At the end of the course, the student should be able to:
– be knowledgeable about the open source analytical tools in business
– detect the advantages and disadvantages of using an open source analytical tool
– be proficient in one of the most important open source analytical tool of data science: Python
– extract, manipulate, analyze, visualize and report financial data using Python
– know how to process data and prepare basetable for machine learning project using Python
These competencies and/or skills contribute to the following learning objectives
– 1.C Communicate effectively in English
– 3.A Breakdown complex organizational problems using the appropriate methodology
– 5.C Employ state-of-the-art management techniques
– 7.A Demonstrate an expertise on key concepts, techniques and trends in their professional field
– 7.C Effectively apply in-depth specialized knowledge to take advantage of contemporary opportunities in their professional field
– 7.D Be a reference point for expertise-related questions and ambiguities
.
Run Python programs in a PC environment
• Read raw input files in various formats and create Python datasets
• Create new variables in the data step
• Use Python procedures to describe data numerically and graphically
• Annotate Python output with informative titles, labels, and formats
• Work with datasets: sort, subset, merge, and re-format datasets using Python
• Use Python scripts for basic statistical inference
• Export data and output to other computers and softwares
