This guide provides a comprehensive and practical introduction to Data Science , emphasizing a deep, conceptual understanding of core algorithms for building reliable and interpretable predictive models. The book focuses heavily on Statistical Learning and foundational Machine Learning techniques, particularly the family of tree-based approaches like the Decision Tree and Random Forest It covers the entire data science workflow, including Statistical Learning, Python for Data Science (NumPy, Pandas, Matplotlib) , Exploratory Data Analysis (EDA) , Statistical Experiments and Significance Testing (t-tests, ANOVA) , Regression Techniques (Linear Regression) , and Classification (Logistic Regression, Decision Trees, Random Forests). The author stresses the importance of model transparency and addressing the bias-variance trade-off.
DATA SCIENCE
Estimated delivery dates: Mar 7, 2026 - Mar 12, 2026
₹399.00
Dr. Prasanna M. Hasabnis is currently working as Associate Professor and Head at Department of Information Technology, Mauli Group of Institution's College of Engineering and Technology, Shegaon, Maharashtra. His areas of interest are Artificial Intelligence, Intelligent Systems, Data Science, Machine Learning, Data warehousing and mining, Object Oriented Programming etc. Dr. Prasanna Hasabnis holds Ph.D. degree in Computer Science and Engineering in the faculty of Engineering & Technology of Sant Gadge Baba Amravati University, Amravati in year 2013. He holds Masters degree in Computer Applications from P.G. Department of Computer Science, Sant Gadge Baba Amravati University, Amravati in year 2006. He has more than 15 years of experience in teaching and research. He has conducted various workshops on Data Science, Power BI, Object oriented programming languages. He has published a number of papers in International and National Journals as well as conferences.
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| Weight | 0.5 kg |
|---|---|
| Dimensions | 21.6 × 14 × 3.3 cm |
| Binding Type | Paperback |
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| Publishers |
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