Data Science Full-time Course

Hands-on tour de force into Data Science.

Starting: 2017-09-04

Apply now

Build Your First Machine Learning Application

Get introduced to data science and machine learning with our 2-day Bootcamp at Mosaik Academy. Learn how to develop, evaluate and optimise different machine learning algorithms with ease. No prior coding experience needed!

Data Scientist Needed

“Data Scientist” is the best job in the US according to Glassdoor’s job rankings. There is a huge shortage of qualified data scientists in the field in both the States and Europe, which means attractive compensation packages. Our Bootcamp is a great opportunity to learn the basics to start working on projects and securing a job in the field.

Do I need any programming experience to attend?

If you have any prior programming experience (preferably in Python) you are good to go. If you do not have any prior programming skills you can still attend our workshop but we strongly recommend to take a look at Codecademy's Beginner Python course. It is free and in a couple of hours, you can complete Unit 1-5 and 7-8. If you have more time, you can finish the whole course.

What will you learn?

  • Essential data science skills focusing on real world use cases
  • Getting, preparing and analyzing data sets
  • Basic theoretical background of machine learning techniques
  • Visualization techniques to present your findings

What technologies will you use?

  • Easy to use online notebook (Jupyter)
  • Data storing libraries for fast data processing
  • Python and all the machine learning libraries written in it
  • Visualization libraries for gaining data insight more easily

Who is it for?

  • Anyone who is interested in Machine Learning and thinking about a career change
  • Analysts who are familiar with statistics and want to build scalable prediction models
  • Developers who want to learn about machine learning libraries and practices


Building data pipelines and analytical systems at massive scale. My experience lies in distributed systems, focusing on data-driven large-scale systems (10.000+ nodes).

Szukacs Istvan

CTO @ StreamBright Data

Trained as a finance-investment professional, born with entrepreneurial mindset and spirit. Had experience with multinational advisor firms (Big4s); later turned to small businesses and helped them start their journey in Budapest and San Diego.

Adam Jermann

CEO @ StreamBright Data

Prioritize, scope and design strategic projects and internal applications for the Marketing Solutions sales team and business, working directly with Engineering, Product, and the Global Sales Organization.

Mariah Walton

Data Scientist @ LinkedIn



Introduction to Machine Learning

  • Overview of machine learning, introduction to supervised vs. unsupervised learning concepts. Basics of classification vs. regression algorithms and how they can be used in real-world applications.

Cleaning, Pre-Processing and Analyzing Data

  • Cleaning and mining of real-world data, pre-processing techniques. How to do exploratory data analysis with summary statistics and visualization.

Building Your First Classification Model

  • Building your first machine learning model for classification. Learning the K-Nearest Neighbor (KNN) algorithm and the how to measure its performance.

Fine-Tuning Models

  • Improving your model with fine-tuning the model parameters and selecting the best variables. The introduction of the bias-variance trade-off.

Mini-Project - Build Your Own Model

  • Build and optimize a classifier on a new real-world data set.

Evening Fun

  • Drinks with fellow participants and lecturers.

Classification with Decision Trees

  • Learn one of the most popular classification tool the decision trees (Decision Trees, Random Forests, Ensemble models, Extremely Randomized Trees).


  • Automatic grouping of similar objects into sets without any knowledge of how many categories you have. Learning how to visualize your results in 2D when you have many dimensions.


  • Building prediction models with multivariable regression methods.

Expert Talk and Panel Discussion

  • We invite a senior data scientist to share her experience on how to apply data science in business environments. After a presentation, we will sit down with her for an open panel discussion about the field to answer any questions the participants may have.

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