Introduction to Data Science for Software Engineers

Next Course: August 19-20, 2017 @ GDL

Learn all the tasks involved in the day-to-day work of a Data Scientist, utilizing your existing skills as a software engineer. You’ll learn about supervised and unsupervised learning while completing a simple but significant project throughout the 2-day course.

Learn with the Best

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.

Rodrigo Ledesma

Rodrigo Ledesma

Rodrigo has worked on feature engineering and social network analysis for credit model scoring at Kueski, as well as data analysis/visualization for decision making regarding stock markets at Banamex. He graduated from financial engineering at ITESO and his main areas of focus are data ingestion and pipeline engineering.

Ana Costilla

Ana Costilla

Ana has held Data Science and Engineering roles at Accenture and Tec de Monterrey. She earned an M.Sc. in Big Data Science from the Queen Mary University of London, and her main interests are machine learning, network analysis, and user behavior modeling.



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.

Crash Course ScheduleAcademy classes are from 8:00-7:30pm on Saturday 19th & Sunday 20th - don’t be late!

Day 1


Data science overview

Exploratory data analysis and statistics

Machine learning

Unsupervised learning and dimensionality reduction

Supervised Learning

Day 2

Introduction to Neural Networks

Training Neural Networks

Convolutional neural networks



Be a software engineer or well-versed in coding who wants to delve into machine learning.


Have basic understanding in probability, statistics, and scripting knowledge. Basic Python fluency required.


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

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