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Introduction to Data Science

Preparation for further training as a Data Scientist

Certificate of completion
(Cross) boarding
Full time/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 group

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)

Modules

1
Introduction to the learning environment
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Objectives:

  • Getting to know and converting the most important data types
  • Writing code according to the DRY principle
  • Organize and prepare data in DataFrames

Contents:

  • Welcome round
  • Training procedure and insight into the 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
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Target:

  • Getting to know the basics of linear algebra

Contents:

  • Vectors, matrices and tensors
  • Vector and matrix multiplication
  • Scalar product
  • Metric and standard

3
Linear Algebra Part 2
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Target:

  • Calculation of similarity of products using the basics of linear algebra

Contents:

  • Cosine similarity
  • Euclidean norm
  • Product recommendation based on similarity

4
Statistical Basics Part 1
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Target:

  • Getting to know the most important discrete and continuous
    Probability distributions

Contents:

  • Probabilities
  • Discrete probability distributions
    • Binomial distribution
    • Negative binomial distribution
    • Poisson distribution
  • Continuous probability distributions
    • Uniform distribution
    • Normal distribution
    • Exponential distribution
  • Mean value and standard deviation
  • Make predictions with distributions

5
Statistical Basics Part 2
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Target:

  • Use of Monte Carlo algorithms for simple simulations

Contents:

  • Law of large numbers
  • Central limit theorem
  • Monte Carlo simulation
FAQ
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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 training 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 trainings.
Lutz Schneider
Strategic IT Buyer at Axel Springer SE
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The content of StackFuel's online training was very practical. There were many good examples and projects. I found that very interesting and instructive. Since the training, my everyday 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 training 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 newly learned knowledge in my everyday job in test automation in greater depth and process data more easily and efficiently since then.
Jenny Lindenau
Technical Manager Test Management at Bank Deutsches Kraftfahrzeuggewerbe GmbH
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