Data Science and Machine Learning with Python Training in Germany

  • Learn via: Classroom
  • Duration: 5 Days
  • Level: Intermediate
  • Price: From €3,763+VAT
We can host this training at your preferred location. Contact us!

This five day course is aimed at those who are familiar with data analysis and are interested in learning about how Data Science, Analytics, Machine Learning, and Artificial Intelligence (AI) can be used to yield value from data assets.

This course will be of interest if you are interested in developing your own skills to move from analytics to Data Science, or if you are working with Data Scientists and want to learn more about what’s possible.

You will be introduced to key concepts and tools for use in Data Science, including typical Data Science Project lifecycles, potential applications & project pitfalls, relevant aspects of data governance and ethics, roles and responsibilities, Machine Learning and AI model development, exploratory analysis and visualisation, as well as techniques and strategies for model deployment.

Throughout the course you will engage in activities and discussions with one of our Data Science technical specialists. Theoretical modules are complimented with comprehensive practical labs.

Delegates must be existing Python Data users who have attended;

Python for Data Handling (QADHPYTHON)

or have a similar level of knowledge with NumPy and Pandas.

Additionally, we recommend that delegates have attended;

Introduction to Data Science for Data Professionals (QAIDSDP)

in order to understand key data science, machine learning, and AI governance requirements before developing Machine Learning models.

Target Audience

Members of the audience are required to have a some technical expertise such as table structure, working with tabular data in Python, and simple data analysis.

They may be Mid/Senior Leadership seeking a greater understanding of how to implement Data Science within their organization.

They may come from other technical backgrounds such as Data Analysts, Software Developers, and Data Engineers who either work with Data Scientists or are using this course in their journey towards training as a Data Scientist.

In the latter case, audience members may ask for recommendations for their next steps in training towards becoming Data Scientists. We recommend the following refreshed courses which are due to launch in 2023 and 2024 in this suggested sequence:

  • Statistics for Data Analysis in Python
  • Time Series and Forecasting with Python
  • Maths and Statistics for Data Science with Python
  • Practical Big Data Analytics (with Python and Spark)
  • Generative AI Essentials
  • Fundamentals of Deep Learning with Python (followed by selected Python & NVIDIA training)

  • Introduction to Data Science & Machine Learning
  • Introduction to Python for Data Science
  • Descriptive & Inferential Statistics with Python
  • Preprocessing Data for Analysis
  • Supervised Learning: Regression
  • Supervised Learning: Classification
  • Model Selection & Evaluation
  • Unsupervised Learning
  • Ethics for Data Scientists
  • Deploying Models & Insights
  • Where to Go Next

Introduction to Data Science & Machine Learning

  • Explain the role of the Data Scientist and the skillset it requires
  • Describe common application areas of Data Science, and examples of its usage in industry
  • Outline the Data Science process detailed in the CRISP-DM methodology
  • Detail the characteristics of problems which Data Science can be used to solve
  • Define how to evaluate the success of a Data Science Project

Introduction to Python for Data Science

  • Understand why notebooks are often used in Data Science projects
  • Use Python and associated libraries to manipulate datasets.
  • Describe why virtual environments are used
  • Visualise data using Python

Descriptive & Inferential Statistics with Python

  • Understand the role that descriptive and inferential statistics play in Data Science
  • Use measures of central tendency, variation, and correlation to understand data
  • Use hypothesis tests to establish the significance of effects
  • Use statistical visualisations to understand data distributions
  • Describe the role of Exploratory Data Analysis in a Data Science project

Preprocessing Data for Analysis

  • Appropriately process duplicated data, missing values & outliers
  • Understand the importance of scaling, encoding, and feature selection
  • Describe the importance of training, testing & validation sets
  • Engineer novel features to analyse

Supervised Learning: Regression

  • Describe regression in the context of machine learning
  • Build simple and multiple linear regression models
  • Understand non-linear regression approaches
  • Evaluate & compare regression models

Supervised Learning: Classification

  • Describe classification in the context of machine learning
  • Build simple and multiple logistic regression models for classification
  • Build Decision Tree & Random forest models for Classification
  • Evaluate and compare classification models

Model Selection & Evaluation

  • Understand how to choose the best model for regression and classification problems
  • Consider tests & baselines that can be used to evaluate model performance & behaviour
  • Evaluate 'how good is good enough'

Unsupervised Learning

  • Describe clustering and dimensionality reduction in the context of machine learning
  • Apply and evaluate KMeans clustering
  • Apply and evaluate dimensionality reduction techniques

Ethics for Data Scientists

  • Be aware of the legislation and standards Data Scientists must adhere to
  • Discuss the importance of legal, ethical, and moral considerations in Data Analytics projects and identify applicable UK legislation for which employees should receive training
  • Discuss ethical considerations for data handling
  • Recognise ethical considerations in examples of machine learning, deep learning, and AI

Deploying Models & Insights

  • Understand how analytical models can be deployed
  • Evaluate how best to deploy a given model
  • Define checks which can be used to prevent model failures
  • Use Python and associated libraries to deploy a machine learning model
  • Describe which metrics can be used to monitor deployed machine learning models

Where to Go Next

  • Understand the role of deep learning in modern Artificial Intelligence
  • Know which qualifications and professional memberships can benefit data scientists


Contact us for more detail about our trainings and for all other enquiries!

Data Science and Machine Learning with Python Training Course in Germany

The Federal Republic of Germany is the second most populous country in Europe and is located in Central Europe. The official language of the country is German. Germany is one of the richest countries in the world. The main exports of the country include motor vehicles and iron and steel products.

Here are some fun facts about Germany:
The fairy tale writer, the Brothers Grimm, came from Germany and wrote many famous stories such as Cinderella, Snow White, and Sleeping Beauty.
Germany is home to the largest theme park in Europe, the Europa-Park.
The famous composer Ludwig van Beethoven was born in Germany.
The Autobahn, the German highway system, is known for having no general speed limit.


Berlin was divided by the Berlin Wall from 1961 to 1989. Known for its street art, Berlin has many colorful murals and graffiti throughout the city. Also, Berlin is home to many famous museums, such as the Pergamon Museum and the Museum Island. Many clubs and bars stay open until the early hours of the morning in this big city.

Another popular city is Munich, which is famous for its Oktoberfest beer festival that attracts millions of visitors every year. Munich is also home to many historic buildings, including Nymphenburg Palace and the Marienplatz town square.

The country's capital and largest city is Berlin, however Frankfurt is considered to be the business and financial center of Germany. It is home to the Frankfurt Stock Exchange, the European Central Bank, and many other financial institutions. Because of its central location within Europe and its status as a major financial hub, Frankfurt is often referred to as the "Mainhattan," a play on the city's name and its association with the Manhattan financial district in New York City.

Frankfurt is also a major transportation hub, with the largest airport in Germany and one of the largest in Europe, Frankfurt Airport. Additionally, it is a popular destination for tourists, with its historic city center, beautiful parks, and vibrant cultural scene.

Some of the top German technology companies like Siemens AG, Bosch, SAP SE, Deutsche Telekom, Daimler AG and Volkswagen has business centers in Frankfurt. The country has a strong tradition of engineering and innovation, and is home to many other world-class technology companies and research institutions.

Tailored to meet the specific needs of Germany, Bilginç IT Academy combines cutting-edge training methodologies with our comprehensive range of Certification Exam preparation courses and accredited corporate training programs. Experience a transformative approach to IT training that will redefine your expectations.
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