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 

Placement test

Basic math, statistics & Python programming skills (incl. Pandas, Matplotlip)

Online course

Part-time: 5 months
Full-time: 8 weeks

4 modules + 1 final project

Mid level

German or English

Certificate of completion

Every 1-2 months

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.

Modules

Modul 0: Preparation

Objective:
Refresh knowledge of Python and mathematical principles.

Description:
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.

Modul 1: Machine Learning Basics

Objective:
Solving supervised and unsupervised machine learning problems
with sklearn.

Description:
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.

Modul 2: Deep Dive Supervised Learning

Objective:
Expanding the data science toolkit.

Description:
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.

Modul 3: Advanced Topics in Data Science

Objective:
Independent application of simple and complex modeling.

Description:
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.

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LEARNING ENVIRONMENT

Train online in your browser in our interactive learning platform.   

StackFuel provides an innovative learning environment to develop your data skills in the most effective way – interactively and with real-world exercises. Learn to code in our Data Lab and develop algorithms and automate things with real industry datasets. Learn more now and benefit from 80% practical content in our courses. 

WHY STACKFUEL 

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

Whether you are an employee, a manager, or looking for a job – we will help you become a data expert with our certified and fundable upskilling and reskilling courses, which are suitable for every specialist department and every career level. We’ll ensure you learn successfully with our dedicated mentoring team to keep an eye on your progress. Our practical tasks and projects will prepare you for dealing with the latest technologies and applications.  

100% FOR YOU

Customize your learning experience.

Trial week without obligation

In our obligation-free trial week, you will get an insight into your desired training. Afterwards you have the choice: Either you decide for the course or choose another one that suits you even better.

Customized course modules

With our program, you can tailor the modules of your course to suit your needs. Whether business intelligence, data analytics, data science or programming: make the most of your time to build up specialized knowledge and develop your skills individually.

Testimonials

What our graduates say. 

The Data Lab really offers added value, you notice instantly how the practical exercises are relevant to your work. The tasks were always clearly described and presented. So I always knew what to do. The course itself was a great experience!

Alexander GroßData Analyst at AIC Portaltechnik

The greatest added value for me is the content’s practical relevance. Thanks to StackFuel, I can quickly implement what I've learned and adapt it for my situation. That's the real learning success behind the online courses.

Lutz SchneiderStrategic IT Buyer at Axel Springer SE

The online StackFuel course content was very hands on. There were a great deal of good examples and projects. I found it very interesting and instructive. My professional life has changed significantly since the course - I am now a data analysis specialist in my department.

Jaroslaw Wojciech SulakSpecialist for data analysis at IAV GmbH

The flexible and user-friendly course completely changed my view of complex data structures. Thanks to the sustainable and well thought-out learning concept, as well as the seamless application of the course content in the coding environment, I can now put the skills I learned into practice in my day-to-day work in test automation and process data more easily and efficiently.

Jenny LindenauTechnical Manager Test Management at Bank Deutsches Kraftfahrzeuggewerbe GmbH

Payment options

Find the right financing for yourself.

Bildungsgutschein

With a “Bildungsgutschein” from the employment agency, 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 work.

Qualification Opportunities Act

If you are employed, you can have your employer finance your continuing education in part or in full through the Qualifications Opportunity Act – regardless of qualification, age and company size.

Students discount

If you are currently enrolled at a university or college in Germany, you can take our courses at a 50% discount.

Payment in installments

Use our installment plan to spread the cost of your continuing education over several months and maintain your financial flexibility.

Bill

Pay securely and easily after your workout by letting us send you a bill.

Our FAQ

The most important questions at a glance.

Why should I choose a career as a data expert?

The demand 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 professionals have already been advertised in Germany. Above all, the demand for data and AI experts continues to grow enormously.

But a decision for a career in data is so much more than just a safe decision for the future! As a data expert, you’ll deal with powerful, socially relevant topics, be a tech professional and be communicative and creative at the same time. The profession is varied, can be combined with most other professions and offers an attractive salary. And most importantly: you can learn the kills you need with us!

Am I qualified to work in this profession after completing the Data Analyst/Data Scientist course and can I change careers?

Yes, after successfully completing the course, you will receive a certificate of completion from us that you can show in your job applications. Data Analysts and Data Scientists are desperately needed in many business sectors. Even without relevant work experience, your chances of finding an entry-level job are good. In addition, there are analysts in almost every industry, who may have different job titles, but the skills you need are the same as those of a data analyst or data scientist.

Do I have to take a break from my job or take time off for the course?

No, the course is designed to be flexible in terms of time and can fit around your job. You can pursue your profession without restrictions and can schedule your learning times as they best suit you. If you suddenly have more time available, you can contact us by email and we’ll activate the learning content for the more intensive version in your account. In this version you can complete the training within just four weeks. If you find that you need more time, you can still complete the content in the regular time.

Can I really arrange my learning times as I choose?

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

(This does not apply to participants in our funded training courses. They have to attend a fixed number of hours per week and are obliged to participate in the live webinars).

Free consultation

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Data Scientist – Focus Python

Data Scientist course description

In the Data Scientist  course you will learn:

Target Audience