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Forecasting in Python: from Linear Regressions to Time Series

Objectives

  • Invite the student to use and understand the statistical and computational qualities of the main algorithms associated with linear regressions and time series.

  • To familiarize the student with some aspects of the mathematical language used in Data Science and Machine Learning.

  • Train the student in the programming techniques necessary to implement the models studied.

Syllabus

1. From the implementation point of view the student will learn:

  • Programming: Basic Python

  • Most used library for data science: Pandas

  • Learning good programming practices for data science

  • Familiarization with common and necessary processes in data science

  • Application of some models without pre-made libraries

  • State-of-the-art libraries application for all the models seen in the course

  • Sufficient knowledge to learn and implement widely used data science models outside of the course

2. From a mathematical point of view we will study the following topics:

  • Linear regressions

  • Statistical qualities behind the least squares estimator

  • Some algorithms to calculate it

  • Time Series: general concepts

  • Buys-Ballot and least squares estimation

  • Moving-Average

  • Associated Vectors

  • ARIMA

  • Invitation to stochastic processes

Contact

CDMX, México.

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