Introduction to Data Science

Learn all the tasks involved in the day-to-day work of a Data Scientist, using a simple but significant project throughout the course. The topics of Exploratory Data Analysis and Machine Learning are explored in cursory detail to provide motivation for the courses to follow. Walk away building a model for a pedometer (day 1), able to use your model to read numbers from images (day 2).

This Wizeline AI Academy course is in partnership with:

Lecturers

Juan Orozco

Juan Orozco

Juan spent several years at Hewlett Packard (HP) as Sr. Analytics Consultant, he filed 3 patents and wrote a defensive publication. Juan earned a M.Sc. in Statistics and Operational Research at the University of Edinburgh with distinction. He is currently a lecturer in the Industrial Engineering department of ITESM.

Ana Costilla

Ana Costilla

Ana has held Data Science and Engineer roles at Accenture and Tec de Monterrey, while also being one of the creators of the Data Science/Big Data department at Tec de Monterrey. She earned a Master of Sciences in Big Data Science from Queen Mary University of London. Ana’s main interests are in machine learning, network analysis and user behavior modeling.

Ricardo Ocampo

Ricardo Ocampo

Ricardo’s recent work has focused on improving processes applying artificial intelligence, such as automatic detection of diabetic retinopathy and cancer. Ricardo is published in Intelligent Data Analysis, Iberoamerican Congress on Pattern Recognition, among other journals. He previously worked as researcher at EPFL, Switzerland and earned a M.Sc. in Computer Science at ITESM.

Ricardo Magaña

Ricardo Magaña

Ricardo got interested in machine learning while analyzing data from proton - antiproton collisions at Fermilab and the search of supersymmetric particles with CERN data. Prior to Wizeline, he created credit models at Kueski and deep learning at Hewlett-Packard. His interests are in Machine Learning, Algorithms and HPC. Ricardo earned his PhD in High Energy Physics from Cinvestav.

Pre-requisites for Introduction to Data Science

Fluent in english icon

Fluent in English - the entire course is taught in English.

Engineer icon

Be a statistician, mathematician or software engineer who wants to delve into machine learning.

Statistician icon

Have basic understanding in probability, statistics, and basic scripting knowledge (e.g. Python, Matlab, R, Julia).