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 premade libraries

Stateoftheart 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

BuysBallot and least squares estimation

MovingAverage

Associated Vectors

ARIMA

Invitation to stochastic processes