Core Course
BAN602 – Programming for Business Analytics
Course Unit Code: BAN602
Type Of Unit: Core
Level of Course Unit: Graduate
Year of Study: 1
Semester: Semester 1
Number of ECTS Credits: 10
Class Contact Hours: 12
Mode of Delivery
Distance Learning
Prerequisites
None
Course Objectives
The course is focused on providing students with the necessary skills to use Python for business analytics. The course includes a vital aspect of data analysis and visualization, introducing students to Python libraries that are commonly used in the field of data analytics.
The main goal of the course is to introduce students to Python as a high-level programming language and equip them with the necessary skills in structured programming, fundamental data structures, and algorithmic thinking.
The course will introduce students to various Python libraries used for data analysis and visualization. This aspect is essential in today’s data-driven world, where the ability to work with data effectively is highly valuable.
One of the highlights of the course is its practical approach to learning. Students will be taught how to apply their acquired knowledge to address real-world business problems. This application-oriented approach will make the learning experience more engaging and valuable.
The course will provide students with ample opportunities to practice their programming skills through practice problems and exercise sheets. This hands-on approach will help reinforce their understanding of Python concepts and programming techniques.
By the end of the course, students will have a strong foundation in Python programming, data analysis, and visualization. This comprehensive skillset will prepare them to tackle a wide range of business challenges using their newly acquired knowledge.
Overall, the course is designed to provide students with the necessary tools and expertise to succeed in using Python for programming, data analysis, and visualization in a business context. It aligns with the current trend of Python’s popularity as a versatile language for various applications, including data science and business analytics.
Learning Outcomes
- Foundational factual understanding of utilizing Python programming within the domain of business analytics.
- Comprehensive familiarity with factual information, principles, processes, and fundamental concepts related to Python programming in the context of business analytics
- Understand the different types of Python statements and their applications.
- Evaluate and select appropriate Python statements to address various solutions using programming.
- Gain familiarity with the functions in Python and use them to solve complex problems.
- Apply python algorithms to extract meaningful insights from data.
- Possess an extensive and specialized understanding of factual and theoretical aspects of programming with Python in the context of business analytics, while also being conscious of the limitations and boundaries of this knowledge.
Course Content
1st week: Programming environment and tools
2nd week: Code and Markdown in Jupyter Notebook
3rd week: Flowcharts. Chapter 2 of the textbook Automate the Boring Stuff with Python – Chapter
4th week: If. . . elif. . . else conditions. Chapter 2 of the textbook Automate the Boring Stuff with Python – Chapter 2
5th week: For loop. Chapter 2 of the textbook Automate the Boring Stuff with Python – Chapter 2
6th week: While loop. Chapter 2 of the textbook Automate the Boring Stuff with Python – Chapter 2
7th week:Functions in python. Chapter 3 of the textbook Automate the Boring Stuff with Python – Chapter 3
8th week:Descriptive statistics. Part III Descriptive statistics of the textbook Descriptive statistics
9th week:Drawing Graphs. Part III 6. Drawing Graphs of the textbook 6. Drawing Graphs
10th week: Data Wrangling. Part III 7. Data Wrangling of the textbook 7. Data Wrangling
11th week: Project (Part I)
12th week: Project (Part II)
Course Features
Weekly self-assessment activities :
On a weekly basis, students will have the possibility to engage in self-assessment activities to judge their own level of understanding of the concepts covered so far. The weekly self-assessment activities provide immediate feedback.
Weekly interactive activities (20%)
Weekly interactive activities account for 20% of the grade, and will be graded almost instantly by the instructor with appropriate feedback.
Final project (30%)
There will be one final project as assignment which accounts for 30% of the final grade.
Final exam (50%):
Students will be provided with a problem statement and the corresponding data set and will have to develop a solution for that problem using a web-based interactive development environment (Jupyter Notebook).
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