Introduction to Data Science for Software Engineers

Next Course:
Saturday, March 25 - Sunday, March 26, 2017
@ CDMX

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.

This Wizeline AI Academy course is in partnership with:

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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.

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.

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.

Course Schedule

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

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

Statistician icon

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

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The application process includes a Hacker Rank challenge to assess your understanding of programming and basic statistics. The course is limited to 24 students.

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Working on some cool data stuff