Data Science training using Python programming language

In recent years, the free-to-use, general-purpose, high-level Python programming language has become particularly popular in data science projects due to its simple and highly streamlined structure, widespread use, ease of learning, and a wealth of add-on packages designed for a variety of tasks.

Competencies to be gained:

We have gathered and transferred our nearly 20 years of experience to the Python environment in order to give participants to Python language and data science analysis a beginner's knowledge set and Python toolkit, with which they can solve general business problems quickly and efficiently.

The topics we offer focus on solving data science tasks in practical life with the help of Python.

Training methodology:

• Theory and practice,
• Reassessing the understanding of what is said in the theoretical education by solving quiz problems individually,
• Programming tasks, individual elaboration and joint evaluation or joint processing of practical business problems

We assume that the participants have basic computer skills, so they will have no difficulty in operating a computer and using a word processor, as well as in understanding basic programming concepts (variables, control structures, etc.).

In the second half of the training, we present a quick solution to data science-type activities using Python, so here we assume that participants have basic math, statistical, and business knowledge.

In our intermediate training, we are introduced to the workflow that is essential for successful data science projects. In a detailed discussion of each step of the workflow, students will learn how to learn and prepare data in Python, as well as the artificial intelligence algorithms most commonly used in business and their Python-based application.

Successful graduates will be able to use analytical methodology and a related set of tools that can be used to solve data science problems effectively in everyday business on their own.

Thematics:

• Python Language Elements and Data Science Objects
• Structure of Data Science projects
• Getting to know, analyzing and preparing data
• Artificial Intelligence Algorithms – Regression, Classification, Segmentation

Training methodology:

• Theory and practice,
• Reassessment of understanding by quizzes and individual tasks
• Individual tasks and joint evaluation or joint processing of programming tasks, practical business problems,
• Intermediate and advanced graduates choose an exam task during the training, which is developed in a project team with the support of the instructors and presented to practical data science professionals after the completion of the training.

We assume that participants have successfully completed our foundation course “Introduction to Data Science Using the Python Language” or have a level of Python language and basic math, statistical and business knowledge covering its subject matter.

In the final part of our training series, our students learn how to solve time series problems often encountered in practical life and the high-performance algorithms used in international data science competitions (e.g. GBM, XGBoost).

We also present the Deep Learning methods for extending previously known neural networks, deep neural networks, and their construction through practical examples.

At the end of the course, our students will learn the basic methods of querying and manipulating data from a database using Python, as well as gain insight into the world of Big Data and the process of Big Data analysis using large amounts of data using Apache Spark and PySpark.

Thematics:

• Advanced Analysis Tasks - Time Series Analysis
• High performance artificial intelligence algorithms
• Application of Deep Learning models
• Query and process data using SQL
• An introduction to the world of Big Data analytics using Apache Spark and PySpark

Training methodology:

• Theory and practice,
• Reassessment of understanding by quizzes and individual tasks
• Individual tasks and joint evaluation or joint processing of programming tasks, practical business problems,
• Intermediate and advanced graduates choose an exam task during the training, which is developed in a project team with the support of the instructors and presented to practical data science professionals after the completion of the training.

We assume that participants have successfully completed our “Successful Implementation of Data Science Projects - Intermediate Data Science Training Using Python” or have a level of Python language and mathematical, statistical, data mining, and business knowledge covering its subject matter.