Class Statistics

The Statistics Curricular Unit is aimed at the development of core competences in Statistics Applied to Management, preparing the students to the new data-driven and data intensive organizational contexts in accelerated transformation by the synergizing of Statistics, Data Science, Business Analytics and Business Intelligence. In that sense, the CU operationalizes the main concepts, methods and technologies, applying them to management problems worked in class with the students.

1. Statistics and Data Science
   1. Statistics, Data Science, Business Intelligence and Business Analytics
   2. The Big Data Revolution and the 5Vs of Big Data
   3. The use of Analytics Engines in statistical analysis
2. Statistics with Python
   1. Statistical Variables and Databases
   2. Introduction to the Python module: Pandas
   3. Statistical Databases - Pandas DataFrame
   4. Use of Analytics Engines to perform statistical analysis
3. Descriptive Statistics and Inferential Statistics
   1. Descriptive and Inferential Statistics
   2. Applications of inferential methods to decision making problems in management
      • Test to one mean
      • Test to two means
      • ANOVA test
      • Nonparametric tests
4. Knowledge Extraction from Data
   1. Classification problems and regression problems
   2. Use of Machine Learning for knowledge extraction from data
   3. Relevance of Machine Learning for Statistics applied to Management in the context of Organizations Management

Development of the CU with practical application to the present context of Business Analytics and Business Intelligence, with the subject matter taught from cases of application of Statistics to decision-making in the business context. Connection between the CU and the international R&D project "Data Science and Machine Learning with Python" (https://sites.google.com/view/datasciml) for the production of scientific and pedagogical materials, including technology, articles and webinars (playlist: https://youtube.com/playlist?list=PLmLUR-kyF1qVVBeLSiNUu5kpGTk84EYZ8), linked to the areas of Data Science, Business Intelligence and Machine Learning, synergizing teaching and research.

• McKinney, Wes (2018). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly, USA.
• Vasiliev, Yuli (2022). Python for Data Science: A Hands-On Introduction. No Starch Press, USA.
• Briggs, Wade (2022) Data Science with Python. Kindle E-Book, Amazon.