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Introduction to Data Science

Preparation for further training as a Data Scientist

Certificate of completion

(Cross) boarding

part time

German, English

Free of charge with education voucher

Course description

The goal of the introductory course is to provide you with essential prerequisite knowledge and skills that you need for further education in data science. You will learn important mathematical basics, such as the handling of vectors, matrices and probabilities, which are necessary for the understanding of data science methods.

You will implement what you have learned with the Python programming language in connection with application scenarios in the field of data science. The training thus also serves as a test of your skills in dealing with the Python programming language.

In this training you will learn

Python Basics

Linear algebra

Statistical basis

Creation of structured scripts in the Python programming language
Generation of recommender systems using the Python library numpy
Recognition of the most important probability distributions

Target audience

The advanced training is suitable for you and your career aspirations if you have a degree, ideally in the fields of mathematics, computer science, natural sciences, technology, business administration, (business) information technology or have a comparable qualification or previous experience.

Requirements for participation

Solid knowledge of Python basics (use of basic data types and flow control concepts)

Learn more about our training
Introduction to BI and Data Analytics

Modules

1
Introduction to the learning environment

Objectives:
- Getting to know and converting the most important data types
- Writing code according to the DRY principle
- Organizing and preparing data in DataFrames

Content:
- Welcome session
- Course schedule and look at building blocks
- Introduction to the learning environment
- Python basics:

Variables and data types
Functions and methods
Flow control commands
numpy arrays
Pandas DataFrames

2
Linear Algebra Part 1

Objective:
- Getting to know the basics of linear algebra

Content:
- Vectors, matrices and tensors
- Vector and matrix multiplication
- Dot product
- Metrics and norms

3
Linear Algebra Part 2

Objective:
- Calculating similarity of products using the basics of linear algebra

Content:
- Cosine similarity
- Euclidean norm
- Product recommendation based on similarity

4
Statistical Basics Part 1

Objective: Getting to know the most important discrete and continuous probability distributions

Content:

Probabilities
Discrete probability distributions:
- Binomial distribution
- Negative binomial distribution
- Poisson distribution
Continuous probability distributions:
- Uniform distribution
- Normal distribution
- Exponential distribution
Mean and standard deviation
Making predictions with distributions

5
Statistical Basics Part 2

Objective: Using Monte-Carlo algorithms for simple simulations

Content:

Law of large numbers
Central limit theorem
Monte-Carlo simulation

FAQ

Why should I choose a career in data, coding or AI?

The demand for data experts is high. Around 4 million data experts will be needed in Europe by 2025. In Germany alone, 149,000 IT jobs are currently vacant. The demand for data and AI experts in particular continues to grow enormously.

But a decision for a data career is so much more than just a safe decision for the future! As a data expert, you deal with powerful, socially relevant topics, are a tech professional, and are 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, with us it can be learned unerringly!

Am I qualified to work in this profession after further training as a data analyst and data scientist and can I make a lateral move?

Yes, after successful completion of the training, you will receive a certificate of completion from us that you can show in your job applications. Data Analysts and Data Scientists are desperately sought after 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 have different job titles, but the skills you need are the same as those of a data analyst or data scientist.

Can I really organize my learning time freely?

Yes, our online training courses are designed to offer you the greatest possible flexibility. In general, we recommend that you plan six to eight hours per week for learning. 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 live webinars where you can ask our mentors questions, but you don't have to attend if it doesn't fit into your schedule.

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The StackFuel Data Lab offers me real added value. Here you can feel the practical relevance particularly well. The tasks were always clearly described and presented. So I always knew what I had to do. The course itself was a great experience!

Alexander Gross
Data Analyst at AIC Portaltechnik

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

Lutz Schneider
Strategic IT Buyer at Axel Springer SE

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The content of StackFuel's online course was very practical. There were plenty of good examples and projects. I found that very interesting and educational. Since the course, my day-to-day professional life has changed significantly: I am now a data analytics specialist in my department.

Jaroslaw Wojciech Sulak
Specialist for data analysis at IAV GmbH

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The user-friendly and flexible Python programming course has 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 learning content in the development environment, I can now implement the new things I've learned in my day-to-day work in test automation in more detail and process data more easily and efficiently than before.

Jenny Lindenau
Technical Manager Test Management at Bank Deutsches Kraftfahrzeuggewerbe GmbH

Let's start with a consultation.

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