Qualification for the Job Role of a Data Scientist

Data Scientist – Focus Python

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 this training 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

Target group

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.

Requirements for participation

  • Placement test
  • Basic math, statistics & Python programming skills (incl. Pandas, Matplotlip)
  • Play Video
    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.
    • Type

      Online course

    • Duration

      18 weeks

    • Structure

      4 modules + 1 final project

    • Level

      Mid level

    • Languages

      German or English

    • Completion

      Certificate of completion

    • Price

      incl. VAT

    That awaits you

    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.


    Module 0: Preperation

    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.

    Module 1: Machine Learning Basics

    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.

    Module 2:Deep Dive Supervised Learning

    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.

    Module 3: Advanced Topics in Data Science

    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.

    Download the curriculum now.​

    Learning environment

    Train online in the browser in our interactive learning platform.

    StackFuel offers you an innovative learning environment with which you can develop your data skills in the most effective way – interactively and with real practical tasks. Learn to program in our data lab and develop algorithms and automations with real data sets from the industry. Convince yourself now and benefit from 80% practice in our training courses.
    Why StackFuel

    We are your strategic learning partner - including mentoring and support.

    Whether you are an employee, unemployed or a manager – we will develop you into a data talent with our fundable further training and retraining courses that are suitable for every department and every career level. We ensure your learning success with our dedicated mentoring team and always stay on the ball with you. Our practical tasks and projects make you fit for dealing with the latest technologies and applications.
    100% FOR YOU

    Personalize your learning experience.

    Non-binding trial week
    With our non-binding trial week you get an insight into your desired training. Then you have the choice: either you decide on the training or you look for another one that suits you even better.
    Individual course modules
    With us you can put together the modules of your further training tailored to your needs. Whether business intelligence, data analytics, data science or programming: use your time optimally to build up expert knowledge and develop your skills individually.

    Here's what our graduates say.

    Payment options

    Find your suitable financing.

    With an education voucher, you can have your further education financed 100% by the job center or the employment agency if you are currently registered as unemployed or looking for a job.
    If you are employed, you can have your employer partially or fully finance your further training through the Qualification Opportunities Act – regardless of qualification, age and company size.
    If you are currently enrolled at a university or college in Germany, you can complete our courses at a 50% discount.
    Use our installment plan to spread the cost of your training over several months and maintain your financial flexibility.
    Pay safely and easily after your training by having us issue you an invoice.
    Our FAQ

    The most important questions at a glance.

    The need for data experts is high. Around 4 million data experts will be needed in Europe by 2025. And in 2021 alone, more than 80,000 positions for IT specialists were advertised in Germany. Above all, the demand for data and AI experts continues to increase enormously. But a decision for a data career is so much more than just a secure future decision! As a data expert, you deal with strong, socially relevant topics, and at the same time you are a tech professional and communicative and creative. The job is varied, can be combined with most other jobs and offers an attractive salary. And the most important thing: It can be learned with us without fail!
    Yes, after successfully completing the training, you will receive a certificate of completion from us that you can show when you apply. Data Analysts and Data Scientists are desperately needed in many economic sectors. Even without relevant professional experience, your chances of getting an entry-level job are good. In addition, there are analysts in almost every industry, they have different job titles, but the skills you need are the same as those of a data analyst or data scientist.
    No, the training is flexible in terms of time and designed to be part-time. You can pursue your profession without restrictions and can plan your learning times at the time that suits you best. If you suddenly have more time available, you are welcome to contact us by e-mail and we will activate the learning content for the part-time version in your account. In this variant, you can complete the training in just four weeks. If you notice that you need more time, you can still complete the content in the normal time.

    Yes, our online training courses should offer you the greatest possible flexibility. Basically, we recommend planning six to eight hours a week for studying. When you want to schedule this time is up to you and is not prescribed by us. In our career paths, the Data Analyst and Data Scientist course, we offer you live webinars where you can ask our mentors questions, but you don’t have to attend if it doesn’t fit into your schedule.

    (Participants in our funded training courses are an exception. They have to attend a fixed number of hours per week and are obliged to take part in the live webinars.)

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