BAN401 Applied Programming and Data Analysis for Business

• Topics

  Programming and data analytical skills are best learned through practice. The topics are oriented on the modern high-demand programming techniques and concepts:

  - Practical and modern introduction to Python:

  Python is a powerful high-level programming language that is easy and fast to learn. This part of the course will gently cover fundamental programming concepts. You will learn how to develop your own code for the practical needs as a part of the professional analytical process.

  Furthermore, the obtained skills will allow you to migrate to any programming language relatively easy since most of the languages are based on similar paradigms.

  Python is not only a perfect base to learn programming, but the most in-demand language in the business-oriented job market. You will learn basic parts of the Python language, packages and libraries that are most required for efficient solving a variety of problems. In addition, you will learn how to combine Python with other analytical tools for the high-level flexibility. These and many other required techniques will be covered in this part of the course.

  Applied programming skills will give you a confidence and privilege in the job market and in your future career.

  - R-based programming and data analysis:

  R is a powerful free software environment to tackle data analysis. Nowadays it has rapidly expended into the enterprise market and good skills in R programming are required for the majority of analytics oriented jobs. R covers all data manipulation, statistical modeling and data visualization techniques that are required in business-oriented data analysis. In this part of the course, you will learn a variety of R-based data processing, visualization and programming techniques. Being designed expressly for data analysis, R became one of the most in-demand languages in the job market. It is extremely popular due to the huge number of open-source packages. The most important and fundamental (must-know) R programming tools and techniques will be covered in this part of the course.

  - SQL (Structured Query Language)

  SQL is a special-purpose programming language for managing data. It is a "must-know" language for creating, accessing, retrieving and manipulating data in databases. Nowadays, SQL is required to know as well as Python and R for the most of data analysis-oriented jobs. The reason is that most companies and organizations work with big datasets that are managed by relational database management systems (RDBMS) based on the SQL-standards. In this part of the course, students will learn the most important aspects of SQL language.

  In the end of the course, students will have practical skills in querying and managing data using SQL.

  - Integration of Python, R and SQL

  An integrated big data analysis requires knowing how to exchange data and analytical results between different apps. In this part of the course, students will get an overview of how to use Python, R and SQL mutually with different software tools.

• Learning outcome

  The aim of the course is to develop practical skills in applied programming and data analysis for use in business and economics. Students will learn different techniques from scratch based on modern tools such as Python, R, and SQL.

  Skills in applied programming are in high demand in the job market. Companies are looking for analysts and managers with good IT knowledge and expertise. In this course, students will learn in practice the most important programming and data processing techniques that will allow them to be fluent in applied data analytics. By the end of this course the students will be able to:

  Knowledge:

  • understand fundamental programming concepts;
  • understand data management concepts;

  Skills:

  • get fundamental skills to work in applied analytics;
  • analyze different problems and implement the required programming solutions;
  • apply most important programming languages and IT tools for data analysis;
  • develop the programming-based solutions for the variety of analytical purposes;

  General Competence:

  • understand the foundation of good programming skills and techniques for applied data analysis and decision-making processes;
  • evaluate, verify, test and analyze different types of code;
  • discuss basic principles and tools of applied programming in analytics.

  Students will get a fundamental knowledge and practical experience in applied programming required for real-world applications. For example, practically oriented data analysis is in high demand in business analytics, decision-making support, capital budgeting, supply chain management, marketing, customer targeting, financial analysis and audit, investment banking, credit analysis, etc. The growth of digital technologies has enabled to collect massive amounts of data in the given areas and the knowledge of applied programming and data analysis tools are fundamental to retrieve, systemize, process and visualize data efficiently.

  The gained skills in this course will allow learning and understanding in a short period any programming and analytical tools required in real company situations. Based on the acquired experience, students will have an advantage compared to many candidates in the job market.

  The course is focusing on the foundations of applied programming and data analysis for business. The main goal is to develop fundamental skills that are in high demand in the job market covering, i.e., data, business and system analysis, business intelligence, risk management, etc.

• Teaching

  The course format is digital (with no in-classroom lectures). It is compulsory for students to follow all information posted on the BAN401 page in Canvas: it is considered that students are aware of all BAN401-related information posted in Canvas. Teaching will consist of lectures with a gentle introduction to fundamental programming and data analysis concepts.

  Collaboration in groups is crucial to mastering the materials of this course. Throughout the course, students will work in groups to learn how to collaborate on programming and analytical projects and master course materials. The work students do in groups throughout the course will culminate in a final group-based project. Therefore, it is crucial and mandatory for students to work collaboratively on all assignments and exercises throughout the course to ensure adequate preparation for the final project.

  The group size is normally 3 persons, but a group of 4 can be accepted in special cases. It is the student's responsibility to find and join a group of 3 (or 4 in exceptional cases) persons. Students can search for group members online (for example, using the Canvas Discussions tab) or any other way.

• Recommended prerequisites

  No previous knowledge in data analysis, computer programming and corresponding IT tools are required

• Credit reduction due to overlap

  The course is identical to BUS455. This course is a continuation of BUS455 and the total number of attempts applies to the course (not the course code).

• Assessment

  The course assessment will be a final group-based project. Collaboration in groups, active contribution from all group members, and organization of your work are crucial to mastering the materials of the course and successfully implementing this knowledge in your career. One final grade will be given for the project.

  It is mandatory to submit the final group-based project in a group of 3 (or 4 in exceptional cases) people. This means that the group size of 3 persons is the minimum, and the group size of 4 persons is the maximum. No exceptions will be made. Submitting group-based projects in groups not consisting of 3 or 4 persons is not allowed.

  It is the student's responsibility to find and join a group of 3 (or 4) people. The course administration does not form groups or assign students to groups.

  The final group-based project must be written in English. Students who wish to retake the course must also retake the final group-based project.

• Grading Scale

  Pass-Fail

• Computer tools

  The course contains an intensive use of open-source (free) software related to Python, R, and SQL. Details regarding the required software will be provided during the course.

• Literature

  Books (free; available online):

  • Swaroop, C. H. "Byte of Python" (for Python 3). 
  • Venables, W. N., & Smith, D. M. "An introduction to R". 

   Recommended official free-access tutorials:

  • The Official Python 3 Tutorial: https://docs.python.org/3/tutorial/
  • An Introduction to R: https://cran.r-project.org/doc/manuals/r-release/R-intro.html
  • SQL Tutorial: http://www.tutorialspoint.com/sql/sql_tutorial.pdf