Data Science Summer School 2022

Intensive and interactive online courses to expand your data knowledge and network

Dive into the Exciting World of Data Science

Explore the fundamental skillsets and knowledge essential to navigate today's data world and investigate the latest research and methods of insight finding

The Data Science Summer School is a series of theoretical and practical workshops on the exciting methods and technologies currently employed by industry, government, and civil society to address the world's most complex problems. Data Science Summer School will provide the following benefits for participants:

  • Single day (4-hour) workshops covering theory and application;
  • Instruction from professors, researchers, and industry experts;
  • Networking opportunities with other Summer School participants;
  • Certificate of Attendance for participation in the Summer School;
  • And best of all, attendance is fully sponsored;

The Data Science Summer School is organized by the Hertie School Data Science Lab with funding and support from the Hertie School.

Fundamentals of Data Science

The Summer School will begin with an introduction into programming and the mathematical foundations essential for the success of data scientists. This is part of the technical preparation for the Master of Data Science for Public Policy programme at the Hertie School and is also open for the general public.

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Introduction to R Programming

R is one of the leading programming languages for data analysis and statistics. It is open-source, and widely used by professional statisticians. This course will introduce you to the basics of the R language such as data types, techniques for data manipulation, and how to implement fundamental data analysis tasks.

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Introduction to Python Programming

Widely considered as one of the best programming languages for beginners, Python is a general purpose language that is currently the best choice for machine learning and deep learning research and application. The course will walk you through the basics of this language and how to use it to solve programming challenges.

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Calculus for Data Science

Many data science challenges, particularly those in machine learning and deep learning, are essentially optimisation problems. Ever wondered how exactly a logistic regression algorithm is implemented? Or how to use gradient descent? To understand how these methods work, you need to use concepts from Calculus - gradient, derivatives, limits, and chain rule.

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Linear Algebra for Data Science

How can we run operations and analysis on large quantity of data? We need matrices to represent these data, process the network structure and learning operations to mine for insights. Linear Algebra is an essential branch of mathematics to help make running algorithms on massive datasets feasible.

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Probability for Data Science

An absolute must-have knowledge for data scientists is probability. It gives us the language and tools to quantify the uncertainty of events and reason in a principled manner. Machine learning is about developing predictive models from uncertain data, with imperfect or incomplete information. We can manage this uncertainty using the theories and tools of probability.

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Applications of Advanced Data Science

We will conclude the Summer School by venturing into the latest advanced machine learning and deep learning techniques to tackle Computer Vision and Natural Language Processing problems

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Deep Learning for Multimodal Systems

Deep Learning has implemented a wide range of applications and has become increasingly popular. Despite the extensive development made for unimodal learning, it still cannot cover all the aspects of human learning. Multimodal learning helps to understand and analyze better the processing of information when various senses are engaged.

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Transformers for Natural Language Processing

In recent years, the transformer architecture has proved to be revolutionary in the field of Deep Learning. This course investigates in detail this exciting deep learning technique for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers

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Generative Adversarial Networks

GANs are an exciting and rapidly changing field, delivering on the promise of generative models in their ability to generate realistic examples across a range of problem domains. This workshop will provide an overview of this technology and showcase some of its application.

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Contact Us

Location:

Data Science Lab, Hertie School
Friedrichstra├če 180, Berlin

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