Data Analysis in Python
- Collect, store, process and analyze data automatically with the use of scripting languages.
- Develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques.
- Solve economic, financial and managerial problems using best practices of data analysis using modern computational tools.
- Can identify the data needed for addressing the financial and business objectives.
- Introduction to Python
- Beginner Data Analysis in Python
- Intermediate Data Analysis in Python
- MOOCCoursera MOOC: “Introduction to Data Science in Python” available here: https://www.coursera.org/learn/python-data-analysis [hereinafter “MOOC”]. Final progress [hereinafter “progress”] of the MOOC is based on several Programming Assignments. So, the student after finishing the course can get the progress in the interval from 0% to 100% including. Hint: The MOOC lasts for 4 weeks. Each student should register in the MOOC strictly within his/her corporate e-mail address (ending on @edu.hse.ru or @hse.ru) and your real First & Last names. The MOOC should be finished, and the progress should be submitted 7 days before the first day of the exam week (or earlier). The progress check and submission procedure are organized in LMS.
- Individual Project in Python (Labs in Python)Individual project consists of 18 computer exercises (in Python). Student should submit one file in Jupyter Notebook format (*.ipynb) with solutions. Each task has its own points. The final grade is calculated on the basis of the points’ sum. The maximum sum is 60. The student gets an integer grade for each task of a Project. If the answer on the particular question in the Project task is not full (not all requirements of the task are done), then the student gets 0 (zero) points for such a task/question. Moreover, the cheating is strongly prohibited. In case of cheating - the student gets 0 (zero) points for the whole Project.
- 2022/2023 1st module0.5 * Individual Project in Python (Labs in Python) + 0.5 * MOOC
- Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.
- Nelli, F. (2015). Python Data Analytics : Data Analysis and Science Using Pandas, Matplotlib and the Python Programming Language. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1056488