills in building and training machine learning models using Python libraries such as Scikit-Learn and TensorFlow.
- Implement best practices in data science, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation.
- Foster a problem-solving mindset and critical thinking skills in approaching data science projects and tasks.
- Prepare participants to effectively communicate their findings and insights to non-technical stakeholders through data visualization, reports, and presentations.
By adhering to these objectives, the three-month certificate course in Data Science with Python at KAAF University College aims to provide participants with a solid foundation in data science principles and practical skills in using Python for data analysis. Participants will be equipped with the tools and knowledge necessary to pursue a career in data science or further studies in the field.
We understand that life can be busy, which is why we offer flexible class options to fit your schedule:
- Morning classes: Perfect for those who prefer to start their day with a productive learning experience.
- Evening classes: Ideal for those who have daytime commitments and want to learn in the evenings.
- Weekend classes: Great for those who prefer to dedicate their weekends to learning.
The Courses is open to
- SHS School graudates
- Diploma Holders
- Undergraduate students
- Working professionals
- Entrepreneurs
Check the fees here
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Course Timetable
The timetable allows for a comprehensive coverage of all modules, practical application through project work, regular assessments, and exam preparation. Additional review and practice sessions are included to ensure students have a thorough understanding of the concepts and are well-prepared for the final exam and project presentation. The course culminates with a certification ceremony and networking opportunity to connect with industry professionals and explore potential career opportunities in the field of data science.
- Module 1: Introduction to Python
- Module 2: Sequences and File Operations
Module 3: Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions
Module 4: Introduction to NumPy, Pandas, and Matplotlib
Module 5: Data Manipulation
Module 6: Introduction to Machine Learning with Python
Module 7: Supervised Learning – I
Module 8: Dimensionality Reduction
- Module 9: Supervised Learning – II
- Module 10: Unsupervised Learning
- Module 11: Association Rules Mining and Recommendation Systems
- Module 12: Reinforcement Learning
Module 13: Time Series Analysis
Review and Practice Session for Modules 1-7
- Review and Practice Session for Modules 8-13
- Project Work Begin
– Guidance and Assistance with Project Work
– Project Presentation and Evaluation
- Final Exam
- Course Completion and Certification Ceremony
Final Assessments and Evaluation
Course Completion and Certification Ceremony