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Data Science Accelerator Program

Data scientists aren't a nice-to-have anymore, they are a must-have. Up until now, there was not a clear definition of what a Data Scientist is exactly. In order to create a benchmark for data scientists, GoDataDriven developed the Data Science Accelerator Program, the best way to assess Data Science skills and take them to the next level.

By combining in-depth lectures with hands-on hackathons, the Data Science Accelerator Program delivers excellent data scientists who are able to transition organizations into DataDriven enterprises.

Audience Profile Data Science Accelerator Program

You will benefit from the Data Science Accelerator Program if:

  • You are a data analyst, business analyst, developer or administrator
  • You have a background in math, statistics, or other quantitative disciplines
  • You have basic Data Science skills
  • You have basic programming skills (R/Rstudio and iPython)
  • You have experience with SQL and basic UNIX or Linux commands

Achievements Upon Completion

The public Data Science program consists of 10 full training days consisting of 5 core lectures and 5 hackathons and spans 5 months. Every month one-day training and one-day hackathon take place.

The program not only focuses on sharing Data Science knowledge but also on sharing development best-practices based on years of hands-on experience with the implementation of Data Science applications within organizations in many industries

After completing the 10-day program, you will know:

  • How to work with the Ipyhton stack
  • How to work with the probability stack
  • How to work with machine learning
  • How to work with ensembles and neural networks
  • How to work with time series

You’ll gain hands-on experience in:

  • Web development (Flask and Pandas as backend)
  • Solve multi-armed bandit problems
  • Create a predictive model and put it into production
  • Using tools and technology including Hadoop, Spark, R, Python, Elastic, Neo4j, Scala, Cassandra, D3, Riak, and Storm

You’ll gain the skills to:

  • Develop various types of Data Science models
  • Code reviewing your automation for nonlinear models
  • Building a method that outperforms Random Forests

Additional Information

Training Program

The upcoming training program starts in May 2017 and includes the following dates:

Day 1: May 16, 2017 - Training: The Ipython Stack
Day 2: May 30, 2017 - Hackathon: Web development with Flask and Postgres as a Backend
Day 3: June 20, 2017 - Training: Probability Stack
Day 4: June 27, 2017 - Hackathon: Solve Multi-Armed Bandit Problems
Day 5: September 5, 2017 - Training: Machine Learning
Day 6: September 19, 2017 - Hackathon: Create a Predictive Model, wrap it in an API and put it into Production
Day 7: October 3, 2017 - Training: Ensembles & Neural Networks
Day 8: October 31, 2017 - Hackathon: Use Different Methods to Recognize Handwritten Digits
Day 9: November 14, 2017 - Training: Time series
Day 10: November 28, 2017 - Hackathon: Predict an optimal Portfolio for the Stock Market

Everything else you should know:

! Please note, that you need to bring your own laptop for this training.

This laptop should meet the following requirements:

  • At least 2GB RAM (4GB or more preferred),
  • 15GB of free hard disk space,
  • VMware Player 5.x or above (Windows)/ VMware Fusion 4.x or above (Mac),
  • Your laptop must support a 64-bit VMware guest image. If the machines are running a 64-bit version of Windows, or Mac OS X on a Core DUO 2 processor or later, not other test is required. Otherwise, VMware provides a tool to check compatibility, which can be downloaded here.
  • Your laptop must have VT-x virtualization support enabled in the BIOS,
  • If running Windows XP: 7-Zip or WinZip is needed (due to a bug in Windows XP's built-in Zip utility),
  • USB port accessible.

Xebia Academy (based in Hilversum, Amsterdam area) is an official training partner of Cloudera, the leader in Apache Hadoop-based software and services.

https://training.xebia.com/data-science/data-science-accelerator-program