Certificate in Python Programming Advance Level
Full-Time
Applicants must have basic computer knowledge and python programming fundamentals. Applicants with one-year experience in the aforementioned fields will be given priority.
Programme Objective
This programme is a skill development programme, which enables students to deal with massive amounts of datasets to analyse and explore in the field of data analytics. As the field of data science grows rapidly in the IT industry, this course is most demanding, as it provides practical hands-on training to develop the skills in the area of machine learning, deep learning as well provide exposure on the advance python libraries.
Applicants must have basic computer knowledge and python programming fundamentals. Applicants with one-year experience in the aforementioned fields will be given priority.
Assessment
Continuous Assessment with Feedback (diversified) will be used for all courses.
Certificate
Upon successful completion of this course, a certificate of completion will be awarded to candidates.
Programme Outcomes
Upon completion of the course, through assessment activities, participants will show evidence of their ability to:
- Develop in depth knowledge of Python Programming and related concepts.
- Write advanced python programs for enterprise development.
- Use the concepts of python programming in machine learning and deep learning.
- Filter the data and perform data analytics on datasets, to analyse the business requirement.
- Develop and demonstrate the industry level, data science projects using advance level concepts.
Programme Design
The course is scheduled over eight weeks, will be delivered on full-time (day and/or evening), online or face-to-face modes in accordance with NUST rules. The following topics will be covered in the programme:
Lessons
- Python Object Oriented Concepts
- Exception Handling and Assertion
- Data Wrangling, Data Cleaning
- Python Libraries – Pandas, Numpy, Scipy, Scrapy, Plotly, Selenium, Beautiful Soup
- Advanced Analytics: Business Analytics and Decisions
- Exploratory Data Analysis - Visualization and Exploring Data
- Statistical Measures, Distribution and Sampling
- Predictive Modelling and Analysis
- Feature Selection and Extraction
- Factor Analysis, Directional and Functional Data Analysis
- Project Application using Python