Training Description
Intensive training aimed at introducing participants to the world of data analysis using Python. Participants will learn to use popular analytical tools such as NumPy, Pandas, Matplotlib, and Scikit-learn, which enable advanced analysis and data visualization. The training combines theory with practice, emphasizing the development of practical skills through workshops and exercises.
Participant Profile
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People working with data who want to start using the wide range of analytical libraries available in Python
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Specialists from sales, marketing, finance, or analytics departments who want to develop data analysis competencies using solutions beyond Excel-type tools
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People with a passion for programming who are already familiar with analyst work, as the training focuses on tools rather than techniques themselves
Agenda
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Installation of basic environment Anaconda
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Jupyter Notebook
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NumPy Arrays, vectors, and matrices
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Functions
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Data analysis
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Reading and writing files
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Linear algebra
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Pandas Data structures
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Basic operations
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Working with data
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Basic statistics
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Charts
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Matplotlib Basic charts
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Advanced functions
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Saving images
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Scikit-learn Basics
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Selected functions
Benefits
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Gaining practical data analysis skills in Python
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Ability to use popular analytical libraries (NumPy, Pandas, Matplotlib, Scikit-learn)
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Ability to create advanced analyses and data visualizations
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Knowledge of integrating various analytical tools in Python
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Ability to prepare data for analysis and modeling
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Practical experience working with real data
Required Participant Preparation
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Basic knowledge of Python
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General knowledge of data analysis
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Experience working with spreadsheets (e.g., Excel) will be an additional advantage
Topics
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Installation and configuration of the working environment (Anaconda, Jupyter Notebook)
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Working with arrays, vectors, and matrices in NumPy
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Data manipulation and analysis using Pandas
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Creating charts and data visualizations with Matplotlib
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Introduction to machine learning with Scikit-learn
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Integration and automation of analytical processes in Python
Read Also
- Modern Technologies in Data Analytics: How to Effectively Use SQL and Python?
- SAS Programming: Data Analysis and Competencies
Develop Your Skills
This article is related to the training Data Analyst Workshop in Python. Check the program and sign up to develop your skills with EITT experts.
Read also
- Modern Technologies in Data Analytics: How to Effectively Use SQL and Python?
- Why We Program in Python
- AI and Machine Learning in Business: First Steps with Python and Key Libraries
Frequently Asked Questions
What level of Python knowledge is needed before attending this workshop?
You should have basic Python familiarity, such as understanding variables, loops, and functions. The workshop focuses on analytical libraries rather than Python fundamentals, so prior experience with basic syntax will help you get the most out of the hands-on exercises.
Will I be able to use these skills immediately at work after the workshop?
Yes, the workshop is designed around practical exercises using real-world data scenarios. After completing it, you will be able to load datasets with pandas, perform statistical analysis, create visualizations with matplotlib, and apply basic machine learning models using scikit-learn.
What software do I need to install before the workshop?
The workshop uses Anaconda and Jupyter Notebook as the primary working environment. Installation and configuration are covered in the first session, so you only need a laptop with enough disk space to install Anaconda (approximately 3 GB).
How does this workshop differ from a full data science course?
This workshop focuses specifically on practical data analysis skills using Python’s core analytical libraries. Unlike a full data science course, it emphasizes hands-on tool proficiency over deep theoretical foundations, making it ideal for professionals who want to quickly add Python-based analytics to their existing skill set.