Deepsim Press Publishes Practical Data Science with Python Series (March–May 2026)
A structured learning pathway from data exploration to intelligent systems and forecasting
Deepsim Press is pleased to announce the publication of the Practical Data Science with Python Series, released between March 04 and May 31, 2026.
This series is designed as a practical, end-to-end learning pathway for data science and applied machine learning using Python. It emphasizes real-world workflows, scalable data processing, modeling techniques, and forecasting systems, with a focus on implementation rather than purely theoretical treatment.
Across the series, readers move from foundational data exploration to advanced predictive modeling and time-series forecasting, building a complete understanding of modern data science pipelines.
Series Overview
The Practical Data Science with Python Series includes three core volumes:
1. Practical Data Analysis and Visualization with Python: Data Exploration, Visualization, and Scalable Data Processing
Focuses on core techniques for working with data in Python, including data cleaning, exploration, visualization, and scalable processing workflows for real-world datasets.
2. Practical Data Modeling and Machine Learning with Python: From Data Preparation to Model Evaluation and Optimization
Covers the full machine learning pipeline, including feature engineering, model building, evaluation strategies, and optimization methods for practical predictive systems.
3. Advanced Data Modeling and Forecasting with Python: Time Series, Advanced Modeling, and Real-World Systems
Focuses on time-series analysis, forecasting methods, and advanced modeling approaches for dynamic and real-world systems with temporal structure.
Additional Publication
In addition to the series, Deepsim Press also published:
Learn Python by Building Your Own AI Companion: Master Modern Python by Building a Smart AI Companion System
This book provides a project-based learning approach to Python programming by guiding readers through the construction of an AI companion system. It emphasizes hands-on development, system design thinking, and practical integration of modern AI components.
Focus and Purpose
The publications in this period reflect a unified direction in applied learning:
- building end-to-end data science capability
- emphasizing practical Python implementation
- bridging data analysis, machine learning, and forecasting systems
- introducing project-based AI system development
- supporting scalable and real-world data workflows
Together, these works form a coherent foundation for learners and practitioners working in data science, machine learning, and applied AI systems.
Deepsim Press continues its mission of publishing technically rigorous, implementation-focused educational resources across artificial intelligence, data science, modeling, and intelligent systems.