Data Science Training

Data Science Training – Dutch Data Science Week

During Dutch Data Science Week, several data science training sessions take place where data scientists can learn about topics like GDPR & Data Privacy, Signal Processing, and Deep Learning.

GDPR & Data Privacy – Monday, May 28

Signal Processing Training – Monday, May 28

Deep Learning – Tuesday, May 29

GDPR & Data Privacy – Monday, May 28

The GDPR changes the rules for collecting and using customer data, impacting every business.

If you work in marketing & sales, HR, consultancy, or another field with access to private, or sensitive information, you need to know all about GDPR. This one-day course facilitated by experts in IT security, data science, and IT law provides an in-depth GDPR overview from three different angles. You’ll learn how to organize data privacy in your organization, the right way.

Because the GDPR affects almost everyone, we’ve designed this training for a broad audience.

What is GDPR? What does GDPR mean for my business?

The General Data Protection Regulation (GDPR) is coming. The regulation, adopted on April 27, 2017, becomes enforceable from May 25, 2018. The impact and changes for some organizations will be far-reaching.

Customers will have the right to be forgotten, therefore organizations must ask for GDPR consent and implement data protection by registering their use of data.

Learn how the regulation affects the majority of the European companies, since almost every company in one form or the other processes privacy-sensitive data.

Q: Is GDPR & Data Privacy training right for me?

  • Yes – if you are in marketing, sales, HR, finance, or IT
  • Yes – if you are a manager, CEO, CIO, etc.
  • Yes – if you are a business decision maker

Q: What will I achieve by completing this training?

You will learn:

  • The principles and most important articles of the GDPR
  • Purpose vs. proportion in data collection
  • Privacy consent, by design, and by default
  • The difference between data controllers and data providers
  • Processing agreement limitations
  • The advantages of data portability

You will gain hands-on experience in:

  • Reviewing concrete cases to learn potential GDPR impacts and changes
  • Determining how your company operates, what it can do, and should do
  • Know how to become and stay GDPR compliant

You will develop the skills to:

  • Assess whether data collection is allowed
  • When to ask for consent and when not to
  • Reason about privacy by design
  • Setup and maintain a data processing register

Signal Processing – Monday, May 28

In the rise of Big Data, not only the amount of data but also its diversity is ever increasing. Beyond ‘traditional’ data consisting of samples of a fixed number of interpretable variables, there is data such as free text, time series (financial transactions, power usage), audio (speech), images and video. These so called signals typically need to be processed such that meaningful variables can be extracted and structured prior to further usage in data analyses and machine learning applications.

Workshop Signal Processing

Topics

This workshop is focused on making powerful data representations from signals for machine learning applications. In two consecutive parts, we will focus on feature engineering and feature learning in which we touch upon the following subjects, for all of which Python code is provided:

  • feature extraction using convolution and Fourier analysis
  • building bag-of-visual-word models from images
  • feature learning for dimensionality reduction
  • end-to-end training of deep convolutional networks
  • applying the feature -engineering and -learning techniques for time series, speech, and image classification

Workshop Signal Processing

For who is this workshop?

The workshop is suitable for data scientists with knowledge and/or experience in applying machine learning with python (e.g. numpy, scipy, scikit-learn, pandas).

Deep Learning – Tuesday, May 29

Description

Deep learning introduction with Python, Tensorflow and Keras with a focus on Recurrent Neural Networks and LSTMs.
Every theory part is complemented by a hands-on session, the goal is that you become familiar with the theory but also learn the how to apply the theory in practice with several exercises.

Curriculum

– Deep Learning basics (Theory)
– Keras API with image classification (Hands-on)
– Neural networks in practice (Theory)
– Predicting bank term deposits (Hands-on)
– Recurrent Neural Networks (Theory)
– Forecasting airline passengers with RNNs (Hands-on)
– Long short-term memory (Theory)
– Human activity recognition with LSTMs (Hands-on)
– NLP sentiment classification with LSTMs (Hands-on)
– Introduction to Gated recurrent units (Theory)
– Q&A

Some of the things you will learn are:

– The Keras API
– Pragmatic best practices when using Deep Learning models.
– Recognize cases when Recurrent Neural Networks are useful.
– Pre-process time-series data for an RNN or LSTM.
– Combine several time-series for a single RNN or LSTM model.
– Use many-to-one RNN and LSTM models.
– Use many-to-many RNN and LSTM models.
– Process text data for an RNN or LSTM model.

Prerequisites

– Experience in Python is advised for the hands-on sessions.
– Experience with Machine Learning concepts (e.g. regularization, overfitting, feature scaling, hyperparameter optimization)
– Basic familiarity with Deep Learning

Activities

The course is dynamic with ideas exchanging and open communication. There are also some fun activities based on the course content.

Instructor

The workshop will be given by Rodrigo Agundez. Data Maverick at GoDataDriven. Rodrigo has been giving training sessions and workshops for several years now, he gave a Deep Learning Tensorflow workshop during the Data Science Summit Europe 2016 in Israel, is one of the current trainers for the Data Science with Python, the time-series lecture and Deep Learning training (GoDataDriven).

In addition, as a consultant, he has seen many use cases and can help you with specific questions that relate to using Data Science in practice, productizing models, etc.