Data Science Bootcamps Germany

In this comprehensive data science program, individuals will learn a variety of critical skills to become a data scientist. Students will learn to code with Python, create machine learning models, automate tasks like web scraping, and interact with databases using SQL. Topics include data analysis with Python's Numpy and Pandas packages, data visualization, predictive analytics, machine learning, SQL querying, Python automation, and web scraping. Students will learn hands-on by working in Python and SQL and will complete the program with the skills they need to enter the field of data science. This program offers flexible scheduling and provides a free retake for students to refresh the material.

Students in this program will learn important concepts and tools for business analytics, machine learning, and data engineering included (but not limited to) Python, Tableau, and SQL. They'll also explore data collection, inferential statistics, and deep learning.

In this data science bootcamp, students will build programming skills and data analysis skills using Python. This course is open to beginners and is meant to get individuals up and running with Python programming and data science to generate insights from data. Topics covered include programming fundamentals, working with data frames, data analysis, data visualization, and statistical analysis. This course offers flexible scheduling options and a free retake for students to refresh the materials.

Learn to problem solve, and effectively communicate, like an analyst. This course teaches you to use industry-standard tools to make ethical, data-driven decisions. Experience hands-on training to master SQL, Excel, Tableau, PowerBI, and Python – tools listed in virtually every data analytics job posting across industries.

Data Science Flex is a 6-month part-time program that will prepare you to to be a data scientist. This program comes with a tuition back guarantee if you can't land a job within 6 months after your graduation. To be accepted into this program, you will have to complete 3 weeks of prepwork and pass an assessment test before you start. Once you're accepted, you'll learn how to clean data using Python, query data using SQL, learn how to train and evaluate supervised and unsupervised machine learning models, and choose a track to specialize. You can deepdive in natural language processing, big data and spark, and deep learning with TensorFlow and Keras.

The Fullstack Academy Artificial Intelligence & Machine Learning bootcamp provides current and prospective data professionals with the in-demand skills to specialize in this lucrative, dynamic, and rapidly-growing field. Over 26 weeks part-time, students will learn practical and theoretical machine learning concepts using real-world tools—graduating with the working knowledge and experience needed to qualify for a range of data roles, including those concentrated in AI. Students will also receive professional career coaching support for up to a year following graduation to help build and maintain an ideal, specialized career path in the industry.

Python is used for a different project each week to teach students data analysis and machine learning. Topics in the course include Pandas, SciKit-Learn, model math, and Deep Learning.

The DSR curriculum covers three dimensions of data science: engineering, analytics, and business. Within each of these disciplines are included sub-topics ranging from Python and SQL to machine learning, probability and statistics, and technical and presentation skills.

This nine-week bootcamp gives participants the skills they need to join data science teams or accelerate their current career growth. Learn how to convert raw data into insightful information. Specific course topics include understanding basic statistical theories, using Python to write code, using seaborn and matplotlib to visualize data, using SQL to develop databases, publishing findings, data processing, using performance metrics, overfitting, unsupervised learning, managing images and text, understanding neural networks, developing computer vision, machine learning, and a capstone project.