Training in English

Data Scientist with Python

Data pipelines with machine learning algorithms and Python – the online training with certificate of completion

Being able to process and analyze data automatically and in real time and derive insights from it is one of the key requirements of companies. Building the data pipelines for this is the task of data scientists - a professional field that is currently in particularly high demand and offers great opportunities. This certified online training course enables you to set up data mining processes, apply machine learning algorithms, create predictive models and implement them productively in automated workflows. The course uses the Python programming language with its leading machine learning libraries. This online course is designed so that you can learn flexibly and at your own pace. You can expect videos, interactive graphics, texts and many practical exercises with extensive data sets and coding tasks. Experienced data analysts are on hand as mentors to answer your questions.

Your curriculum

Working with real data sets from different industries

1. Basics of data analytics with Python

  • Working with the Data Lab
  • Basics and concepts in Python
  • Presentation of the tools pandas, matplotlib and Seaborn
  • Database queries with SQL Alchemy

2. Linear algebra

  • Mathematical background
  • Basic concepts of linear algebra
  • Calculation with vectors and matrices
  • Use of the Python library numpy

3. Probability distribution

  • Statistics in data science algorithms
  • Discrete and continuous distributions
  • Versioning code in Git

4. Supervised learning (regression)

  • Concepts of supervised learning
  • Using linear regression
  • Using the Python package sklearn
  • Understanding regression models
  • Evaluation of predictions
  • Bias variance trade-off and regularization
  • Measuring the quality of the model

5. Supervised learning (classification)

  • Introduction to classification algorithms
  • The k-Nearest Neighbors algorithm
  • Assessment of classification performance
  • Optimization of the parameters
  • Splitting the data into training and evaluation sets

6. Unsupervised learning (clustering)

  • Concepts of unsupervised learning
  • The k-Means algorithm
  • Evaluation of the performance metrics
  • Alternatives to k-means clustering

7. Unsupervised learning (dimension reduction)

  • Reducing dimensions in the data view
  • Principal Component Analysis (PCA)
  • Creating uncorrelated features from original data
  • Introduction to feature engineering

8. Identification and excluding outliers

  • Methods for detecting outliers
  • Criteria for unusual data points
  • Robust measures and reducing the influence of outliers

9. Collecting and merging data

  • Reading data from web pages and PDF documents
  • Use of regular expressions
  • Structuring text data before processing

10. Logistic regression

  • Concepts of logistic regression
  • Performance metrics for evaluation
  • Using non-numerical data in models

11. Decision trees and random forests

  • The concept of decision trees
  • Combining multiple models into ensembles
  • Methods for improving the quality of predictions

12. Support Vector Machines

  • Use of Support Vector Machines (SVM)
  • Introduction to Natural Language Processing (NLP)
  • Text classification with bag-of-words models

13. Neural networks

  • Basics of artificial neural networks
  • Basics of deep learning
  • Deeper understanding of neural network layers

14. Visualization and model interpretation

  • Derive and visualize functionalities of models
  • Methods for interpretation and visualization
  • Apply model-agnostic methods

15. Using distributed databases

  • Using the Python package PySpark
  • Reading data from distributed databases
  • Basics of big data analysis
  • Using machine learning algorithms in distributed systems

16. Exercise project

  • Work on a comprehensive exercise project independently
  • Solve a prediction problem using a larger data set
  • Preparation for the final project

17. Final project

  • Independent analysis of the data project
  • Presentation of results and 1:1 feedback session with mentoring team
  • Certificate for Data Analyst with Python

How do you learn with this course?

This online course offers you a particularly practice-oriented learning concept with comprehensive self-study units and a team of mentors who are available to you at all times. A new chapter is activated for you every week. With a time budget of around 6 hours per week, you are sure to reach your goal in 17 weeks. This is how you learn in the course:

Assessment test: In an onboarding meeting at the start of the course, you and the mentoring team will determine what knowledge you already have and which parts of the course you should pay particular attention to. This will prepare you optimally for learning in the self-study units.

Data lab: In the course's learning environment, you can expect videos, interactive graphics, text and, above all, lots of practical exercises with comprehensive datasets and coding tasks. You carry these out directly in the browser - without any installation or configuration effort and with direct success control.

Mentoring team: Your learning coaches are available to answer any questions you may have. They are experienced data analysts who will be happy to help you - via chat, audio or video call.

Webinars: Once a week, you have the opportunity to take part in webinars and immerse yourself in selected specialist data analysis topics.

Career coaching: What professional goals are you pursuing with your further training and how can you achieve them? A team of mentors will be on hand to help you achieve your career goals.

Final project: In your own data project, you will work independently through the entire data pipeline and answer typical questions. At the end, you will present your project in a 1-to-1 feedback session with your mentoring team.

Certificate: After the final project, you will receive your official certificate as a Data Scientist with Python.

This online training course is run by our partner StackFuel GmbH. StackFuel is a specialist in the field of further training in data literacy, data science and AI.

Your benefits

In this practice-oriented training course, you will learn how to carry out data analyses with large data sets independently.

You will learn how to use Python competently, how to use the programming language for data analysis and how to create effective visualizations.

You will learn how to connect different data sources, filter and merge data from them.

You will learn comprehensive methods, algorithms and technologies of machine learning and how to use them with Python packages.

You will learn everything you need to know about the use of deep learning and create an artificial neural network with multiple layers

After the training, you will be able to visualize company data in a meaningful way and make it interactively accessible in dynamic dashboards.

The technical entry hurdles are minimized by the use of Jupyter Notebooks, with which you can carry out the programming exercises directly in the browser.

Participants

The online training course to become a Data Scientist with Python is suitable for anyone who wants to learn Python as a programming language and use it to carry out data analyses independently. No special requirements need to be met. The course is also suitable for career changers.

Final exam

In your own data project, you will work independently through the entire data pipeline and answer typical questions. At the end, you will present your project in a 1-to-1 feedback session with your mentoring team.

Further recommendations for „Data Scientist with Python“

Digital learning for individuals
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30676
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18 weeks (6 h …
Online
8 Events
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Booking number: 30676
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€ 5.176,50 incl. VAT
Details
18 weeks (6 h/week)
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