Data Science & AI Bootcamp

Building on the foundations from the first phase of your Data Science & AI Bootcamp, you’ll now become familiar with tools like SQL and Pandas to extract and manipulate data from various sources — ranging from individual files to entire databases. After preparing and cleaning your data, you’ll move on to exploratory data analysis (EDA), where you’ll learn how to uncover insights and patterns hidden within complex datasets.

In this phase, you’ll work with a variety of data visualization libraries, including Matplotlib, Seaborn, Plotly, and Geo-visualization tools to create meaningful and visually compelling representations of your findings. You’ll also be introduced to data cleaning techniques, learn the basics of data ethics, and understand how to design and interpret A/B tests to validate insights and decisions.

This phase concludes with a two-day project focused on exploratory data analysis based on real-world datasets. With an emphasis on the business case, you’ll create tailored recommendations and compelling visualizations designed to communicate your insights effectively to fictional stakeholders.

In this phase, you’ll dive deep into the foundations of machine learning. You’ll explore the core concepts of supervised learning, including regression and classification. You’ll become familiar with different models such as linear and logistic regression, decision trees, random forests, k-nearest neighbors (KNN), and support vector machines. You’ll also learn when to apply each model and understand the assumptions and simplifications behind these algorithms.

Additionally, you’ll explore the basics of unsupervised learning, working with techniques like principal component analysis (PCA), clustering, and dimensionality reduction to identify patterns and structures in complex datasets.

A significant focus of this phase is on model tuning and optimizing predictive models. You’ll learn how to adjust hyperparameters to improve model performance and gain insights into concepts like the bias-variance trade-off, regularization, and cross-validation. You’ll also discover how optimization techniques such as gradient descent and cost functions are applied in practice.

This phase concludes with a four-day group project covering the entire data science lifecycle. Working collaboratively with Git and GitHub, your team will plan milestones and define the goals for your data product before presenting your findings to fictional stakeholders.

In this phase, you’ll dive into advanced concepts of machine learning and artificial intelligence. Building on your existing knowledge, you’ll explore the fundamentals of deep learning and understand how artificial neural networks work. Using TensorFlow and Keras, you’ll learn to design and train your own models to solve complex problems.

A major focus of this phase is on natural language processing (NLP), where you’ll discover how computers can analyze and process human language. You’ll also be introduced to time series analysis, enabling you to make forecasts and predictions based on time-dependent data — an essential skill in many data-driven applications.

In addition, you’ll gain insights into machine learning engineering: you’ll learn how to deploy models into production environments, monitor their performance, and make results accessible through dashboards and cloud deployment (e.g., GCP).

With these advanced skills, you’ll be fully prepared to apply your knowledge in an extensive capstone project in the next phase.

In the final phase of your Data Science & AI Bootcamp, you’ll apply everything you’ve learned in an extensive capstone project. Over the course of four weeks, you and your team will develop a complete predictive modeling project — from data preparation and model training to delivering actionable results.

You’ll deepen your understanding of deep learning and artificial neural networks by building and training your own models. You’ll also explore transfer learning and work with pre-trained models from repositories like Hugging Face, enabling you to efficiently leverage state-of-the-art techniques for various applications.

Another key focus is machine learning engineering: you’ll learn how to deploy your models into production, monitor their performance, and make your results accessible using dashboards and cloud deployment (e.g., GCP).

Finally, you’ll present your findings to fictional stakeholders, strengthening your skills in data communication and stakeholder interaction.

Upon successful completion, you’ll receive the official IHK certificate “Specialist in Data Science & AI”, issued by HKBiS, the educational service of the Hamburg Chamber of Commerce, in addition to your neuefische graduation certificate.