Qualification for the Job Role of a Data Scientist

Data Scientist – Focus Python

Data Scientist course description

With the certified online course to become a Data Scientist with the focus on Python, you will enter the world of data science. You will be able to generate, verify and interpret predictive models based on data in order to communicate the model results efficiently. You will gain advanced skills in the Python programming language and machine learning. Furthermore, ou will finish the program with a final project, so that upon successful completion of the career path, you will be qualified for the job role of a data scientist or another analytical role.

In the Data Scientist  course you will learn:  

  • How to independently retrieve, clean, and filter data
  • How to explore and analyze data using descriptive statistics
  • How to develop and verify complex prediction models
  • Deep dive into your Python programming skills
  • How to build data models to predict business use cases
  • How to develop machine learning algorithms
  • Best practices for effective data visualization
  • Data storytelling methods
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    Target Audience  

    The mid level course is suitable for anyone who uses Python as a programming language, wants to analyze data and create predictions based on this data in order to make data-driven decisions. You should have a basic motivation for statistics, logical thinking and machine learning. This course is also suitable for career changers.

    Prerequisites for participation 

    A good knowledge of programming skills in Python and common modules (Pandas, Matplotlib) is required for the course.
    Intensive course - further training to become a Data Scientist. You can receive your Data Scientist certification at the end of the Data Scientist course after passing the exam.
    Online course
    108 hours (4.5 months)
    4 modules + 1 final project
    Mid level
    German or English
    Certificate of completion
    incl. VAT
    What to Expect

    Course Overview

    Python Skills

    Python is the number one programming language for machine learning and data science and is relatively easy to learn - even for beginners.

    Interactive tasks & final project

    Apply your knowledge in interactive practical assignments and a final project in which you implement your own data science project with real data sets.

    Job qualification

    This course will qualify you directly for role of data scientist as well as other analytical roles in data teams.

    Python Skills

    Python is the number one programming language for machine learning and data science and is relatively easy to learn - even for beginners.

    Interactive tasks & final project

    Apply your knowledge in interactive practical assignments and a final project in which you implement your own data science project with real data sets.

    Job qualification

    This course will qualify you directly for role of data scientist as well as other analytical roles in data teams.


    Refresh knowledge of Python and mathematical principles.

    Participants carry out analysis and data manipulation in Python
    using the pandas and Matplotlib packages.

    Chapter 1 – Data Analytics with Python:
    Participants get to know our interactive programming
    environment – the Data Lab – and brush up on key programming
    and Python basics for data processing with Pandas, data
    visualization with Matplotlib and Seaborn, and database querying
    with SQL Alchemy.

    Chapter 2 – Linear Algebra:
    Participants become familiar with the mathematical background
    of data science algorithms and learn the basic concepts of
    linear algebra. Participants use the NumPy package to perform
    calculations with vectors and matrices.

    Chapter 3 – Probability Distributions:
    Participants learn more about the statistical background of
    data science algorithms. They explore important statistical
    concepts and learn about discrete and continuous distributions.
    Participants also gain an insight into code versioning with Git.
    Solving supervised and unsupervised machine learning problems
    with sklearn.

    Participants create data science workflows with sklearn, evaluate
    their model performance using appropriate metrics, and learn about
    the overfitting problem.

    Chapter 1 – Supervised Learning: Regression:
    Participants learn how to use the Python package sklearn with
    linear regressions. They also learn about the regression model’s
    assumptions and evaluating the predictions generated. Participants
    learn about the bias-variance trade-off, regularization, and various
    metrics of model quality.

    Chapter 2 – Supervised Learning (Classification):
    Participants are introduced to classification algorithms using the
    k-Nearest-Neighbors algorithm and learn to evaluate the algorithm
    and assess classification performance. They optimize the parameters
    of their model and pay attention to dividing the data into training
    and evaluation sets.

    Chapter 3 – Unsupervised Learning (Clustering):
    Participants learn about the k-Means algorithm as an example of
    an unsupervised learning algorithm. They critically examine the
    algorithm’s assumptions and performance metrics. Then they take a
    brief look at an alternative to k-Means clustering.

    Chapter 4 – Unsupervised Learning (Dimensionality Reduction):
    Participants learn how to reduce the dimension of their data using
    Principal Component Analysis (PCA) and use PCA to generate
    uncorrelated features from the original data. In this context, they
    explore the topic of feature engineering in more detail and new
    features are generated from the old ones.

    Chapter 5 – Outlier Detection:
    Participants learn about different approaches to identifying outliers
    and understand how to deal with these unusual data points. They use
    robust measures and models to minimize the impact of outliers.
    Expanding the data science toolkit.

    Participants deepen their knowledge of data classification models. In
    doing so, they also expand their skills in collecting and preparing data.

    Chapter 1 – Data Gathering:
    Participants learn to gather data by mining web pages and PDF
    documents. They structure collected text data using regular
    expressions so that they can use it together with familiar algorithms.

    Chapter 2 – Logistic Regression:
    Participants learn a second classification algorithm: logistic regression.
    They use new performance metrics to evaluate results and learn how
    to prepare non-numeric data for their models.

    Chapter 3 – Decision Trees and Random Forests:
    Participants learn about the decision tree as an easy-to-interpret
    model. They combine multiple models in an ensemble to improve
    the predictions of their model. They also learn methods to deal with
    unbalanced categories.

    Chapter 4 – Support Vector Machines:
    Participants learn about a final classification algorithm – Support
    Vector Machines (SVMs) and examine the behavior of different kernels
    for SVMs. They also learn the typical steps of Natural Language
    Processing (NLP) and work through an NLP scenario using bag-ofwords models.

    Chapter 5 – Neural Networks:
    Participants are introduced to artificial neural networks and learn more
    about deep learning, to create a multilayer artificial neural network
    and apply it to a data set.
    Independent application of simple and complex modeling.

    Participants gain confidence in solving data science problems and
    learn to communicate results competently.

    Chapter 1 – Visualization and Model Interpretation:
    Participants learn important methods for interpreting and
    visualizing machine learning models. By using model-agnostic
    methods for interpretation, they learn to derive and communicate
    insights into how their models work.

    Chapter 2 – Spark:
    Participants learn why working with distributed memory systems
    is relevant. Using the Python package PySpark, they learn how
    to read distributed databases, perform big data analysis, and use
    well-known machine learning algorithms on distributed systems.

    Chapter 3: Exercise Project:
    Participants work on a prediction problem using a larger data set
    and independently apply their data science skills from cleaning
    the data set to interpreting the model. Participants receive
    feedback on their approach to solving the problem in a project
    consultation with StackFuel‘s mentoring team.

    Chapter 4 – Final Project:
    Participants are given another larger dataset to analyze
    independently and solve with less assistance than they received
    for the practice project. Participants receive feedback on their
    solution approach in an individual project consultation with the
    StackFuel mentoring team.

    Start Dates

    Duration: 18 weeks
    Duration: 18 weeks

    Download the­ curriculum now.


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